Selective Offloading in Mobile Edge Computing for the Green Internet of Things

Transcription

1 EDGE COMPUTING FOR THE INTERNET OF THINGS Selecive Offloading in Mobile Edge Compuing for he Green Inerne of Things Xinchen Lyu, Hui Tian, Li Jiang, Alexey Vinel, Sabia Maharjan, Sein Gjessing, and Yan Zhang This work was suppored in par by he Naional Naural Science Foundaion of China under Gran and Gran 64206, and in par by he Naional Key Research and Developmen Program of China under Gran 207ZX Digial Objec Idenifier: 0.09/MNET Absrac Mobile edge compuing provides he radio access neworks wih cloud compuing capabiliies o fulfill he requiremens of he Inerne of Things services such as high reliabiliy and low laency. Offloading services o edge servers can alleviae he sorage and compuing limiaions and prolong he lifeimes of he IoT devices. However, offloading in MEC faces scalabiliy problems due o he massive number of IoT devices. In his aricle, we presen a new inegraion archiecure of he cloud, MEC, and IoT, and propose a lighweigh reques and admission framework o resolve he scalabiliy problem. Wihou coordinaion among devices, he proposed framework can be operaed a he IoT devices and compuing servers separaely, by encapsulaing laency requiremens in offloading requess. Then a selecive offloading scheme is designed o imize he energy consumpion of devices, where he signaling overhead can be furher reduced by enabling he devices o be self-noaed or self-denied for offloading. Simulaion resuls show ha our proposed selecive offloading scheme can saisfy he laency requiremens of differen services and reduce he energy consumpion of IoT devices. Inroducion The Inerne of Things (IoT) is proposed o equip everyday objecs wih elecronics, sofware, sensors, and nework conneciviy, and bring he vision of a conneced world ino realiy [, 2]. However, compuaion-inensive services, such as e-healh, auomaic driving, and indusrial auomaion, are fas developing and ougrowing he compuing and sorage capabiliies of IoT devices. Cloud compuing offers enormous sorage, compuing faciliies, and daa sharing opporuniies. By offloading he compuaion and sorage from he IoT devices o he cloud hrough mobile neworks, mobile cloud compuing can alleviae he compuaion and sorage limiaions and prolong he lifeimes of IoT devices [3]. As he compuing unis in he core nework use shared backhaul resources, mobile cloud compuing may no be able o mee he reliabiliy and laency requiremens of IoT services. For insance, emergency IoT services, such as mobile vehicular conneciviy, e-healh, and indusrial auomaion, require ulra low laency and exremely high reliabiliy. In addiion, he services from smar sensors generae high volumes of daa. Uploading he sensed daa o he cloud may wase energy and cause raffic congesion in he core nework. Mobile edge compuing (MEC) is inroduced o provide radio access neworks wih cloud compuing capabiliies [4]. For insance, macro/pico/ femo base saions (BSs) may be conneced o co-locaed edge servers o reduce laency, ease he raffic on backhaul links, and deliver reliable services. Typical characerisics of MEC include proximiy, high energy efficiency, low laency, high hroughpu, mobiliy suppor, and locaion awareness [4]. These feaures align well wih he requiremens of IoT services. Offloading incurs exra energy consumpion and laency due o he communicaion beween devices and servers. Earlier works on ask offloading focus on single-device decision making, where he devices make offloading decisions independenly o imize eiher laency [5] or energy consumpion [6]. Due o he resource bolenecks of edge servers, scalabiliy becomes a key problem in MEC [7 0]; ha is, here is a rade-off beween he scale of offloading and he qualiy of service (QoS). Especially in he era of IoT, he offloaded services from millions of devices will exhaus he compuaional resources a he edge servers, which leads o increased processing laency ha violaes he requiremens of he emergency IoT services. The exising sudies resolve he scalabiliy problem by eiher execuing he load balancing among edge servers o aggregae and susain he workloads [7, 8] or implemening he coordinaion among mobile devices o selec services for offloading [9, 0]. On he oher hand, cross-plaform IoT services have been enabled via ransparen compuing in [], and offloading decisions and resource allocaions have been joinly opimized in [2, 3]. However, he remendous scale of IoT devices necessiaes efficien service discovery and lighweigh resource managemen for heerogeneous IoT services. In his aricle, we presen a novel hree-layer inegraion archiecure including he cloud, MEC, and IoT, and propose a lighweigh reques and admission framework o resolve he scalabiliy problem. Following he proposed framework, a selecive offloading scheme is developed o imize he energy consumpion of he IoT devices and furher reduce he signaling overhead of MEC. Our main conribuions are summarized as follows. Inegraion archiecure of he cloud, MEC, and IoT: The cloud and he geo-disribued edge Xinchen Lyu and Hui Tian (corresponding auhor) are wih Beijing Universiy of Poss and Telecommunicaions; Li Jiang is wih Guangdong Universiy of Technology; Alexey Vinel is wih Halmsad Universiy; Sabia Maharjan and Sein Gjessing are wih Simula Research Laboraory and he Universiy of Oslo; Yan Zhang is wih he Universiy of Oslo /8/$ IEEE IEEE Nework January/February 208

2 servers can be complemened by each oher o fulfill he various requiremens of he IoT services. In he inegraion, by exploiing he locaion awareness of edge servers and low-laency inerconnecs beween hem, IoT devices from a wide range can be grouped ino virual clusers for efficien service discovery, and he edge servers are organized in a hierarchical srucure o susain he peak workloads by aggregaing services across differen iers of servers. Lighweigh reques and admission framework: We encapsulae he laency requiremens deered a each device in heir offloading requess o decouple he dependency of ask pariioning of differen devices. The proposed reques and admission framework resolves he inrinsic scalabiliy problem of MEC, and can be operaed a he devices and edge servers separaely, wihou he need for coordinaion among devices. Selecive offloading scheme: We propose a selecive offloading scheme under he reques and admission framework o imize he energy consumpion of he IoT devices while saisfying he laency requiremens of differen services. The signaling overhead of MEC can be furher reduced by enabling he devices o be self-noaed or self-denied for offloading. The res of his aricle is organized as follows. We presen he hree-layer inegraion archiecure, and propose he reques and admission framework.. We hen illusrae our selecive offloading scheme and evaluae is efficiency hrough numerical resuls. Finally, we conclude he aricle. Cloud servers Cloud sorage Cloud compuing plane Edge compuing plane Access nework User plane Wearable devices Table Smar sensors Smarphone Smar ciy Lapop Indusry Virual cluser Gaeway Edge server Transporaion Proposed Inegraion Archiecure of he Cloud, MEC, and IoT MEC can complemen he cloud o fulfill he various requiremens of IoT services, such as low laency, high reliabiliy, locaion awareness, and bandwidh demand. Offloading can save he energy of local execuion and srech he sorage and compuaional capaciies of IoT devices. However, he inegraion of he cloud, MEC, and IoT remains challenging in erms of service discovery, service supply, and load aggregaion. In his secion, we presen our proposed hree-layer inegraion archiecure design and illusrae he scalabiliy problem in MEC for IoT. Three-Layer Inegraion Archiecure Figure shows he proposed hree-layer inegraion archiecure for he inegraion of he cloud, MEC, and IoT: The user plane is he boom layer consising of boh mobile users (e.g., smarphones, ables, and lapops) and IoT devices, such as indusrial acuaors, wearable devices, and smar sensors. These devices can be grouped ino virual clusers based on heir ownership, and co-locaion and co-service relaionships. The edge compuing plane is in close proximiy o he users. MEC enables cloud compuing capabiliies wihin he radio access neworks o fulfill he requiremens of he IoT services. The geo-disribued edge servers can be organized in a hierarchical srucure o efficienly uilize he resources, FIGURE. The hree-layer inegraion archiecure of he cloud, MEC, and IoT. aggregae he services, and susain he workloads during peak hours. The cloud compuing plane is in he core nework, and consiues muliple cloud servers and daa ceners, which are capable of processing and soring enormous amouns of daa. In his hree-layer archiecure, he daa ceners in he cloud can perform complex compuing and daa analysis, and hence, is responsible for processing he delay-oleran services ha require a large number of sorage and compuaional resources o augmen he ask processing of he edge compuing plane. Specifically, he IoT devices sense a muliude of daa and offload heir services only o he edge servers, insead of offloading direcly o he cloud o reduce he required signaling and corresponding energy consumpion for decision making. The edge servers in proximiy o he user plane collec he offloaded services, prioriize he processing of delay-sensiive services o ease raffic on he backhaul links, and offload delay-oleran services o he cloud based on heir workloads. The advanages of he inegraion archiecure mainly include he following. Efficien service discovery: In millions of IoT devices, he service discovery, ha is, he search for he righ device ha can provide he desired daa or service, is challenging due o is large scale. In he Social Inerne of Things (SIoT) paradigm [4], devices can esablish social relaionships based on heir ownerships, locaions, and services o enhance he process of service discovery. The edge servers IEEE Nework January/February

3 Workload Edge server Workload Cloud server 2 a 2 b Simulaneous arrival of asks FIGURE 2. Scalabiliy problem and selecive offloading. 2 Compuaional resources c Workload Edge server 2 Non-simulaneous arrival of asks are aware of he locaions and services of hese IoT devices, and hen are responsible for grouping hese devices ino virual clusers. The devices in a virual cluser are of similar services, and can aggregae he sensed daa o a cluser head o furher enhance energy efficiency. Moreover, in he edge compuing plane, he edge servers in he hierarchical srucure can exploi he low-laency inerconnecs beween hem o address he devices from a wide range of locaions and improve he service visibiliy for he IoT devices. High-performance compuing as a service: The IoT services may have significanly various requiremens in erms of laency, daa volumes, and reliabiliy. Tradiional mobile cloud compuing canno fulfill he requiremens of low laency and high reliabiliy for indusrial auomaion, e-healh, and auomaic driving. Wih he deploymen of edge servers, MEC has he poenial o make high-performance compuing a service ha resolves he laency flucuaion and delivers reliable services. Workload aggregaion: To complemen he cloud, which canno fulfill he reliabiliy and laency requiremens of IoT services, edge servers are deployed in proximiy o users wih high flexibiliy of geo-disribuion. However, he deploymen of edge servers faces a rade-off beween resource efficiency and service provisioning during peak workloads. Specifically, he scarce deploymen of edge servers will inroduce excessive delay due o lack of compuaional resources, bu provisioning more resources hrough dense deploymen could resul in poor resource uilizaion. The ree-srucured hierarchical archiecure of he edge servers ensures efficien resource uilizaion by aggregaing services across differen iers of servers, and can susain heavy workloads even during peak hours. However, considering he resource bolenecks in he edge servers, scalabiliy is an inheren problem in he proposed archiecure. 2 Scalabiliy Problem Services can be execued locally or offloaded o he cloud or edge servers. A large number of asks ha arrive a he same edge server will exhaus he compuaional resources and face scalabiliy problems. Figure 2 illusraes he scalabiliy problem in he proposed inegraion archiecure. When asks arrive non-simulaneously, he offloaded asks can uilize he compuaional resources in urns, and achieve low laency and high reliabiliy as desired. However, when asks arrive simulaneously, offloading all he asks o he same edge server may undergo severe resource scarciy and may suffer much longer service laency in consequence. Especially in he IoT scenario, housands of devices may wake up concurrenly and compee for he limied resources in he edge servers. This no only significanly degrades he user experience for he services ha require ulra low laency and high reliabiliy, bu also hampers he lifeimes of he IoT devices, since he devices have o say acive when waiing for he compuaion resuls. As a resul, he resource bolenecks and workloads of he edge servers inroduce a rade-off beween he number of offloaded asks and he QoS. In addiion, he heerogeneiy among millions of devices and heir services would inensify he scalabiliy problem. In Fig. 2, he blue pars of he edge severs and he cloud server denoe heir workloads (i.e., he congesion of asks). Specifically, edge server is congesed by he offloaded asks and canno serve he offloaded asks promply (i.e., face he scalabiliy problem). As dicaed in Fig. 2, using selecive offloading, a device can: Execue is ask locally Offload is ask o edge server 2 under ligh workload (e.g., via dual conneciviy) [5] Offload is ask o he cloud hrough he edge compuing plane so as o alleviae he scalabiliy problem and balance he workloads among edge servers Proposed Reques and Admission Framework for he Green IoT Green neworking, which aims a reducing energy consumpion and imizing operaional coss, plays an imporan role in he IoT paradigm. This is even more crucial for energy-consrained sensors, which are expeced o run auonomously for long periods. The longer acive ime, as described in he scalabiliy problem, would hamper he lifeimes of IoT devices, which necessiaes selecive offloading for energy saving. However, he selecive offloading schemes in [9, 0] require coordinaion among devices, which resuls in a wase of energy in millions of IoT devices. In his secion, we discuss he key challenges in offloading in MEC, and propose a reques and admission framework for he green IoT. Challenges of Offloading in MEC Offloading sraegies for ask pariioning have been exensively sudied in mobile cloud compuing (e.g., [5, 6]). In [5], a dynamic programg approach was developed o imize he execuion laency under cos consrains. In [6], an adapive receding horizon offloading sraegy among muliple devices was proposed, where he solver can adjus is offloading decision according o environmenal dynamics (e.g., flucuaing laency). However, compared o he cloud, he edge servers in MEC are heerogeneous and raher limied in erms of sorage and compuaional capabiliies. The compeiion for he compuaional resources 56 IEEE Nework January/February 208

4 inroduces couplings of decision making among devices. As a resul, offloading in MEC is more challenging han in mobile cloud compuing. The sudies in [9, 0] verify he scalabiliy problem due o he resource bolenecks and propose selecive offloading schemes. In paricular, in [9], he offloading compeiion among muliple devices was modeled as a sequenial offloading game, where he mobile devices made offloading decisions sequenially o obain a sable offloading resul. Assug ha asks are exremely resource demanding, in [0], only one ask was seleced for offloading a he same ime, and boh offline and online algorihms were proposed o opimize he allocaion of wireless and compuaional resources. In [2], a heurisic scheme based on a submodular opimizaion mehod was proposed o joinly opimize offloading decisions and resource allocaion, bu only for delay-oleran services. Developing efficien offloading schemes in MEC for IoT faces he following challenges. Coordinaion coss: Coordinaion among devices consumes energy and incurs furher laency due o communicaion overhead. Moreover, he coordinaion coss increase exponenially wih he number of devices, and herefore, enabling coordinaion may be cos-prohibiive when he scale of IoT (millions of devices) is considered. Couplings among ask parioning: Curren selecive offloading schemes in MEC [9, 0, 2] only consider offloading services as a whole insead of offloading par of a service o increase efficiency as in [5, 6]. This is because he resource bolenecks of he edge servers inroduce srong couplings among ask pariioning of differen devices. Solving he problem opimally requires full knowledge on boh he devices and edge servers, including fuure ask arrivals and channel condiions. Heerogeneiy of edge servers and devices Boh edge servers and IoT devices are heerogeneous in erms of compuing and sorage capabiliies, as well as desirable services. The heerogeneiy makes he selecion of he offloaded devices even more challenging. For insance, he cloud, wih abundan compuaional resources, may prefer o execue resource-inensive and delay-oleran services, while offloading he delay-sensiive services wih large volumes of daa o edge servers may achieve high reliabiliy and low laency, and reduce he energy consumpion on backhaul links. In summary, he coordinaion coss necessiae he developmen of a lighweigh scheme for he IoT devices, while he couplings among ask pariioning require frequen communicaion among devices and even he exac predicion of fuure ask arrivals and channel condiions o resolve he scalabiliy problem. Moreover, he selecion of offloaded devices from a large number of IoT devices is even more challenging due o he heerogeneiy of edge servers and devices. Reques and Admission Framework for he Green IoT The proposed reques and admission framework is lighweigh in erms of signaling overhead, where he devices can send offloading requess independenly while he servers only admi seleced requess. This is because he dependency of ask pariioning among muliple devices can be decoupled by encapsulaing he laency requiremen in he offloading requess o he compuing servers. Program profiler QoS manager Mobile device Decision engine Communicaion inerface Reques Admission Admission conroller FIGURE 3. The proposed reques and admission framework. The laency requiremens are se as he deadlines of each ask deered by he ask pariioning schemes, and he edge servers make bes effors o saisfy he requiremens such ha he asks can be execued wihou delays. Besides, he ask pariioning schemes in [5] can help devices selec he offloaded server among muliple edge servers. The working procedure of he proposed reques and admission framework consiss of hree sages: Each mobile device pariions is asks independenly, and sends an offloading reques o he seleced compuing server including he laency requiremens and oher inrinsic feaures of he device and is service (e.g., he memory requiremen, he hread CPU ime, and he needed CPU cycles of he service). Each server receives he offloading requess, only admis he seleced users for offloading, and pre-allocaes he compuaional resources o saisfy laency requiremens. Mobile devices offload heir asks according o he admission resuls. The proposed lighweigh framework enables he selecion funcionaliy in boh servers and devices o reduce signaling overhead, where only he informaion on offloading requess and admission resuls mus be exchanged hrough he communicaion inerface. As shown laer, he signaling overhead can be furher reduced by enabling he devices o be self-noaed and self-denied for offloading. Figure 3 shows he major blocks of he proposed reques and admission framework, which can be operaed a he devices and servers separaely. The blocks in mobile devices mainly include program profilers, QoS managers, decision engines, and synchronizers. In paricular, he program profiler moniors he program parameers such as execuion ime, acquired memory, hread CPU ime, number of insrucions, and mehod calls; he QoS manager deeres he service requiremens (e.g., he laency, energy consumpion, and reliabiliy) and esimaes he required laency and energy consumpion for execuion of he service; he decision engine is responsible for he ask pariioning and selecs he desired server o send offloading requess; and he synchronizer handles he communicaion and synchronizaion beween devices and servers in order o ensure inegriy of he offloaded daa. On he oher hand, he blocks in compuing servers consis of synchronizers, admission conrollers, resource schedulers, and virual machine (VM) Compuing server Resource scheduler VM manager IEEE Nework January/February

5 Program profiler User side Server side Resource scheduler Decision engine U monior program parameers U3 make offloading decision U2 deere laency requiremen U4 send offloading reques S7 send admission resul S2 classify requesed users S receive offloading reques S6 admission decision S4 uilize remaining resources Admission conroller S3 allocae resources S5 prepare for offloading QoS manager Communicaion inerface VM manager FIGURE 4. Flow diagram of our proposed selecive offloading scheme. In he proposed scheme, he delay-sensiive asks are given high prioriy for processing, and hence, he delay-oleran asks are queued a he edge server under heavy workload. As a resul, he edge server only processes delay-oleran asks under ligh workload, and offloads he queueing asks o he cloud o avoid excessive queueing delay under heavy workload. managers. Specifically, he synchronizer receives he offloading requess; he admission conroller selecs he devices for offloading according o is curren available resources; and he resource scheduler and VM manager allocae he compuaional resources and acivae he VM o prepare for he offloading from he seleced devices. Implemening he Selecive Offloading Scheme In his secion, we demonsrae he implemenaion of he proposed reques and admission framework in a muli-user MEC scenario, where he LTE macro BS is co-locaed wih an edge server of limied compuaional resources, denoed by f 0.The proposed selecive offloading scheme follows he working procedure of he reques and admission framework, as summarized in Fig. 4. The working procedure mainly consiss of:. Forg he offloading requess a he devices 2. Allocaing he resources under he laency requiremens a he resource scheduler 3. Exploiing he heerogeneiy among devices o selec he energy-saving services for offloading a he admission conroller In he proposed scheme, he delay-sensiive asks are given high prioriy for processing, and hence, he delay-oleran asks are queued a he edge server under heavy workload. As a resul, he edge server only processes delay-oleran asks under ligh workload, and offloads he queueing asks o he cloud o avoid excessive queueing delay under heavy workload. In he following, we analyze hese seps in deail o illusrae he selecive offloading scheme. Offloading Reques Formaion The offloading requess are formed a each mobile device independenly. A ask can be described in erms of:. Inpu D i, including sysem seings, program codes, and inpu parameers 2. The number of CPU cycles required o accomplish he ask, denoed by C i The informaion abou D i and C i can be obained hrough he program profiler. The laency and energy consumpion of local execuion, denoed by T i and E i, respecively, can be l l obained a he QoS manager [9, 0]. Besides, he QoS manager can deere he laency requiremen T i based on he deadlines deered by req he ask pariioning schemes [5, 6]. A ask can also be offloaded for remoe execuion o he servers. A ypical remoe compuing approach consiss of hree sages:. Uploading he inpu 2. Remoe execuion a he edge server 3. Receiving he compuaion resul The size of compuaion resul is much smaller han ha of inpu, and he overhead can be negleced [9]. As a resul, he oal remoe compuaion ime of device i can be obained as T i r = T i + T e i, which is composed of wo pars: he uplink ransmission ime T i = D i /R i and he remoe e execuion ime T i = C i /f i. R i is he achieved daa rae of device i for he uplink ransmission, and f i is he allocaed compuaional resources by he edge server. The energy consumpion for remoe compuaion of device i can be given by E r i = (p i / {z i }) T i, where z i is he power amplifier efficiency of device i. Then he decision engine can apply he exising offloading sraegies in [5, 6], o deere he server o send offloading requess. The offloading reques of device i consiss of boh he laency requiremen and inrinsic feaures of he device. Compuaional Resource Allocaion A he server side, he synchronizer receives he offloading requess. Then he problem of ineres becomes selecing he offloaded asks and allocaing he limied resources o imize he sysem energy consumpion while saisfying he laency requiremens of all he offloading requess. Noe ha he asks have o be accomplished before he deadlines (i.e., he laency requiremens) deered by ask pariioning. As a resul, he allocaed compuaional resources should saisfy f i f i = C i /(T i req Ti ), where f i denoes he imum resources allocaed o device i under he laency requiremens. In order 58 IEEE Nework January/February 208

6 o enhance he scalabiliy and save energy, he resource scheduler allocaes he imum compuaional resources o he admied asks according o f i = s i f i = s i C i /(T i req Di /R i ) () where s i {0,} denoes wheher he ask is admied for offloading or no (i.e., he ask is offloaded when s i = ). Offloading Decision The heerogeneiy of devices and heir IoT services makes offloading more beneficial for some devices, and local execuion more beneficial for ohers. For insance, a delay-sensiive service a a resource-resrained device will benefi from offloading. Therefore, we inroduce he following condiion o prioriize emergency asks for offloading. Condiion. If T i^l > T i req, he admission conroller selecs device i for offloading. The resource-resrained devices wih delay-sensiive asks saisfying Condiion are prioriized for offloading, since heir local compuing capabiliies req canno fulfill he laency requiremens (i.e., T i > T i ). Then he edge server pre-allocaes f i resources o hese devices, deeres is remaining resources f ~ 0 = f 0 S T l i i >T i req f i, and checks he following condiion o exclude some devices from offloading. Condiion 2. If (T i r ) = T i + C i / ~ f 0 > T i req or Ei r E i l, device i execues is ask locally. If his condiion is saisfied, even allocaing all he remaining resources o device i canno saisfy is laency requiremens, or offloading will no save energy. Thus, he device is excluded from offloading and chooses o execue he ask locally. Noe ha he classificaion of requesed users in S2 of Fig. 4 can be disribued o he IoT devices o furher reduce he signaling overhead of MEC. Paricularly, devices saisfying Condiion can be self-noaed o send an indicaion o he edge server for offloading prioriizaion. Then he edge server broadcass is remaining resources ~ f 0 o he devices. Afer receiving ~ f 0, he devices saisfying Condiion 2 can be self-denied for offloading wihou sending offloading requess. As a resul, only he undeered devices ha are neiher self-noaed nor self-denied send offloading requess o he edge server, which leads o he reducion of signaling overhead in implemenaion. Afer receiving he offloading requess from he undeered devices, he resource scheduler allocaes he imum compuaional resources by Eq.. Then he selecive offloading problem can be reduced o a binary linear programg problem, which can be efficienly solved hrough a branch and bound algorihm. The heerogeneiy of devices and heir IoT services makes offloading more beneficial for some devices, and local execuion more beneficial for ohers. For insance, a delay-sensiive service a a resource-resrained device will benefi from offloading. Average laency (s) Local Toal Selecive, T req = s Selecive, T req =.5 s Number of requess FIGURE 5. Comparison of average laency. Selecive offloading approaches o he laency requiremens. Numerical Resuls In his secion, numerical resuls are presened o demonsrae he performance improvemens brough by our proposed selecive offloading scheme. We consider a single macrocell nework wih a radius of 250 m, which is co-locaed wih an edge server wih f 0 = 0 GHz. The radio communicaion parameers follow he Third Generaion Parnership Projec (3GPP) specificaion. As an example of a complex applicaion, we adop he face recogniion applicaion [9] where D = 420 kb and C =000 MCycles. The compuaional capabiliy of devices is uniformly disribued in [0.5,.5] GHz. We se he laency requiremens T req as s or.5 s for delay-sensiive and delay-oleran applicaions, respecively. Nex, we evaluae he average laency and energy consumpion of selecive offloading, local execuion, and oal offloading when he number of offloading requess n varies from 5 o 20. Figure 5 shows he average per-user laency in boh delay-sensiive and delay-oleran applicaions. Our scheme can leverage he compuaional resources in he devices and edge servers, and make effecive ask admission o saisfy boh sringen and loose laency requiremens. In paricular, he average laency of our scheme approaches T req = s of delay-sensiive applicaions, and is abou.4 s for delay-oleran applicaions. Irrespecive of n, he average laency of local execuion is. s, violaing he laency requiremen of delay-sensiive applicaions wih T req = s. The average laency of oal offloading grows linearly wih N, and is up o.6 s when N = 20. This is because he scalabiliy problem due o resource scarciy is inensified wih increasing n. Figure 6 demonsraes he average per-user energy consumpion. The energy consumpion of oal offloading and local execuion is relaively sable, and can be lower han ha of selecive offloading wih increasing n when T req = s. This is a he cos of violaing laency requiremens, as shown in Fig. 5. We also see ha he energy consumpion of selecive offloading in delay-oleran applicaions says low (0.085j), which is 22 percen less han local execuion. However, for delay-sensiive applicaions, he energy consumpion of selecive offloading only says low and sable when N < 2. The energy consumpion increasingly grows (up o percen higher han ha of local execuion) as N increases from 2 o 20, since offloading he asks of some resource-resrained devices may no save energy. This reveals he inheren rade-off IEEE Nework January/February

7 The proposed selecive offloading scheme can imize he energy consumpion of devices under laency requiremens, and he signaling overhead can be furher reduced by enabling he devices o be self-noaed or self-denied for offloading. Average energy consumpion (J) Local Toal Selecive, T req = s Selecive, T req =.5 s Number of requess FIGURE 6. Comparison of average energy consumpion. The rade-off beween laency and energy consumpion is inheren. beween laency and energy consumpion in MEC, ha is, laency and energy consumpion canno be imized a he same ime. Conclusion In his aricle, we propose a hree-layer inegraion archiecure of he cloud, MEC, and IoT, and develop a lighweigh reques and admission framework o resolve he scalabiliy problem by offloading only seleced services. By encapsulaing laency requiremens in offloading requess, he framework can be operaed a devices and edge servers separaely wihou he need o coordinae among devices. The proposed selecive offloading scheme can imize he energy consumpion of devices under laency requiremens, and he signaling overhead can be furher reduced by enabling he devices o be self-noaed or self-denied for offloading. Numerical resuls show ha, by prioriizing he emergency offloading requess, selecive offloading is able o saisfy he laency requiremens of differen services and save energy for he IoT devices. References [] X. Chen e al., Opimal Qualiy-of-Service Scheduling for Energy-Harvesing Powered Wireless Communicaions, IEEE Trans. Wireless Commun., vol. 5, no. 5, May 206, pp [2] Y. Zhang e al., A Survey on Emerging Compuing Paradigms for Big Daa, Chinese J. Elecronics, vol. 26, no., 207, pp. 2. [3] J. Ren e al., Exploiing Mobile Crowdsourcing for Pervasive Cloud Services: Challenges and Soluions, IEEE Commun. Mag., vol. 53, no. 3, Mar. 205, pp [4] Mobile-Edge Compuing Inroducory Technical Whie Paper, Mobile Edge Compuing (MEC) indusry iniiaive, 204. [5] Y. H. Kao e al., Hermes: Laency Opimal Task Assignmen for Resource-Consrained Mobile Compuing, IEEE Trans. Mobile Comp., 207. [6] X. Lyu and H. Tian, Adapive Receding Horizon Offloading Sraegy Under Dynamic Environmen, IEEE Commun. Le., vol. 20, no. 5, May 206, pp [7] R. Yu e al., Decenralized and Opimal Resource Cooperaion in Geo-Disribued Mobile Cloud Compuing, IEEE Trans. Emerg. Topics Comp., 206. [8] R. Yu e al., Cooperaive Resource Managemen in Cloud-Enabled Vehicular Neworks, IEEE Trans. Ind. Elecronics, vol. 62, no. 2, Dec. 205, pp [9] X. Chen e al., Efficien Muli-User Compuaion Offloading for Mobile-Edge Cloud Compuing, IEEE/ACM Trans. Ne., vol. 24, no. 5, Oc. 206, pp [0] W. Labidi, M. Sarkiss, and M. Kamoun, Join Muli-User Resource Scheduling and Compuaion Offloading in Small Cell Neworks, IEEE Wireless and Mobile Compuing, Neworking and Commun., Oc. 205, pp [] J. Ren e al., Serving a he Edge: A Scalable IoT Archiecure Based on Transparen Compuing, IEEE Nework, 207. [2] X. Lyu e al., Muliuser Join Task Offloading and Resource Opimizaion in Proximae Clouds, IEEE Trans. Vehic. Tech., vol. 66, no. 4, Apr. 207, pp [3] X. Lyu e al., Opimal Schedule of Mobile Edge Compuing for Inerne of Things Using Parial Informaion, IEEE JSAC, 207. [4] A. M. Oriz e al., The Cluser Beween Inerne of Things and Social Neworks: Review and Research Challenges, IEEE Inerne Things J., vol., no. 3, June 204, pp [5] Y. Wu e al., Secrecy-Based Energy-Efficien Daa Offloading Via Dual Conneciviy Over Unlicensed Specrums, IEEE JSAC, vol. 34, no. 2, Dec. 206, pp Biographies Xinchen Lyu received his B.E. degree from Beijing Universiy of Poss and Telecommunicaions (BUPT), China, in 204. He is currenly pursuing a Ph.D. degree a BUPT. His research ineress include mobile edge compuing and radio resource managemen. Hui Tian received her M.S. in micro-elecronics and Ph.D. degree in circuis and sysems from BUPT in 992 and 2003, respecively. Currenly, she is a professor a BUPT and he Nework Informaion Processing Research Cener direcor of he Sae Key Laboraory of Neworking and Swiching Technology. Her curren research ineress mainly include radio resource managemen, cross-layer opimizaion, M2M, cooperaive communicaion, mobile social neworks, and mobile edge compuing. Li Jiang received her Ph.D. degree from he School of Informaion and Communicaions Engineering, BUPT, in 207. She was also wih Simula Research Laboraory as a visiing scholar from 205 o 206. She is currenly an associae professor wih he School of Auomaion, Guang Dong Universiy of Technology. Her curren research ineress include D2D communicaions, energy harvesing, physical layer securiy, and mobile social neworks. Alexey Vinel is a professor of compuer communicaions wih he School of Informaion Technology, Halmsad Universiy, Sweden. He received his Ph.D. degree from he Insiue for Informaion Transmission Problems, Russia, in 2007 His research ineress include wireless communicaions, vehicular neworking, cooperaive inelligen ransporaion sysems, and auonomous driving. Sabia Maharjan received her M.Eng. in wireless communicaion from he Anenna and Propagaion Laboraory, Tokyo Insiue of Technology, Japan, in 2008, and her Ph.D. degree in nework and disribued sysems from he Universiy of Oslo and Simula Research Laboraory, Norway, in 203. She is currenly a posdocoral fellow wih Simula Research Laboraory. Her curren research ineress include resource opimizaion, nework securiy, game heory, smar grid communicaions, and he Inerne of Things. Sein Gjessing received his Dr.Phil. degree in compuer science from he Universiy of Oslo in 985. He is currenly a professor of compuer science wih he Deparmen of Informaics, Universiy of Oslo, Norway, and an adjunc researcher a Simula Research Laboraory. His curren research ineress include nework and ranspor proocols, nework resilience, cogniive radio neworks, and he smar grid. His original research was in he field of objec-oriened concurren programg. He has researched compuer inerconnecs such as he scalable coheren inerface (IEEE Sandard 596), and local area nework/ meropolian area neworks such as he resilien packe ring (IEEE Sandard 802.7). Yan Zhang received his Ph.D. degree from he School of Elecrical and Elecronics Engineering, Nanyang Technological Universiy, Singapore. He is currenly a full professor wih he Deparmen of Informaics, Universiy of Oslo. His curren research ineress include nex-generaion wireless neworks leading o 5G, green and secure cyber-physical sysems (e.g., smar grid, healhcare, and ranspor). He is a Regional Edior, an Associae Edior, on he Ediorial Boards, and a Gues Edior of a number of inernaional journals. 60 IEEE Nework January/February 208