FRUGAL: Provisioning of Fog Services over 5G Slices To Meet QoS with Minimal Cost

Size: px
Start display at page:

Download "FRUGAL: Provisioning of Fog Services over 5G Slices To Meet QoS with Minimal Cost"

Transcription

1 RUGAL: Provisioning of og Services over 5G Slices To Meet QoS with Miniml ost Ashkn Yousefpour, Json P. Jue The University of Texs t Dlls Emil: shkn, jjue}@utdlls.edu Abstrct Recent dvnces in the res of Internet of Things (IoT), cloud computing nd big dt hve been ttributed to the rise of growing number of complex nd useful pplictions. On the other hnd, The fifth genertion (5G) wireless technology is envisioned to provide fster Internet ccess, with lower ltency, nd ubiquitous mobile coverge compred to its predecessors. As IoT becomes more prevlent in our dily life, more dtintensive, dely-sensitive, nd rel-time pplictions re expected to emerge. Ensuring Qulity of Service (QoS) in terms of bndwidth nd low-ltency for these pplictions is essentil in 5G, nd fog computing is seen s one of the primry enblers for stisfying these QoS requirements. og puts compute, storge, nd networking resources closer to the user. In this report, we show the system model of RUGAL, frmework for QoS-wre og-supported 5G Slice Provisioning (QSP). QSP concerns the dynmic deployment or relese of ppliction services over 5G slice on fog nodes, or dimensioning (enlrge or shrink) of fog-supported 5G slice, in order to meet the ltency nd QoS constrints of pplictions while minimizing cost. I. SYSTEM MODEL In this pper we study the QSP problem, which is to dimension (enlrge or shrink) fog-supported 5G slice to comply with the ltency (i.e. QoS) constrints of the ppliction while lso minimizing the cost of the slice resources. The Slice Owner (SO) wishes to run (possibly ltency-sensitive) ppliction(s) over her slice. The SO solves n instnce of the QSP problem to find good solution for utilizing her 5G slice; the SO wishes to comply with the ltency constrints of her ppliction nd wishes to do so with miniml resource cost. A slice cn be shrunk to free up extr resources, which my sve costs, or my be enlrged if needed, to serve the rising demnds, which my reduce the violtion costs. Moreover, within slice, ppliction components (i.e. services) my be dimensioned or plced in different loctions to comply with the ltency constrints nd/or to minimize the cost. A 5G slice is define s networking, compute, storge, nd memory resources reserved to serve one or more pplictions in the 5G networks. Networking resources re modeled in terms of reserved bndwidth between two physicl nodes, wheres compute, storge, nd memory re modeled in terms of reserved resources within physicl node. A slice cn be comprised of severl pplictions tht spn cross fog, cloud, nd edge networks. An IoT ppliction my be composed of severl services tht re essentilly the components of the ppliction nd cn run in different loctions. Such services re normlly implemented s continers, virtul mchines (VMs), or unikernels. or instnce, n ppliction my hve services such s uthentiction, firewll, cching, nd encryption. Some ppliction services re dely-sensitive nd hve tight dely thresholds, nd my need to run closer to the users/dt sources (e.g. cching), for instnce t the edge of the network or on fog computing devices. On the other hnd, some ppliction services re dely-tolernt nd my hve high vilbility requirements, nd my be deployed frther from the users/dt sources long the fog-to-cloud continuum or in the cloud (e.g. uthentiction). We lbel these fog-cpble IoT services tht could run on the fog computing devices or the cloud servers s fog services. To formulte the QSP problem, we introduce some nottion. Let set of (telecom) edge nodes be denoted by, set of cloud servers by, nd set of fog services running over the SO s slice by A. Some services comprise the components of complex IoT ppliction tht will eventully be run over 5G slice. Some edge nodes hve fog computing cpbilities (e.g. compute, storge, memory), while some edge nodes solely ct s the ccess points in the edge network nd re the entry points of the incoming IoT trffic to the telecom network. Let f(j) = 1, if edge node j hve fog computing cpbility (hence clled fog node), nd f(j) = 0, otherwise. Let the desired QoS level for service be denoted by q (0, 1), nd dely threshold for service by th. Let p denote the penlty tht the SO experiences if ltency requirements of service re violted by 1% per unit time, nd let V % be the percentge of IoT service dely smples of service tht do not meet the dely requirement. or instnce if q = 97%, ny violtion percentge V % greter thn 3% must be compensted for by the SO. The underlying communiction network, set of edge nodes nd cloud servers re modeled s grph G = (V, E), such tht the node set V includes the edge nodes nd cloud servers (V = ), nd the grph edge set E includes the logicl links between the nodes in V. Ech grph edge e(src, dst) E is ssocited with two numbers: r e, the trnsmission rte of logicl link e (megbits per second); nd d e, the propgtion dely of logicl link e (milliseconds). or the ske of correct indexing, if e(src, dst) is the logicl link between n edge node nd cloud server, we renme r e nd d e to r e nd d e, respectively. These prmeters re mintined

2 by the Physicl Infrstructure Provider (PIP) nd re shred with the SO. The min decision vribles of the QSP problem re the resource lloction vribles, defined in the tble I. A. Optimiztion Problem The QSP problem cn be formulted s the following minimiztion of ost(t) over ll time intervls (strting from t = 0 to t = T end ): P1 : min ost(t), 0 t T end ost(t) = [ proc (t) + proc (t)]+ (1) (t) + stor (t)] + [ mem (t) + mem (t)]+ [ stor [ net (t) + net (t)] + viol (t), Subject to QoS constrints. The cost components re defined below. proc (t) = X k P k P T t, (2) k proc (t) = Xj P j P T t, (3) j stor (t) = (X k S k S + λ in k k R )T t, (4) k stor (t) = (Xj S j S + λ in jj R )T t, (5) j mem (t) = X k M k M T t, (6) k mem (t) = j X M j M j T t, (7) net (t) = j net (t) = j viol (t) = j Y jh (j) u (j,h (j))t t, (8) Y jj u (j,j )T t, (9) j [V % (1 q )] + λ in jp T t. (10) The nottion [.] + in the definition of viol is defined s [x] + = mx(x, 0). k R nd j R re the unit cost of request in cloud server k nd fog node j, respectively (e.g. per 1000 requests). proc nd proc re cost of processing in cloud nd fog, re cost of storge in cloud nd fog, nd mem stor nd stor nd mem re cost of memory in cloud nd fog, respectively. The cost of processing is defined similr to the pricing of compute instnces in Amzon E2 [1] nd the cost of storge is defined similr to the pricing of storge instnces in Amzon S3, which is the storge cost plus the request cost (GET, POST, etc.) [2]. comm is the cost of communiction between fog nd cloud, comm is the cost of communiction between fog nodes, nd viol is the cost (penlty) of dely violtions. A service deployed on fog node my be relesed when the demnd for the service is smll. Therefore, we ssume the services re stteless, tht is they do not store ny stte informtion on fog nodes [3], [4], nd we do not consider costs for stte migrtions. We consider discrete-time system model where time is divided into time periods clled reconfigurtion periods. T is the time intervl between two instnces of solving the optimiztion problem. B. onstrints The constrints of the optimiztion problem re introduced here. 1) Mximum Allowed Slice pcity: The PIP exerts mximum llowed slice cpcity (MAS) for given slice. In other words, the SO cnnot llocte to her slice more resources thn the mximum llowed slice cpcity imposed by the PIP. Let the MAS for compute, storge, nd memory t fog node j be Kj P, KS j, KM j, nd t cloud server k be K P k, K S p, K k M, respectively. Also, let the MAS for networking (bndwidth) between fog node j nd fog node j be Kjj N nd between fog node j nd cloud server k be K jk N. irst, we express the MAS constrints for the nodes in the SO s slice: Xj P < Kj P, j ; X k P < K k P, k ; (11) Xj S < Kj S, j ; X k S < K k S, k ; (12) Xj M < Kj M, j ; X k M < K k M, k. (13) Note tht in certin scenrios the MAS for cloud servers my be very lrge vlue, nd thus we cn relx those constrints in such scenrios. Similr to the MAS constrints for the nodes, MAS constrints for the links in the SO s slice re Y jj K N jj, (j, j ) E, (14) Y jk K N jk, (j, k) E. (15) 2) Trffic Offloding: We im to define the QoS-wre og-supported 5G Slice Provisioning (QSP) problem generl enough nd gnostic to the underlying trffic offloding scheme such tht different offloding frmeworks cn be used. The simplest offloding scheme of fog nodes is no-offlod. In the no-offlod frmework, when request rrives to the fog node, the fog node will process it if its queue is not full, or drops the request if the queue is full. It cn be seen tht no-offlod is not good trffic offloding policy, since requests my be blocked or overloded fog nodes my hve to process more requests which will result in high ltency. Another simple offloding policy is cloud-only-offlod, where offloding hppens only from fog node to the cloud. Another offloding policy is single-neighbor-multi-offlod, when fog node cn offlod trffic to one of its neighbors (e.g. the one with the smllest witing time, s in our prior work [5]) nd offloding cn hppen multiple times mong the fog nodes until it reches the mx-offlod-limit nd is hence offloded to the cloud. One cn envisge mny such offloding policies. In this subsection we select one such offloding policy to show how n rbitrry offloding scheme cn fit in our

3 x P j 0 x S j 0 x M j 0 k 0 x S k 0 TABLE I VARIABLES O QSP PROBLEM Service Vribles omputing resources llocted to service over the SO s slice on fog node j (in MIPS) Storge llocted to service over the SO s slice on fog node j (in megbits) Memory llocted to service over the SO s slice on fog node j (in megbits) omputing resources llocted to service over the SO s slice on cloud server k (in MIPS) Storge llocted to service over the SO s slice on cloud server k (in megbits) x k M 0 Memory llocted to service over the SO s slice on cloud server k (in megbits) Trffic Vribles rction of trffic for service offloded from fog 0 t jj 1 node j to fog node j 0 t jk 1 rction of trffic for service offloded from fog node j to cloud server k Slice Node Vribles Xj P 0 omputing resources llocted to the SO s slice on fog node j (in MIPS) Xj S 0 Storge llocted to the SO s slice on fog node j (in megbits) Xj M Memory llocted to the SO s slice on fog node j (in 0 megbits) X k P 0 omputing resources llocted the SO s slice on cloud server k (in MIPS) X k S 0 Storge llocted to the SO s slice on cloud server k (in megbits) X M k 0 Y jj 0 Y jk 0 Memory llocted to the SO s slice on cloud server k (in megbits) Slice Link Vribles Networking resources (bndwidth) llocted to the (logicl) link between edge node j nd edge node j in the SO s slice (in megbits per second) Networking resources (bndwidth) llocted to the (logicl) link between edge node j nd cloud server k in the SO s slice (in megbits per second) frmework. We choose nd describe the multi-neighbor-singleofflod, where the trffic from fog node cn be offloded to ll of the fog node s fog neighbors nd lso to the cloud. In multi-neighbor-single-offlod n offloded trffic to fog node cnnot be offloded gin nd must be processed by the receiving node. Let I j denote the incoming IoT trffic for service over the SO s slice t fog node j, nd let λ j j denote the offloded trffic for service from fog node j to fog node j over the SO s slice. Then the totl incoming trffic of fog node j for service over the SO s slice, v j, will be v j = I j + λ j j. (16) j Note tht in multi-neighbor-single-offlod n offloded trffic to fog node cnnot be offloded gin; nonetheless, not ll of I j is ccepted by fog node j for processing. Let us define t jj s the frction of trffic for service tht is offloded from fog node j to fog node j (when j = j, t jj indictes the frction of ccepted trffic by fog node j). Similrly, t jk is the frction of trffic for service tht is offloded from fog node j to cloud server k. Obviously, these frctions should dd up to one for given service t given fog node j, or t jh + (j) t jj = 1, A, j. (17) j The ccepted incoming trffic to fog node j for service over the SO s slice is λ in j = t jj I j + λ j j. (18) j The frction of I j tht is not ccepted by the fog node will be offloded either to nother fog node, or to cloud server. The disptched trffic from fog node j to cloud server k = h (j) for service over the SO s slice is denoted by λ out j nd is derived by λ out j = t jh (j) I j. (19) The incoming trffic to cloud server k for service over the SO s slice is λ in k = I k + λ out j, (20) j H 1 (k) where H 1 (k) set of indices of ll fog nodes tht route the trffic for service to cloud server k, nd I k is the incoming IoT trffic for service over the SO s slice t cloud server k. Similr to Eq. (18) nd Eq. (19), the offloded trffic from fog node j to fog node j for service over the SO s slice is λ jj = t jj I j. (21) 3) og omputing pbility: If edge node j does not hve fog computing cpbilities, it should not ccept ny trffic for processing nd it should offlod ll of the incoming trffic either to fog node or cloud server; tht is, if f(j) = 0, then t jj = 0. Similrly, if edge node j does not hve fog computing cpbilities, other edge nodes j should not offlod ny trffic to this node; tht is, if f(j) = 0, then t j j = 0. These two constrints cn be linerly described by t j j f(j), A, j, j. (22) 4) Service Dely: IoT service dely is defined s the time intervl between the moment when n IoT node sends service request nd when it receives the response for tht request. (The closed-form eqution of IoT service dely is explined in our prior study [6]). To obtin the service dely, we need to hve the verge propgtion dely nd verge trnsmission rte between IoT nodes nd their corresponding fog nodes. These vlues must be known by the SO, nd in some cses cn be pproximted by round-trip dely mesurement techniques. However, since obtining these vlues re not lwys fesible, we chnge the scope of the definition of the IoT service dely to consider the dely only within fog to cloud domins. IoT service dely cptures the dely from the moment n IoT node sends request until it receives the response for tht request. This cn be chnged to cpture the dely

4 from the moment the request reches fog node. This new service dely, d j, cn be relized s the verge dely budget for service t fog node j within fog-cloud. The verge dely budget for the multi-neighbor-single-offlod scheme for service t fog node j is equl to d j = w j t jj + [2d (j,j ) + lrq + l rp + w j ] t jj + [2d (j,k) + lrq j Y jk Y jj + w k] t jk; k = h (j).(23) Similrly, the verge dely budget for the single-neighborsingle-offlod scheme for service t fog node j is equl to d j = w j t jj + [2d (j,j ) + lrq + [2d (j,k) + lrq Y jk Y jj + w j ] t jj + w k] t jk; k = h (j).(24) where j is the best neighbor of fog node j. The vribles t jj, t jj, t jk, Y jj, nd Y jk re clculted by the QSP problem. In order to evlute the dely budget, we need to hve the verge size of requests nd reply of service (l rq nd l rp ) nd the propgtion dely between fog nodes nd from fog nodes to cloud servers (d (j,j ) nd d (j,k)), which re known or mesured by the SO. We lso need the verge witing times (queueing time plus processing time), w j, w j, nd w k, which cn be obtined either from the corresponding M/M/c queueing models of the fog nodes nd the cloud servers (discussed in Section I-B8), or bsed on predictive performnce modeling nd blck-box monitoring techniques [7]. In either cse, the incoming trffic to fog nodes is required to obtin the verge witing times. The incoming trffic to the fog nodes (I j ) cn either be directly monitored by the SO (e.g. using the monitoring gent of n SDN controller [6]) or cn be predicted by the SO using lerning pproch (to be discussed in Section II). In the first pproch where I j is monitored (t the beginning of ech configurtion intervl), the QSP problem is solved in rective nture; wheres the second pproch tht predicts I j is proctive, since it provisions the resources hed of time bsed on the estimted incoming IoT trffic. Moreover, the predicting pproch hs the dvntge of getting n verge of I j during configurtion intervl, s pposed to the monitoring pproch tht only obtins n instnce of I j during configurtion intervl. In the Section II we discuss how employ lerning methods to predict I j nd obtin the witing times of the fog nodes nd cloud servers. 5) SLA Violtion: To mesure the qulity of given service, we need to see wht percentge of IoT requests do not meet the dely threshold th (SLA violtions). We first need to check if verge dely budget of fog node j for service is greter thn the threshold th defined in SLA for service. Let us define binry vrible v j to indicte this: 1, if d j > th v j =, j, A. (25) 0, otherwise We define nother vrible tht mesures the SLA violtion (SLAV) of given service ccording to the defined QoS prmeters in the SLA. We denote by V % the percentge of IoT service dely smples of service tht do not meet the dely requirement. V % cn be clculted s follows V % = j λin j v j, A. (26) j λin j Note tht V % is mesured s weighted verge of v j, with λ in j s the weight. 6) 5G Slice Resource pcity: The mount of the resources llocted to the services nd trffic within the SO s slice should not exceed the cpcity of the slice; this pplies to ll the nodes nd the links of the slice. We first look t this constrints with respect to the nodes in the slice, i.e. compute, storge, nd memory resources within node of the slice: x P j < Xj P, j ; x S j < Xj S, j ; x M j < Xj M, j ; k < X P k, k ; (27) x S k < X S k, k ; (28) x M k < X M k, k. (29) Similrly, we lso hve constrints for cpcity of the links in slice: the mount of trffic routed over prticulr slice link should not exceed the mount of llocted bndwidth to tht slice link, which is expressed by λ j j(l rq ) Y jj, (j, j ) E, (30) λ out j (l rq + l rp ) Y jk, (j, k) E. (31) 7) Arrivl of Requests: Let Λ j denote the rrivl rte of instructions (in MIPS) to fog node j for service over the SO s slice. This is the rrivl rte of instructions of the incoming requests tht re ccepted for processing by fog node j tht is given by Λ j = L P λ in j. Similrly, the rrivl rte of instructions (in MIPS) to the cloud server k for service over slice the SO s slice, Λ k, cn be written s Λ k = LP λ in k, where λ in k is the incoming trffic rte to cloud server k for service over the SO s slice. L P is the required mount of processing for service per request. The mount of computing resources llocted to the fog nodes nd cloud servers for given service on the SO s slice must be bigger thn the incoming rrivl of processing requests for tht service (i.e. stbility constrints): x P j Λ j, j, A, (32) k Λ k, k, A. (33)

5 8) Witing Times: To get the witing times, We dopt commonly used M/M/c queueing system [8], [9], [10] model for fog node with n j processing units, ech with service rte µ j nd totl rrivl rte of Λ j (totl processing cpcity of fog node j will be Kj P = n j µ j ). To model wht frction of processing units ech service cn obtin, we ssume tht the processing units of fog node re llocted to the deployed services proportionl to their processing needs (L P ). or instnce, if the requests for service 1 need twice the mount of processing thn tht of the requests for service 2 (L P 1 = 2 L P 2 ), service 1 should receive twice the service rte compred to service 2. orrespondingly, we define f j, the frction of service rte tht service obtins t fog node j s: f j = x j L P x jl P. (34) Ech deployed service cn be seen s n M/M/c queueing system with service rte of f j Kj P = f j n j µ j, nd rrivl rte of Λ j (both in MIPS). Thus, the witing time for requests of service t fog node j will be w j = 1 f j µ j + f j K P j P Q j Λ, (35) j where P Q j is the probbility tht n rriving request to fog node j for service hs to wit in the queue. P.. Q is lso referred to s Erlng s formul nd is equl to such tht ρ j = P 0 j = [ nj 1 c=0 P Q j = (n jρ j ) nj n j! Λj f jk P j nd (n j ρ j ) c c! + (n jρ j ) nj n j! P 0 j 1 ρ j, (36) 1 1 ρ j ] 1. (37) Note tht the requests for different services hve different processing times. Nevertheless, s discussed before, in the definition of Λ j we ccount for the different processing times by the inclusion of L P. Similrly, cloud server k with n k processing units (i.e. servers), ech with service rte µ k nd totl rrivl rte of Λ k cn be seen s n M/M/c queueing system (totl processing cpcity of cloud server k will be K k P = n k µ k ). Therefore, similr to Eq. (35), w k, the witing time for requests of service t cloud server k, could be derived s w k = 1 f k µ k + P Q k f k K P k Λ k, A, k. (38) An eqution similr to Eq. (36) is defined for P Q k, the probbility of queueing t cloud server k. Note tht for simplicity, insted of modeling ech cloud server M/M/c queue, one my lso model the whole cloud s n M/M/ queueing system. 9) Dependency of ompute, Storge, nd Memory: The vribles for compute, storge, nd memory lloction re interrelted to ech other; when there is no compute resources llocted to node for prticulr service, there is no need to llocte corresponding storge nd memory resources to the node for tht service. These dependencies for fog nodes cn be formulted using the following constrints: x S 0, if x P j j = = 0 L S, j, A, (39), otherwise x M 0, if x P j j = = 0 L M, j, A, (40), otherwise nd for cloud servers using the following constrints: x S 0, if k k = = 0 L S, otherwise x M k =, k, A, (41) 0, if k = 0 L M,, otherwise k, A. (42) L S nd L M re the required minimum mount of storge nd memory for service, respectively. It is worth mentioning tht since cloud is usully regrded s hving mple resources, the constrints (41) nd (42) could be esily modified to llocte more storge nd memory resources for cloud servers when k > 0. II. LEARNING METHOD OR INOMING IOT TRAI A. Predicting Incoming IoT Trffic To predict the incoming trffic to Iot nodes, we employ the follow the regulrized leder (TRL) online lerning method [11]. Our method for trffic prediction is minly influenced by the demnd prediction method discussed in [12]. To ccount for the mount of incoming trffic in different configurtion intervls, we dd time vrible t to the vrible I j, mke it I j (t), the predicted incoming IoT trffic for service over the SO s slice t fog node j in the configurtion intervl t. Let Ij (t) be the ctul (e.g. mesured) incoming IoT trffic for service over the SO s slice t fog node j in time t. Let us define Ij mx = mx t [0,T end ](I j (t)), which is known to or cn be mesured by the SO bsed on historicl observtions. Hence, the predicted incoming trffic I j (t) will be in [0, Ij mx ]. We cn then divide the time into configurtion intervls so tht we hve countble number of I j (t) s. We define convex loss function to minimize the prediction error for the incoming IoT trffic I j (t) s E t (I j ) = I j (t) I j(t), j, A, t. (43) Therefore, loss minimiztion problem over ll time slots then cn be expressed s min E t (I j ). (44) 0 t T end The solution to this minimiztion problem is set of vlues for I j (t) [0, I mx j ] for every t [0, T end ].

6 TRL chooses the predicted incoming trffic vlue I j (t+1) tht minimizes the cumultive loss function over the previous t time slots plus regulrizer (to void overfitting), tht is I j (t + 1) = rg min I j [0,Ij mx][ s=0 t E s (I j ) + R(I j )]. (45) where I j reflects the possible predicted incoming IoT trffic rte for service to fog node j in intervl t + 1. R(.) is convex regulrizer. Since solving this optimiztion problem t ech reconfigurtion intervl is not fesible, we convert it to the following problem I j (t + 1) = rg min I j [0,I mx j ] h t(i j ), (46) where h t (.) is the surrogte loss function tht should ccurtely estimtes the originl loss function nd is efficient to evlute. We use the surrogte function chosen in [12] s h t (I j ) = [ t ( E s (I j )] I j + r t (I j ). (47) s=0 E s (I j ) is the grdient of E s (I j ) nd r t (.) is regulriztion function (similr to R(.)). The regulriztion function is chosen s r t (x) = 0.5 t s=0 ( 1 α s 1 α s 1 )(x x(s)) 2, (48) where α s = Θ( 1 s ) is e lerning rte in time slot s [12]. Specificlly, we will hve β α t = t, (49) s=0 ( E s(i j )) 2 nd since ( E s (I j )) 2 = 1, then α t = β t+1, where β is hyper prmeter bsed on the fetures nd dt (set to β = Ij mx ) [12]. After this conversion, eq. (46) becomes smooth minimiztion problem with no constrints. We then simply tke the derivtive of our chosen surrogte function h t (.) with respect to I j nd set it to be equl to 0 to obtin the I j (t) tht minimizes eq. (46). Tking derivtive is esy nd its solution will be the predicted incoming IoT trffic. B. Obtining Witing Times Now hving the predicted incoming IoT trffic, one cn lso predicts the witing times. As discussed before, the online lerning pproch tht predicts I j is proctive in nture, since bsed on predicted witing times (nd hence violtion rte) the SO provisions the resources hed of time bsed. Bsiclly, the predicted incoming IoT trffic cn be simply plugged in the equtions for witing times to get the predicted witing times. Note: We leve the investigtion of other choices for surrogte functions nd regulriztion functions s our future work. More noteworthy, one cn lso study the pplicbility of reinforcement lerning techniques for incoming IoT trffic prediction. III. TESTBED SETUP or obtining numericl evlutions nd show the vlidity nd performnce of our proposed frmework, we implement RUGAL in the following testbed. We hve 4 SDN-enbled HPE switches in our lbortory, which we use to mke tree topology: 1 switch s the root nd 3 switches s the lef nodes. We directly connect one computer to ech lef switch to ct s fog node. hence, we hve 3 fog nodes in our testbed. We lso connect Wii ccess points to the switches to enble the pssing trffic from the Wii-enbled IoT devices. The RUGAL runs s n pp (e.g. on the sme computer tht hs the root SDN controller) tht bsiclly communictes with the SDN controllers through Openlow protocol, to monitor the IoT trffic, deploy or relese fog service, or dimension the compute, storge or networking resources in the testbed. The RUGAL solves the QSP problem nd determine wht resources need to be deployed, relese, or dimensioned. The experiment will consist of 2 mjor components: (1) run few IoT pplictions (e.g. object recognition from cmer imges) whose trffic pss through these switches in our lb to some service tht is hosted in the cloud nd (2) nlyzing the performnce of RUGAL with regrds to service dely, dely violtions, nd resource usge. The switches, ccess points, nd computers in our lb ct s the edge of the network, where fog nodes locte nd operte. or the cloud, we cn use ny cloud service provider such s AWS, Google, or reserch experiment cloud computing environments such s hmeleon loud or loudlb. The cloud/fog services could be implemented s continers or VMs. or the gile service provisioning in the fog network, we cn use Docker continers tht cn be esily provisioned using kubernetes or OpenStck. kybernetes nd OpenStck gives us the flexibility nd gility for resource mngement (deploying, relesing or dimensioning of compute, storge, nd networking resources). To generte IoT trffic, we hve cn do the following: (1) use the IoT bords we lredy hve in our lb (e.g. Rspberry Pi nd Arduino tht hve cmer nd other sensors) to generte ctul trffic, (2) use the Iperf tool to generte constnt bit rte trffic flows, (3) combintion of both pproches: use the IoT bords to generte ctul trffic, nd if needed more, use the Iperf tool to generte more trffic (e.g. for other services or bckground trffic). REERENES [1] Amzon E2 pricing, [Avilble] [2] Amzon S3 pricing, [Avilble] [3] S. H. Mortzvi, M. Slehe,. S. Gomes,. Phillips, nd E. de Lr, loudpth: multi-tier cloud computing frmework, in Proceedings of the Second AM/IEEE Symposium on Edge omputing, p. 20, AM, [4] R. S. Montero, E. Rojs, A. A. rrillo, nd I. M. Llorente, Extending the cloud to the network edge., IEEE omputer, vol. 50, no. 4, pp , [5] A. Yousefpour, G. Ishigki, R. Gour, nd J. P. Jue, On reducing IoT service dely vi fog offloding, IEEE Internet of Things Journl, vol. 5, no. 2, pp , 2018.

7 [6] A. Yousefpour, A. Ptil, G. Ishigki, I. Kim, X. Wng, H.. nky, Q. Zhng, W. Xie, nd J. P. Jue, QoS-wre dynmic fog service provisioning, rxiv preprint rxiv: , [7]. Witt, M. Bux, W. Gusew, nd U. Leser, Predictive performnce modeling for distributed computing using blck-box monitoring nd mchine lerning, rxiv preprint rxiv: , [8] M. Ji, J. o, nd W. Ling, Optiml cloudlet plcement nd user to cloudlet lloction in wireless metropolitn re networks, IEEE Trnsctions on loud omputing, [9] Z. Zhou, J. eng, L. Tn, Y. He, nd J. Gong, An ir-ground integrtion pproch for mobile edge computing in iot, IEEE ommunictions Mgzine, vol. 56, no. 8, pp , [10] L. Liu, X. Guo, Z. hng, nd T. Ristniemi, Joint optimiztion of energy nd dely for computtion offloding in cloudlet-ssisted mobile cloud computing, Wireless Networks, pp. 1 14, July [11] B. McMhn, ollow-the-regulrized-leder nd mirror descent: Equivlence theorems nd l1 regulriztion, in Proceedings of the ourteenth Interntionl onference on Artificil Intelligence nd Sttistics, pp , [12] X. ei,. Liu, H. Xu, nd H. Jin, Adptive vnf scling nd flow routing with proctive demnd prediction, in IEEE INOOM IEEE onference on omputer ommunictions, pp , IEEE, 2018.

MAXIMUM FLOWS IN FUZZY NETWORKS WITH FUNNEL-SHAPED NODES

MAXIMUM FLOWS IN FUZZY NETWORKS WITH FUNNEL-SHAPED NODES MAXIMUM FLOWS IN FUZZY NETWORKS WITH FUNNEL-SHAPED NODES Romn V. Tyshchuk Informtion Systems Deprtment, AMI corportion, Donetsk, Ukrine E-mil: rt_science@hotmil.com 1 INTRODUCTION During the considertion

More information

The Discussion of this exercise covers the following points:

The Discussion of this exercise covers the following points: Exercise 4 Bttery Chrging Methods EXERCISE OBJECTIVE When you hve completed this exercise, you will be fmilir with the different chrging methods nd chrge-control techniques commonly used when chrging Ni-MI

More information

Exercise 1-1. The Sine Wave EXERCISE OBJECTIVE DISCUSSION OUTLINE. Relationship between a rotating phasor and a sine wave DISCUSSION

Exercise 1-1. The Sine Wave EXERCISE OBJECTIVE DISCUSSION OUTLINE. Relationship between a rotating phasor and a sine wave DISCUSSION Exercise 1-1 The Sine Wve EXERCISE OBJECTIVE When you hve completed this exercise, you will be fmilir with the notion of sine wve nd how it cn be expressed s phsor rotting round the center of circle. You

More information

Multi-beam antennas in a broadband wireless access system

Multi-beam antennas in a broadband wireless access system Multi-em ntenns in rodnd wireless ccess system Ulrik Engström, Mrtin Johnsson, nders Derneryd nd jörn Johnnisson ntenn Reserch Center Ericsson Reserch Ericsson SE-4 84 Mölndl Sweden E-mil: ulrik.engstrom@ericsson.com,

More information

METHOD OF LOCATION USING SIGNALS OF UNKNOWN ORIGIN. Inventor: Brian L. Baskin

METHOD OF LOCATION USING SIGNALS OF UNKNOWN ORIGIN. Inventor: Brian L. Baskin METHOD OF LOCATION USING SIGNALS OF UNKNOWN ORIGIN Inventor: Brin L. Bskin 1 ABSTRACT The present invention encompsses method of loction comprising: using plurlity of signl trnsceivers to receive one or

More information

Engineer-to-Engineer Note

Engineer-to-Engineer Note Engineer-to-Engineer Note EE-297 Technicl notes on using Anlog Devices DSPs, processors nd development tools Visit our Web resources http://www.nlog.com/ee-notes nd http://www.nlog.com/processors or e-mil

More information

Energy Harvesting Two-Way Channels With Decoding and Processing Costs

Energy Harvesting Two-Way Channels With Decoding and Processing Costs IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING, VOL., NO., MARCH 07 3 Energy Hrvesting Two-Wy Chnnels With Decoding nd Processing Costs Ahmed Arf, Student Member, IEEE, Abdulrhmn Bknin, Student

More information

Network Sharing and its Energy Benefits: a Study of European Mobile Network Operators

Network Sharing and its Energy Benefits: a Study of European Mobile Network Operators Network Shring nd its Energy Benefits: Study of Europen Mobile Network Opertors Mrco Ajmone Mrsn Electronics nd Telecommunictions Dept Politecnico di Torino, nd Institute IMDEA Networks, mrco.jmone@polito.it

More information

(CATALYST GROUP) B"sic Electric"l Engineering

(CATALYST GROUP) Bsic Electricl Engineering (CATALYST GROUP) B"sic Electric"l Engineering 1. Kirchhoff s current l"w st"tes th"t (") net current flow "t the junction is positive (b) Hebr"ic sum of the currents meeting "t the junction is zero (c)

More information

Y9.ET1.3 Implementation of Secure Energy Management against Cyber/physical Attacks for FREEDM System

Y9.ET1.3 Implementation of Secure Energy Management against Cyber/physical Attacks for FREEDM System Y9.ET1.3 Implementtion of Secure Energy ngement ginst Cyber/physicl Attcks for FREED System Project Leder: Fculty: Students: Dr. Bruce cillin Dr. o-yuen Chow Jie Dun 1. Project Gols Develop resilient cyber-physicl

More information

University of North Carolina-Charlotte Department of Electrical and Computer Engineering ECGR 4143/5195 Electrical Machinery Fall 2009

University of North Carolina-Charlotte Department of Electrical and Computer Engineering ECGR 4143/5195 Electrical Machinery Fall 2009 Problem 1: Using DC Mchine University o North Crolin-Chrlotte Deprtment o Electricl nd Computer Engineering ECGR 4143/5195 Electricl Mchinery Fll 2009 Problem Set 4 Due: Thursdy October 8 Suggested Reding:

More information

CHAPTER 2 LITERATURE STUDY

CHAPTER 2 LITERATURE STUDY CHAPTER LITERATURE STUDY. Introduction Multipliction involves two bsic opertions: the genertion of the prtil products nd their ccumultion. Therefore, there re two possible wys to speed up the multipliction:

More information

Module 9. DC Machines. Version 2 EE IIT, Kharagpur

Module 9. DC Machines. Version 2 EE IIT, Kharagpur Module 9 DC Mchines Version EE IIT, Khrgpur esson 40 osses, Efficiency nd Testing of D.C. Mchines Version EE IIT, Khrgpur Contents 40 osses, efficiency nd testing of D.C. mchines (esson-40) 4 40.1 Gols

More information

Domination and Independence on Square Chessboard

Domination and Independence on Square Chessboard Engineering nd Technology Journl Vol. 5, Prt, No. 1, 017 A.A. Omrn Deprtment of Mthemtics, College of Eduction for Pure Science, University of bylon, bylon, Irq pure.hmed.omrn@uobby lon.edu.iq Domintion

More information

Fuzzy Logic Controller for Three Phase PWM AC-DC Converter

Fuzzy Logic Controller for Three Phase PWM AC-DC Converter Journl of Electrotechnology, Electricl Engineering nd Mngement (2017) Vol. 1, Number 1 Clusius Scientific Press, Cnd Fuzzy Logic Controller for Three Phse PWM AC-DC Converter Min Muhmmd Kml1,, Husn Ali2,b

More information

Synchronous Machine Parameter Measurement

Synchronous Machine Parameter Measurement Synchronous Mchine Prmeter Mesurement 1 Synchronous Mchine Prmeter Mesurement Introduction Wound field synchronous mchines re mostly used for power genertion but lso re well suited for motor pplictions

More information

Synchronous Machine Parameter Measurement

Synchronous Machine Parameter Measurement Synchronous Mchine Prmeter Mesurement 1 Synchronous Mchine Prmeter Mesurement Introduction Wound field synchronous mchines re mostly used for power genertion but lso re well suited for motor pplictions

More information

ABB STOTZ-KONTAKT. ABB i-bus EIB Current Module SM/S Intelligent Installation Systems. User Manual SM/S In = 16 A AC Un = 230 V AC

ABB STOTZ-KONTAKT. ABB i-bus EIB Current Module SM/S Intelligent Installation Systems. User Manual SM/S In = 16 A AC Un = 230 V AC User Mnul ntelligent nstlltion Systems A B 1 2 3 4 5 6 7 8 30 ma 30 ma n = AC Un = 230 V AC 30 ma 9 10 11 12 C ABB STOTZ-KONTAKT Appliction Softwre Current Vlue Threshold/1 Contents Pge 1 Device Chrcteristics...

More information

Interference Cancellation Method without Feedback Amount for Three Users Interference Channel

Interference Cancellation Method without Feedback Amount for Three Users Interference Channel Open Access Librry Journl 07, Volume, e57 ISSN Online: -97 ISSN Print: -9705 Interference Cncelltion Method without Feedbc Amount for Three Users Interference Chnnel Xini Tin, otin Zhng, Wenie Ji School

More information

Experiment 3: Non-Ideal Operational Amplifiers

Experiment 3: Non-Ideal Operational Amplifiers Experiment 3: Non-Idel Opertionl Amplifiers Fll 2009 Equivlent Circuits The bsic ssumptions for n idel opertionl mplifier re n infinite differentil gin ( d ), n infinite input resistnce (R i ), zero output

More information

Compared to generators DC MOTORS. Back e.m.f. Back e.m.f. Example. Example. The construction of a d.c. motor is the same as a d.c. generator.

Compared to generators DC MOTORS. Back e.m.f. Back e.m.f. Example. Example. The construction of a d.c. motor is the same as a d.c. generator. Compred to genertors DC MOTORS Prepred by Engr. JP Timol Reference: Electricl nd Electronic Principles nd Technology The construction of d.c. motor is the sme s d.c. genertor. the generted e.m.f. is less

More information

Redundancy Data Elimination Scheme Based on Stitching Technique in Image Senor Networks

Redundancy Data Elimination Scheme Based on Stitching Technique in Image Senor Networks Sensors & Trnsducers 204 by IFSA Publishing, S. L. http://www.sensorsportl.com Redundncy Dt Elimintion Scheme Bsed on Stitching Technique in Imge Senor Networks hunling Tng hongqing Technology nd Business

More information

High-speed Simulation of the GPRS Link Layer

High-speed Simulation of the GPRS Link Layer 989 High-speed Simultion of the GPRS Link Lyer J Gozlvez nd J Dunlop Deprtment of Electronic nd Electricl Engineering, University of Strthclyde 204 George St, Glsgow G-lXW, Scotlnd Tel: +44 4 548 206,

More information

Experiment 3: Non-Ideal Operational Amplifiers

Experiment 3: Non-Ideal Operational Amplifiers Experiment 3: Non-Idel Opertionl Amplifiers 9/11/06 Equivlent Circuits The bsic ssumptions for n idel opertionl mplifier re n infinite differentil gin ( d ), n infinite input resistnce (R i ), zero output

More information

Lecture 20. Intro to line integrals. Dan Nichols MATH 233, Spring 2018 University of Massachusetts.

Lecture 20. Intro to line integrals. Dan Nichols MATH 233, Spring 2018 University of Massachusetts. Lecture 2 Intro to line integrls Dn Nichols nichols@mth.umss.edu MATH 233, Spring 218 University of Msschusetts April 12, 218 (2) onservtive vector fields We wnt to determine if F P (x, y), Q(x, y) is

More information

Understanding Basic Analog Ideal Op Amps

Understanding Basic Analog Ideal Op Amps Appliction Report SLAA068A - April 2000 Understnding Bsic Anlog Idel Op Amps Ron Mncini Mixed Signl Products ABSTRACT This ppliction report develops the equtions for the idel opertionl mplifier (op mp).

More information

EET 438a Automatic Control Systems Technology Laboratory 5 Control of a Separately Excited DC Machine

EET 438a Automatic Control Systems Technology Laboratory 5 Control of a Separately Excited DC Machine EE 438 Automtic Control Systems echnology bortory 5 Control of Seprtely Excited DC Mchine Objective: Apply proportionl controller to n electromechnicl system nd observe the effects tht feedbck control

More information

CS 135: Computer Architecture I. Boolean Algebra. Basic Logic Gates

CS 135: Computer Architecture I. Boolean Algebra. Basic Logic Gates Bsic Logic Gtes : Computer Architecture I Boolen Algebr Instructor: Prof. Bhgi Nrhri Dept. of Computer Science Course URL: www.ses.gwu.edu/~bhgiweb/cs35/ Digitl Logic Circuits We sw how we cn build the

More information

Example. Check that the Jacobian of the transformation to spherical coordinates is

Example. Check that the Jacobian of the transformation to spherical coordinates is lss, given on Feb 3, 2, for Mth 3, Winter 2 Recll tht the fctor which ppers in chnge of vrible formul when integrting is the Jcobin, which is the determinnt of mtrix of first order prtil derivtives. Exmple.

More information

Algorithms for Memory Hierarchies Lecture 14

Algorithms for Memory Hierarchies Lecture 14 Algorithms for emory Hierrchies Lecture 4 Lecturer: Nodri Sitchinv Scribe: ichel Hmnn Prllelism nd Cche Obliviousness The combintion of prllelism nd cche obliviousness is n ongoing topic of reserch, in

More information

Adaptive Network Coding for Wireless Access Networks

Adaptive Network Coding for Wireless Access Networks Adptive Network Coding for Wireless Access Networks Tun Trn School of Electricl Engineering nd Computer Science Oregon Stte University, Corvllis, Oregon 9733 Emil: trntu@eecs.orst.edu Thinh Nguyen School

More information

Kirchhoff s Rules. Kirchhoff s Laws. Kirchhoff s Rules. Kirchhoff s Laws. Practice. Understanding SPH4UW. Kirchhoff s Voltage Rule (KVR):

Kirchhoff s Rules. Kirchhoff s Laws. Kirchhoff s Rules. Kirchhoff s Laws. Practice. Understanding SPH4UW. Kirchhoff s Voltage Rule (KVR): SPH4UW Kirchhoff s ules Kirchhoff s oltge ule (K): Sum of voltge drops round loop is zero. Kirchhoff s Lws Kirchhoff s Current ule (KC): Current going in equls current coming out. Kirchhoff s ules etween

More information

A Slot-Asynchronous MAC Protocol Design for Blind Rendezvous in Cognitive Radio Networks

A Slot-Asynchronous MAC Protocol Design for Blind Rendezvous in Cognitive Radio Networks Globecom 04 - Wireless Networking Symposium A Slot-Asynchronous MAC Protocol Design for Blind Rendezvous in Cognitive Rdio Networks Xingy Liu nd Jing Xie Deprtment of Electricl nd Computer Engineering

More information

Solutions to exercise 1 in ETS052 Computer Communication

Solutions to exercise 1 in ETS052 Computer Communication Solutions to exercise in TS52 Computer Communiction 23 Septemer, 23 If it occupies millisecond = 3 seconds, then second is occupied y 3 = 3 its = kps. kps If it occupies 2 microseconds = 2 6 seconds, then

More information

Postprint. This is the accepted version of a paper presented at IEEE PES General Meeting.

Postprint.   This is the accepted version of a paper presented at IEEE PES General Meeting. http://www.div-portl.org Postprint This is the ccepted version of pper presented t IEEE PES Generl Meeting. Cittion for the originl published pper: Mhmood, F., Hooshyr, H., Vnfretti, L. (217) Sensitivity

More information

BP-P2P: Belief Propagation-Based Trust and Reputation Management for P2P Networks

BP-P2P: Belief Propagation-Based Trust and Reputation Management for P2P Networks BP-PP: Belief Propgtion-Bsed Trust nd Reputtion Mngement for PP Networs Ermn Aydy School of Electricl nd Comp. Eng. Georgi Institute of Technology Atlnt, GA 333, USA Emil: eydy@gtech.edu Frmrz Feri School

More information

CSI-SF: Estimating Wireless Channel State Using CSI Sampling & Fusion

CSI-SF: Estimating Wireless Channel State Using CSI Sampling & Fusion CSI-SF: Estimting Wireless Chnnel Stte Using CSI Smpling & Fusion Riccrdo Crepldi, Jeongkeun Lee, Rul Etkin, Sung-Ju Lee, Robin Krvets University of Illinois t Urbn-Chmpign Hewlett-Pckrd Lbortories Emil:{rcrepl,rhk}@illinoisedu,

More information

Joanna Towler, Roading Engineer, Professional Services, NZTA National Office Dave Bates, Operations Manager, NZTA National Office

Joanna Towler, Roading Engineer, Professional Services, NZTA National Office Dave Bates, Operations Manager, NZTA National Office . TECHNICA MEMOANDM To Cc repred By Endorsed By NZTA Network Mngement Consultnts nd Contrctors NZTA egionl Opertions Mngers nd Are Mngers Dve Btes, Opertions Mnger, NZTA Ntionl Office Jonn Towler, oding

More information

Temporal Secondary Access Opportunities for WLAN in Radar Bands

Temporal Secondary Access Opportunities for WLAN in Radar Bands The 4th Interntionl Symposium on Wireless Personl Multimedi Communictions WPMC'), Temporl Secondry Access Opportunities for WLAN in Rdr Bnds Miurel Tercero, Ki Won Sung, nd Jens Znder Wireless@KTH, Royl

More information

10.4 AREAS AND LENGTHS IN POLAR COORDINATES

10.4 AREAS AND LENGTHS IN POLAR COORDINATES 65 CHAPTER PARAMETRIC EQUATINS AND PLAR CRDINATES.4 AREAS AND LENGTHS IN PLAR CRDINATES In this section we develop the formul for the re of region whose oundry is given y polr eqution. We need to use the

More information

Topic 20: Huffman Coding

Topic 20: Huffman Coding Topic 0: Huffmn Coding The uthor should gze t Noh, nd... lern, s they did in the Ark, to crowd gret del of mtter into very smll compss. Sydney Smith, dinburgh Review Agend ncoding Compression Huffmn Coding

More information

Improving synchronized transfers in public transit networks using real-time tactics

Improving synchronized transfers in public transit networks using real-time tactics Improving synchronized trnsfers in public trnsit networks using rel-time tctics Zhongjun Wu 1,2,3, Grhm Currie 3, Wei Wng 1,2 1 Jingsu Key Lbortory of Urbn ITS, Si Pi Lou 2#, Nnjing, 210096, Chin 2 School

More information

A Simple Approach to Control the Time-constant of Microwave Integrators

A Simple Approach to Control the Time-constant of Microwave Integrators 5 VOL., NO.3, MA, A Simple Approch to Control the Time-constnt of Microwve Integrtors Dhrmendr K. Updhyy* nd Rkesh K. Singh NSIT, Division of Electronics & Communiction Engineering New Delhi-78, In Tel:

More information

Study on SLT calibration method of 2-port waveguide DUT

Study on SLT calibration method of 2-port waveguide DUT Interntionl Conference on Advnced Electronic cience nd Technology (AET 206) tudy on LT clibrtion method of 2-port wveguide DUT Wenqing Luo, Anyong Hu, Ki Liu nd Xi Chen chool of Electronics nd Informtion

More information

9.4. ; 65. A family of curves has polar equations. ; 66. The astronomer Giovanni Cassini ( ) studied the family of curves with polar equations

9.4. ; 65. A family of curves has polar equations. ; 66. The astronomer Giovanni Cassini ( ) studied the family of curves with polar equations 54 CHAPTER 9 PARAMETRIC EQUATINS AND PLAR CRDINATES 49. r, 5. r sin 3, 5 54 Find the points on the given curve where the tngent line is horizontl or verticl. 5. r 3 cos 5. r e 53. r cos 54. r sin 55. Show

More information

On the Prediction of EPON Traffic Using Polynomial Fitting in Optical Network Units

On the Prediction of EPON Traffic Using Polynomial Fitting in Optical Network Units On the Prediction of EP Trffic Using Polynomil Fitting in Opticl Networ Units I. Mmounis (1),(3), K. Yinnopoulos (2), G. Ppdimitriou (4), E. Vrvrigos (1),(3) (1) Computer Technology Institute nd Press

More information

Application Note. Differential Amplifier

Application Note. Differential Amplifier Appliction Note AN367 Differentil Amplifier Author: Dve n Ess Associted Project: Yes Associted Prt Fmily: CY8C9x66, CY8C7x43, CY8C4x3A PSoC Designer ersion: 4. SP3 Abstrct For mny sensing pplictions, desirble

More information

BP-P2P: Belief Propagation-Based Trust and Reputation Management for P2P Networks

BP-P2P: Belief Propagation-Based Trust and Reputation Management for P2P Networks 1 9th Annul IEEE Communictions Society Conference on Sensor, Mesh nd Ad Hoc Communictions nd Networs (SECON) BP-PP: Belief Propgtion-Bsed Trust nd Reputtion Mngement for PP Networs Ermn Aydy School of

More information

This is a repository copy of Effect of power state on absorption cross section of personal computer components.

This is a repository copy of Effect of power state on absorption cross section of personal computer components. This is repository copy of Effect of power stte on bsorption cross section of personl computer components. White Rose Reserch Online URL for this pper: http://eprints.whiterose.c.uk/10547/ Version: Accepted

More information

Dataflow Language Model. DataFlow Models. Applications of Dataflow. Dataflow Languages. Kahn process networks. A Kahn Process (1)

Dataflow Language Model. DataFlow Models. Applications of Dataflow. Dataflow Languages. Kahn process networks. A Kahn Process (1) The slides contin revisited mterils from: Peter Mrwedel, TU Dortmund Lothr Thiele, ETH Zurich Frnk Vhid, University of liforni, Riverside Dtflow Lnguge Model Drsticlly different wy of looking t computtion:

More information

A Stochastic Geometry Approach to the Modeling of DSRC for Vehicular Safety Communication

A Stochastic Geometry Approach to the Modeling of DSRC for Vehicular Safety Communication A Stochstic Geometry Approch to the Modeling of DSRC for Vehiculr Sfety Communiction Zhen Tong, Student Member, IEEE, Hongsheng Lu 2, Mrtin Henggi, Fellow, IEEE, nd Christin Poellbuer 2, Senior Member,

More information

Simulation of Transformer Based Z-Source Inverter to Obtain High Voltage Boost Ability

Simulation of Transformer Based Z-Source Inverter to Obtain High Voltage Boost Ability Interntionl Journl of cience, Engineering nd Technology Reserch (IJETR), olume 4, Issue 1, October 15 imultion of Trnsformer Bsed Z-ource Inverter to Obtin High oltge Boost Ability A.hnmugpriy 1, M.Ishwry

More information

Section 17.2: Line Integrals. 1 Objectives. 2 Assignments. 3 Maple Commands. 1. Compute line integrals in IR 2 and IR Read Section 17.

Section 17.2: Line Integrals. 1 Objectives. 2 Assignments. 3 Maple Commands. 1. Compute line integrals in IR 2 and IR Read Section 17. Section 7.: Line Integrls Objectives. ompute line integrls in IR nd IR 3. Assignments. Red Section 7.. Problems:,5,9,,3,7,,4 3. hllenge: 6,3,37 4. Red Section 7.3 3 Mple ommnds Mple cn ctully evlute line

More information

Adaptive VoIP Smoothing of Pareto Traffic Based on Optimal E-Model Quality

Adaptive VoIP Smoothing of Pareto Traffic Based on Optimal E-Model Quality Adptive VoIP Smoothing of Preto Trffic Bsed on Optiml E-Model Qulity Shyh-Fng Hung 1, Eric Hsio-Kung Wu 2, nd Po-Chi Chng 3 1 Deprtment of Electricl Engineering, Ntionl Centrl University, Tiwn hsf@vplb.ee.ncu.edu.tw

More information

Distance dependent Call Blocking Probability, and Area Erlang Efficiency of Cellular Networks

Distance dependent Call Blocking Probability, and Area Erlang Efficiency of Cellular Networks Distnce dependent Cll Blocking Probbility, nd Are Erlng Efficiency of Cellulr Networks Subhendu Btbyl 1, Suvr Sekhr Ds 1,2 1 G.S.Snyl School of Telecommuniction, Indin Institute of Technology Khrgpur,

More information

MULTILEVEL INVERTER TOPOLOGIES USING FLIPFLOPS

MULTILEVEL INVERTER TOPOLOGIES USING FLIPFLOPS MULTILVL INVRTR TOPOLOGIS USING FLIPFLOPS C.R.BALAMURUGAN S.SIVASANKARI Aruni ngineering College, Tiruvnnmli. Indi crblin010@gmil.com, sivyokesh1890@gmil.com S.P.NATARAJAN Annmli University, Chidmbrm,

More information

INSTITUTE OF AERONAUTICAL ENGINEERING (Autonomous) Dundigal, Hyderabad

INSTITUTE OF AERONAUTICAL ENGINEERING (Autonomous) Dundigal, Hyderabad Hll Ticket No Question Pper Code: AEC009 INSTITUTE OF AERONAUTICAL ENGINEERING (Autonomous) Dundigl, Hyderd - 500 043 MODEL QUESTION PAPER Four Yer B.Tech V Semester End Exmintions, Novemer - 2018 Regultions:

More information

Demand response for aggregated residential consumers with energy storage sharing

Demand response for aggregated residential consumers with energy storage sharing 21 IEEE 4th Annul Conference on Decision nd Control (CDC December 1-18, 21. Os, Jpn Demnd response for ggregted residentil consumers with energy storge shring Kveh Pridri, Alessndr Prisio, Henri Sndberg

More information

A Cluster-based TDMA System for Inter-Vehicle Communications *

A Cluster-based TDMA System for Inter-Vehicle Communications * JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 30, 213-231 (2014) A Cluster-bsed TDMA System for Inter-Vehicle Communictions * Deprtment of Electricl Engineering Ntionl Sun Yt-Sen University Kohsiung,

More information

Network-coded Cooperation for Multi-unicast with Non-Ideal Source-Relay Channels

Network-coded Cooperation for Multi-unicast with Non-Ideal Source-Relay Channels This full text pper ws peer reviewed t the direction of IEEE Communictions Society suject mtter experts for puliction in the IEEE ICC 2010 proceedings Network-coded Coopertion for Multi-unicst with Non-Idel

More information

4110 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 66, NO. 5, MAY 2017

4110 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 66, NO. 5, MAY 2017 40 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 66, NO. 5, MAY 07 Trnsmit Power Control for DD-Underlid Cellulr Networs Bsed on Sttisticl Fetures Peng Sun, Kng G. Shin, Life Fellow, IEEE, Hilin Zhng,

More information

Area-Time Efficient Digit-Serial-Serial Two s Complement Multiplier

Area-Time Efficient Digit-Serial-Serial Two s Complement Multiplier Are-Time Efficient Digit-Seril-Seril Two s Complement Multiplier Essm Elsyed nd Htem M. El-Boghddi Computer Engineering Deprtment, Ciro University, Egypt Astrct - Multipliction is n importnt primitive

More information

A Novel Back EMF Zero Crossing Detection of Brushless DC Motor Based on PWM

A Novel Back EMF Zero Crossing Detection of Brushless DC Motor Based on PWM A ovel Bck EMF Zero Crossing Detection of Brushless DC Motor Bsed on PWM Zhu Bo-peng Wei Hi-feng School of Electricl nd Informtion, Jingsu niversity of Science nd Technology, Zhenjing 1003 Chin) Abstrct:

More information

DESIGN OF CONTINUOUS LAG COMPENSATORS

DESIGN OF CONTINUOUS LAG COMPENSATORS DESIGN OF CONTINUOUS LAG COMPENSATORS J. Pulusová, L. Körösi, M. Dúbrvská Institute of Robotics nd Cybernetics, Slovk University of Technology, Fculty of Electricl Engineering nd Informtion Technology

More information

Chapter 2 Literature Review

Chapter 2 Literature Review Chpter 2 Literture Review 2.1 ADDER TOPOLOGIES Mny different dder rchitectures hve een proposed for inry ddition since 1950 s to improve vrious spects of speed, re nd power. Ripple Crry Adder hve the simplest

More information

Math Circles Finite Automata Question Sheet 3 (Solutions)

Math Circles Finite Automata Question Sheet 3 (Solutions) Mth Circles Finite Automt Question Sheet 3 (Solutions) Nickols Rollick nrollick@uwterloo.c Novemer 2, 28 Note: These solutions my give you the nswers to ll the prolems, ut they usully won t tell you how

More information

Three-Phase Synchronous Machines The synchronous machine can be used to operate as: 1. Synchronous motors 2. Synchronous generators (Alternator)

Three-Phase Synchronous Machines The synchronous machine can be used to operate as: 1. Synchronous motors 2. Synchronous generators (Alternator) Three-Phse Synchronous Mchines The synchronous mchine cn be used to operte s: 1. Synchronous motors 2. Synchronous genertors (Alterntor) Synchronous genertor is lso referred to s lterntor since it genertes

More information

Foot-Pedal: Haptic Feedback Human Interface Bridging Sensational Gap between Remote Places

Foot-Pedal: Haptic Feedback Human Interface Bridging Sensational Gap between Remote Places Foot-Pedl: Hptic Feedbck Humn Interfce Bridging Senstionl Gp between Remote Plces Mincheol Kim 1, De-Keun Yoon 2, Shin-Young Kim 1, Ji-Hi Cho 1, Kwng-Kyu Lee 1, Bum-Je You 1,3 1 Center of Humn-centered

More information

A New Stochastic Inner Product Core Design for Digital FIR Filters

A New Stochastic Inner Product Core Design for Digital FIR Filters MATEC Web of Conferences, (7) DOI:./ mtecconf/7 CSCC 7 A New Stochstic Inner Product Core Design for Digitl FIR Filters Ming Ming Wong,, M. L. Dennis Wong, Cishen Zhng, nd Ismt Hijzin Fculty of Engineering,

More information

Simultaneous Adversarial Multi-Robot Learning

Simultaneous Adversarial Multi-Robot Learning Simultneous Adversril Multi-Robot Lerning Michel Bowling nd Mnuel Veloso Computer Science Deprtment Crnegie Mellon University Pittsburgh PA, 15213-3891 Abstrct Multi-robot lerning fces ll of the chllenges

More information

Synchronous Generator Line Synchronization

Synchronous Generator Line Synchronization Synchronous Genertor Line Synchroniztion 1 Synchronous Genertor Line Synchroniztion Introduction One issue in power genertion is synchronous genertor strting. Typiclly, synchronous genertor is connected

More information

& Y Connected resistors, Light emitting diode.

& Y Connected resistors, Light emitting diode. & Y Connected resistors, Light emitting diode. Experiment # 02 Ojectives: To get some hndson experience with the physicl instruments. To investigte the equivlent resistors, nd Y connected resistors, nd

More information

DYE SOLUBILITY IN SUPERCRITICAL CARBON DIOXIDE FLUID

DYE SOLUBILITY IN SUPERCRITICAL CARBON DIOXIDE FLUID THERMAL SCIENCE, Yer 2015, Vol. 19, No. 4, pp. 1311-1315 1311 DYE SOLUBILITY IN SUPERCRITICAL CARBON DIOXIDE FLUID by Jun YAN, Li-Jiu ZHENG *, Bing DU, Yong-Fng QIAN, nd Fng YE Lioning Provincil Key Lbortory

More information

Design and Modeling of Substrate Integrated Waveguide based Antenna to Study the Effect of Different Dielectric Materials

Design and Modeling of Substrate Integrated Waveguide based Antenna to Study the Effect of Different Dielectric Materials Design nd Modeling of Substrte Integrted Wveguide bsed Antenn to Study the Effect of Different Dielectric Mterils Jgmeet Kour 1, Gurpdm Singh 1, Sndeep Ary 2 1Deprtment of Electronics nd Communiction Engineering,

More information

Eliminating Non-Determinism During Test of High-Speed Source Synchronous Differential Buses

Eliminating Non-Determinism During Test of High-Speed Source Synchronous Differential Buses Eliminting Non-Determinism During of High-Speed Source Synchronous Differentil Buses Abstrct The t-speed functionl testing of deep sub-micron devices equipped with high-speed I/O ports nd the synchronous

More information

High Speed On-Chip Interconnects: Trade offs in Passive Termination

High Speed On-Chip Interconnects: Trade offs in Passive Termination High Speed On-Chip Interconnects: Trde offs in Pssive Termintion Rj Prihr University of Rochester, NY, USA prihr@ece.rochester.edu Abstrct In this pper, severl pssive termintion schemes for high speed

More information

Performance Comparison between Network Coding in Space and Routing in Space

Performance Comparison between Network Coding in Space and Routing in Space Performnce omprison etween Network oding in Spce nd Routing in Spce Yunqing Ye, Xin Hung, Ting Wen, Jiqing Hung nd lfred Uwitonze eprtment of lectronics nd Informtion ngineering, Huzhong University of

More information

Spiral Tilings with C-curves

Spiral Tilings with C-curves Spirl Tilings with -curves Using ombintorics to Augment Trdition hris K. Plmer 19 North Albny Avenue hicgo, Illinois, 0 chris@shdowfolds.com www.shdowfolds.com Abstrct Spirl tilings used by rtisns through

More information

Mixed CMOS PTL Adders

Mixed CMOS PTL Adders Anis do XXVI Congresso d SBC WCOMPA l I Workshop de Computção e Aplicções 14 20 de julho de 2006 Cmpo Grnde, MS Mixed CMOS PTL Adders Déor Mott, Reginldo d N. Tvres Engenhri em Sistems Digitis Universidde

More information

The Math Learning Center PO Box 12929, Salem, Oregon Math Learning Center

The Math Learning Center PO Box 12929, Salem, Oregon Math Learning Center Resource Overview Quntile Mesure: Skill or Concept: 300Q Model the concept of ddition for sums to 10. (QT N 36) Model the concept of sutrction using numers less thn or equl to 10. (QT N 37) Write ddition

More information

Exponential-Hyperbolic Model for Actual Operating Conditions of Three Phase Arc Furnaces

Exponential-Hyperbolic Model for Actual Operating Conditions of Three Phase Arc Furnaces Americn Journl of Applied Sciences 6 (8): 1539-1547, 2009 ISSN 1546-9239 2009 Science Publictions Exponentil-Hyperbolic Model for Actul Operting Conditions of Three Phse Arc Furnces 1 Mhdi Bnejd, 2 Rhmt-Allh

More information

ALTERNATIVE WAYS TO ENHANCE PERFORMANCE OF BTB HVDC SYSTEMS DURING POWER DISTURBANCES. Pretty Mary Tom 1, Anu Punnen 2.

ALTERNATIVE WAYS TO ENHANCE PERFORMANCE OF BTB HVDC SYSTEMS DURING POWER DISTURBANCES. Pretty Mary Tom 1, Anu Punnen 2. ALTERNATIVE WAYS TO ENHANCE PERFORMANCE OF BTB HVDC SYSTEMS DURING POWER DISTURBANCES Pretty Mry Tom, Anu Punnen Dept.of Electricl n Electronics Engg. Sint Gits College of Engineering,Pthmuttm,Kerl,Ini

More information

Convolutional Networks. Lecture slides for Chapter 9 of Deep Learning Ian Goodfellow

Convolutional Networks. Lecture slides for Chapter 9 of Deep Learning Ian Goodfellow Convolutionl Networks Lecture slides for Chpter 9 of Deep Lerning In Goodfellow 2016-09-12 Convolutionl Networks Scle up neurl networks to process very lrge imges / video sequences Sprse connections Prmeter

More information

Section 2.2 PWM converter driven DC motor drives

Section 2.2 PWM converter driven DC motor drives Section 2.2 PWM converter driven DC motor drives 2.2.1 Introduction Controlled power supply for electric drives re obtined mostly by converting the mins AC supply. Power electronic converter circuits employing

More information

RSS based Localization of Sensor Nodes by Learning Movement Model

RSS based Localization of Sensor Nodes by Learning Movement Model RSS bsed Locliztion of Sensor Nodes by Lerning Movement Model 1 R.ARTHI, 2 P.DEVARAJ, 1 K.MURUGAN 1 Rmnujn Computing Centre, Ann University, Guindy, Chenni, Indi 2 Deprtment of Mthemtics, College of Engineering,

More information

Development of an Energy Estimation Algorithm for LTE Mobile Access Networks

Development of an Energy Estimation Algorithm for LTE Mobile Access Networks Development of n Energy Estimtion Algorithm for LTE Mobile Access Networks 1 E. Obi, 2 S. Grb nd 2 S. M. Sni Deprtment of Electricl nd Computer Engineering, Ahmdu Bello University, Zri. Abstrct - This

More information

Robustness Analysis of Pulse Width Modulation Control of Motor Speed

Robustness Analysis of Pulse Width Modulation Control of Motor Speed Proceedings of the World Congress on Engineering nd Computer Science 2007 WCECS 2007, October 24-26, 2007, Sn Frncisco, USA obustness Anlysis of Pulse Width Modultion Control of Motor Speed Wei Zhn Abstrct

More information

Distributed two-hop proportional fair resource allocation in Long Term Evolution Advanced networks

Distributed two-hop proportional fair resource allocation in Long Term Evolution Advanced networks WIRELESS COMMUNICATIONS AND MOBILE COMPUTING Wirel. Commun. Mob. Comput. 2016; 16:264 278 Published online 12 August 2014 in Wiley Online Librry (wileyonlinelibrry.com)..2517 RESEARCH ARTICLE Distributed

More information

MEASURE THE CHARACTERISTIC CURVES RELEVANT TO AN NPN TRANSISTOR

MEASURE THE CHARACTERISTIC CURVES RELEVANT TO AN NPN TRANSISTOR Electricity Electronics Bipolr Trnsistors MEASURE THE HARATERISTI URVES RELEVANT TO AN NPN TRANSISTOR Mesure the input chrcteristic, i.e. the bse current IB s function of the bse emitter voltge UBE. Mesure

More information

Geometric quantities for polar curves

Geometric quantities for polar curves Roerto s Notes on Integrl Clculus Chpter 5: Bsic pplictions of integrtion Section 10 Geometric quntities for polr curves Wht you need to know lredy: How to use integrls to compute res nd lengths of regions

More information

MATH 118 PROBLEM SET 6

MATH 118 PROBLEM SET 6 MATH 118 PROBLEM SET 6 WASEEM LUTFI, GABRIEL MATSON, AND AMY PIRCHER Section 1 #16: Show tht if is qudrtic residue modulo m, nd b 1 (mod m, then b is lso qudrtic residue Then rove tht the roduct of the

More information

Math 116 Calculus II

Math 116 Calculus II Mth 6 Clculus II Contents 7 Additionl topics in Integrtion 7. Integrtion by prts..................................... 7.4 Numericl Integrtion.................................... 7 7.5 Improper Integrl......................................

More information

ECE 274 Digital Logic Fall 2009 Digital Design

ECE 274 Digital Logic Fall 2009 Digital Design igitl Logic ll igitl esign MW -:PM, IL Romn Lysecky, rlysecky@ece.rizon.edu http://www.ece.rizon.edu/~ece hpter : Introduction Slides to ccompny the textbook igitl esign, irst dition, by rnk Vhid, John

More information

Address for Correspondence

Address for Correspondence Mrndn et l., Interntionl Journl of Advnced Engineering Technology E-ISSN 0976-3945 Reserch Pper A LATTICE REDUCTION-AIDED INFORMATION PRECODER FOR MULTIUSER COMMUNICATION SYSTEM S. Mrndn, N. Venteswrn

More information

A Comparative Analysis of Algorithms for Determining the Peak Position of a Stripe to Sub-pixel Accuracy

A Comparative Analysis of Algorithms for Determining the Peak Position of a Stripe to Sub-pixel Accuracy A Comprtive Anlysis of Algorithms for Determining the Pek Position of Stripe to Sub-pixel Accurcy D.K.Nidu R.B.Fisher Deprtment of Artificil Intelligence, University of Edinburgh 5 Forrest Hill, Edinburgh

More information

Kyushu Institute of Technology

Kyushu Institute of Technology Title: Integrted Rescue Service Stellite (IRS-St) Primry Point of Contct (POC): Mohmed Ibrhim Co-uthors: Btsuren Amglnbt, Puline Fure, Kevin Chou Orgniztion:, 1-1 Sensui, Tobt, Kitkyushu 804-8550, Jpn

More information

Direct AC Generation from Solar Cell Arrays

Direct AC Generation from Solar Cell Arrays Missouri University of Science nd Technology Scholrs' Mine UMR-MEC Conference 1975 Direct AC Genertion from Solr Cell Arrys Fernndo L. Alvrdo Follow this nd dditionl works t: http://scholrsmine.mst.edu/umr-mec

More information

Section 16.3 Double Integrals over General Regions

Section 16.3 Double Integrals over General Regions Section 6.3 Double Integrls over Generl egions Not ever region is rectngle In the lst two sections we considered the problem of integrting function of two vribles over rectngle. This sitution however is

More information

TIME: 1 hour 30 minutes

TIME: 1 hour 30 minutes UNIVERSITY OF AKRON DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING 4400: 34 INTRODUCTION TO COMMUNICATION SYSTEMS - Spring 07 SAMPLE FINAL EXAM TIME: hour 30 minutes INSTRUCTIONS: () Write your nme

More information

Crime Scene Documentation. Crime Scene Documentation. Taking the C.S. What should my notes include. Note Taking 9/26/2013

Crime Scene Documentation. Crime Scene Documentation. Taking the C.S. What should my notes include. Note Taking 9/26/2013 Crime Scene Documenttion Crime Scene Documenttion Most importnt step in C.S. processing Purpose: permnently record the condition of C.S. & physicl evidence Time consuming Documenter must be orgnized nd

More information