Energy aware edge computing: A survey

Edge computing is an emerging paradigm to meet the ever-increasing computation demands from pervasive devices such as sensors, actuators, and smart things. Though the edge devices can execute complex applications, it is necessary for some applications to migrate to centralized servers. By offloading the computation from the edge nodes to the edge servers or cloud servers, the quality of computation experience could be greatly improved. However, it may cause delay and increase network overheads, and energy consumption eventually. Therefore, an optimal offloading strategy should take into account what task should be offloaded, when to offload and where to offload to avoid the overheads. Thus, it is important to tradeoff between energy consumption, computation delay and throughput when the system makes the computation offloading to achieve high energy efficiency. In this paper, we conduct a survey of energy aware edge computing, including the existing work on computation offloading frameworks and strategies in edge computing. Specifically, we describe the strategies from the perspective of energy aware offloading, energy optimization offloading and offloading algorithms.

[1]  Antonio Iera,et al.  Lightweight service replication for ultra-short latency applications in mobile edge networks , 2017, 2017 IEEE International Conference on Communications (ICC).

[2]  Mianxiong Dong,et al.  Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing , 2018, IEEE Network.

[3]  James H. Laros,et al.  Evaluating the viability of process replication reliability for exascale systems , 2011, 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

[4]  Sven Helmer,et al.  A Container-Based Edge Cloud PaaS Architecture Based on Raspberry Pi Clusters , 2016, 2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW).

[5]  Tao Zhang,et al.  CryptSQLite: Protecting Data Confidentiality of SQLite with Intel SGX , 2017, 2017 International Conference on Networking and Network Applications (NaNA).

[6]  Jianqiu Li,et al.  Fault diagnosis and quantitative analysis of micro-short circuits for lithium-ion batteries in battery packs , 2018, Journal of Power Sources.

[7]  Jack J. Dongarra,et al.  From CUDA to OpenCL: Towards a performance-portable solution for multi-platform GPU programming , 2012, Parallel Comput..

[8]  Mehmet Demirci,et al.  A Survey of Machine Learning Applications for Energy-Efficient Resource Management in Cloud Computing Environments , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

[9]  Fernando M. V. Ramos,et al.  Software-Defined Networking: A Comprehensive Survey , 2014, Proceedings of the IEEE.

[10]  Kian-Lee Tan,et al.  Authenticating query results in edge computing , 2004, Proceedings. 20th International Conference on Data Engineering.

[11]  Schahram Dustdar,et al.  Towards QoS-Aware Fog Service Placement , 2017, 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC).

[12]  D. Carlson,et al.  Solar cells using discharge-produced amorphous silicon , 1977 .

[13]  Athanasios V. Vasilakos,et al.  Software-Defined Networking for Internet of Things: A Survey , 2017, IEEE Internet of Things Journal.

[14]  Terence D. Todd,et al.  Energy Aware Offloading for Competing Users on a Shared Communication Channel , 2017, IEEE Transactions on Mobile Computing.

[15]  Bo Yuan,et al.  Mobilouds: An Energy Efficient MCC Collaborative Framework With Extended Mobile Participation for Next Generation Networks , 2016, IEEE Access.

[16]  Kai Li,et al.  The PARSEC benchmark suite: Characterization and architectural implications , 2008, 2008 International Conference on Parallel Architectures and Compilation Techniques (PACT).

[17]  Wenzhong Li,et al.  Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing , 2015, IEEE/ACM Transactions on Networking.

[18]  Jagan Singh Meena,et al.  Overview of emerging nonvolatile memory technologies , 2014, Nanoscale Research Letters.

[19]  Jun Wang,et al.  Application-Specific Performance-Aware Energy Optimization on Android Mobile Devices , 2017, 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA).

[20]  Jie Xu,et al.  EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks , 2017, IEEE Journal on Selected Areas in Communications.

[21]  Lixia Zhang,et al.  An Overview of Security Support in Named Data Networking , 2018, IEEE Communications Magazine.

[22]  Guangjie Han,et al.  A Maximum Cache Value Policy in Hybrid Memory-Based Edge Computing for Mobile Devices , 2019, IEEE Internet of Things Journal.

[23]  Athanasios V. Vasilakos,et al.  Secure Data Sharing and Searching at the Edge of Cloud-Assisted Internet of Things , 2017, IEEE Cloud Computing.

[24]  Joseph F. Parker,et al.  Rechargeable nickel–3D zinc batteries: An energy-dense, safer alternative to lithium-ion , 2017, Science.

[25]  Guangjie Han,et al.  An Energy Efficient and QoS Aware Routing Algorithm Based on Data Classification for Industrial Wireless Sensor Networks , 2018, IEEE Access.

[26]  Michael F. P. O'Boyle,et al.  Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM , 2014, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[27]  Carlo Cavazzoni EURORA: a European architecture toward exascale , 2012, FutureHPC '12.

[28]  Guangjie Han,et al.  Characteristics of Co-Allocated Online Services and Batch Jobs in Internet Data Centers: A Case Study From Alibaba Cloud , 2019, IEEE Access.

[29]  Weisong Shi,et al.  Edge Computing: Vision and Challenges , 2016, IEEE Internet of Things Journal.

[30]  Geoffrey Fox,et al.  Energy-efficient multisite offloading policy using Markov decision process for mobile cloud computing , 2016, Pervasive Mob. Comput..

[31]  Dong Wang,et al.  Cooperative-Competitive Task Allocation in Edge Computing for Delay-Sensitive Social Sensing , 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC).

[32]  Haitao Zhang,et al.  NDN host model , 2018, CCRV.

[33]  Viresh Dutta,et al.  Thin‐film solar cells: an overview , 2004 .

[34]  David Levin,et al.  A secure content network in space , 2012, CHANTS '12.

[35]  Vangelis Metsis,et al.  IoT Middleware: A Survey on Issues and Enabling Technologies , 2017, IEEE Internet of Things Journal.

[36]  Yuqing Zhu,et al.  BigDataBench: A big data benchmark suite from internet services , 2014, 2014 IEEE 20th International Symposium on High Performance Computer Architecture (HPCA).

[37]  Shivakant Mishra,et al.  Optimizing power consumption in multicore smartphones , 2016, J. Parallel Distributed Comput..

[38]  Xiaohui Peng,et al.  The Φ-stack for smart web of things , 2017, SmartIoT@SEC.

[39]  Sherief Reda,et al.  Adaptive Power Capping for Servers with Multithreaded Workloads , 2012, IEEE Micro.

[40]  Mario Di Francesco,et al.  Energy conservation in wireless sensor networks: A survey , 2009, Ad Hoc Networks.

[41]  Hassan Harb,et al.  En-Route Data Filtering Technique for Maximizing Wireless Sensor Network Lifetime , 2018, 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC).

[42]  Guillaume Aupy,et al.  Energy-Aware Checkpointing Strategies , 2015 .

[43]  Qing Yang,et al.  Fog Data: Enhancing Telehealth Big Data Through Fog Computing , 2015, ASE BD&SI.

[44]  Weisong Shi,et al.  Position Paper: Challenges Towards Securing Hardware-assisted Execution Environments , 2017, HASP@ISCA.

[45]  C. Wild,et al.  Study of fault-tolerant software technology , 1984 .

[46]  Bronis R. de Supinski,et al.  ALEA: Fine-Grain Energy Profiling with Basic Block Sampling , 2015, 2015 International Conference on Parallel Architecture and Compilation (PACT).

[47]  Thomas Rausch,et al.  Message-oriented middleware for edge computing applications , 2017, Middleware Doctoral Symposium.

[48]  Rafael Asenjo,et al.  Workload Partitioning Strategy for Improved Parallelism on FPGA-CPU Heterogeneous Chips , 2018, 2018 28th International Conference on Field Programmable Logic and Applications (FPL).

[49]  Jeongho Kwak,et al.  DREAM: Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems , 2015, IEEE Journal on Selected Areas in Communications.

[50]  Xiaopei Wu,et al.  CAVBench: A Benchmark Suite for Connected and Autonomous Vehicles , 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC).

[51]  Minhaj Ahmad Khan,et al.  A survey of computation offloading strategies for performance improvement of applications running on mobile devices , 2015, J. Netw. Comput. Appl..

[52]  Mahmoud Al-Ayyoub,et al.  Software Defined Storage for cooperative Mobile Edge Computing systems , 2017, 2017 Fourth International Conference on Software Defined Systems (SDS).

[53]  Kerstin Eder,et al.  Energy Transparency for Deeply Embedded Programs , 2017, ACM Trans. Archit. Code Optim..

[54]  Shoaib Akram Managed Language Runtimes on Heterogeneous Hardware: Optimizations for Performance, Efficiency and Lifetime Improvement , 2017, Programming.

[55]  Weisong Shi,et al.  EdgeBox: Live Edge Video Analytics for Near Real-Time Event Detection , 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC).

[56]  Danda B. Rawat,et al.  Software Defined Networking Architecture, Security and Energy Efficiency: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[57]  Song Guo,et al.  Energy-Efficient Transmission Scheduling in Mobile Phones Using Machine Learning and Participatory Sensing , 2015, IEEE Transactions on Vehicular Technology.

[58]  Hui Tian,et al.  Multiuser Joint Task Offloading and Resource Optimization in Proximate Clouds , 2017, IEEE Transactions on Vehicular Technology.

[59]  Serge J. Belongie,et al.  SD-VBS: The San Diego Vision Benchmark Suite , 2009, 2009 IEEE International Symposium on Workload Characterization (IISWC).

[60]  Weisong Shi,et al.  The Promise of Edge Computing , 2016, Computer.

[61]  Mohsen Guizani,et al.  Energy-efficient cloud resource management , 2014, 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[62]  Li Zhou,et al.  VRAA: virtualized resource auction and allocation based on incentive and penalty , 2012, Cluster Computing.

[63]  Hai Jin,et al.  Energy efficient task allocation and energy scheduling in green energy powered edge computing , 2019, Future Gener. Comput. Syst..

[64]  Beichuan Zhang,et al.  On broadcast-based self-learning in named data networking , 2017, 2017 IFIP Networking Conference (IFIP Networking) and Workshops.

[65]  R. Mickelsen,et al.  High photocurrent polycrystalline thin‐film CdS/CuInSe2 solar cella , 1980 .

[66]  Yunsi Fei,et al.  QELAR: A Machine-Learning-Based Adaptive Routing Protocol for Energy-Efficient and Lifetime-Extended Underwater Sensor Networks , 2010, IEEE Transactions on Mobile Computing.

[67]  Hong Zhong,et al.  Firework: Big Data Sharing and Processing in Collaborative Edge Environment , 2016, 2016 Fourth IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb).

[68]  Xiaopei Wu,et al.  OpenVDAP: An Open Vehicular Data Analytics Platform for CAVs , 2018, 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS).

[69]  Qun Li,et al.  Efficient service handoff across edge servers via docker container migration , 2017, SEC.

[70]  Yan Zhang,et al.  Optimal delay constrained offloading for vehicular edge computing networks , 2017, 2017 IEEE International Conference on Communications (ICC).

[71]  Rahul Khanna,et al.  RAPL: Memory power estimation and capping , 2010, 2010 ACM/IEEE International Symposium on Low-Power Electronics and Design (ISLPED).

[72]  D. Carlson,et al.  AMORPHOUS SILICON SOLAR CELL , 1976 .

[73]  Mani B. Srivastava,et al.  SensorWare: Programming sensor networks beyond code update and querying , 2007, Pervasive Mob. Comput..

[74]  Qun Li,et al.  Security and Privacy Issues of Fog Computing: A Survey , 2015, WASA.

[75]  Chris Fallin,et al.  Memory power management via dynamic voltage/frequency scaling , 2011, ICAC '11.

[76]  Woo-Seok Choi,et al.  Guaranteeing Local Differential Privacy on Ultra-Low-Power Systems , 2018, 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA).

[77]  Pingfan Meng,et al.  FPGA-GPU-CPU heterogenous architecture for real-time cardiac physiological optical mapping , 2012, 2012 International Conference on Field-Programmable Technology.

[78]  Yonggang Wen,et al.  Data Center Energy Consumption Modeling: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[79]  Kaibin Huang,et al.  Asynchronous Mobile-Edge Computation Offloading: Energy-Efficient Resource Management , 2018, IEEE Transactions on Wireless Communications.

[80]  Eriko Nurvitadhi,et al.  Accelerating recurrent neural networks in analytics servers: Comparison of FPGA, CPU, GPU, and ASIC , 2016, 2016 26th International Conference on Field Programmable Logic and Applications (FPL).

[81]  Song Han,et al.  ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA , 2016, FPGA.

[82]  Nathalie Mitton,et al.  The design of the gateway for the Cloud of Things , 2017, Ann. des Télécommunications.

[83]  Daisuke Takahashi,et al.  The HPC Challenge (HPCC) benchmark suite , 2006, SC.

[84]  Yumei Wang,et al.  Energy Aware Virtual Machine Scheduling in Data Centers , 2019, Energies.

[85]  Israel Koren,et al.  Fault-Tolerant Systems , 2007 .

[86]  Matthew L. Johnston,et al.  Heterogeneous Integration of CMOS Sensors and Fluidic Networks Using Wafer-Level Molding , 2018, IEEE Transactions on Biomedical Circuits and Systems.

[87]  Trevor N. Mudge,et al.  Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge , 2017, ASPLOS.

[88]  Bertrand A. Maher,et al.  Glow: Graph Lowering Compiler Techniques for Neural Networks , 2018, ArXiv.

[89]  Ghulam Mujtaba,et al.  Energy Efficient Data Encryption Techniques in Smartphones , 2019, Wirel. Pers. Commun..

[90]  Laurent Lemarchand,et al.  iFogStor: An IoT Data Placement Strategy for Fog Infrastructure , 2017, 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC).

[91]  Hyun-Wook Lee,et al.  Scalable synthesis of silicon-nanolayer-embedded graphite for high-energy lithium-ion batteries , 2016, Nature Energy.

[92]  Carole-Jean Wu,et al.  Machine Learning at Facebook: Understanding Inference at the Edge , 2019, 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA).

[93]  Bruno Ciciani,et al.  A Power Cap Oriented Time Warp Architecture , 2018, SIGSIM-PADS.

[94]  Ke Zhang,et al.  Energy-Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks , 2016, IEEE Access.

[95]  Yonggang Wen,et al.  Energy-efficient scheduling policy for collaborative execution in mobile cloud computing , 2013, 2013 Proceedings IEEE INFOCOM.

[96]  Félix García Carballeira,et al.  Fog computing through public-resource computing and storage , 2017, 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC).

[97]  Weisong Shi,et al.  Energy Proportional Servers: Where Are We in 2016? , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).

[98]  H. Meling,et al.  SenseWrap: A service oriented middleware with sensor virtualization and self-configuration , 2009, 2009 International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP).

[99]  Abdelmadjid Bouabdallah,et al.  Trusted Execution Environment: What It is, and What It is Not , 2015, TrustCom 2015.

[100]  Siobhán Clarke,et al.  Middleware for Internet of Things: A Survey , 2016, IEEE Internet of Things Journal.

[101]  Samee Ullah Khan,et al.  An Energy-Efficient Task Scheduling Algorithm in DVFS-enabled Cloud Environment , 2015, Journal of Grid Computing.

[102]  Mahadev Satyanarayanan,et al.  You can teach elephants to dance: agile VM handoff for edge computing , 2017, SEC.

[103]  Po-Ting Lai,et al.  Design and Implementation of a Critical Speed-Based DVFS Mechanism for the Android Operating System , 2010, 2010 5th International Conference on Embedded and Multimedia Computing.

[104]  Naixue Xiong,et al.  Interdomain I/O Optimization in Virtualized Sensor Networks , 2018, Sensors.

[105]  Juan Benet,et al.  IPFS - Content Addressed, Versioned, P2P File System , 2014, ArXiv.

[106]  Raphaël Couturier,et al.  On the performance of resource-aware compression techniques for vital signs data in wireless body sensor networks , 2018, 2018 IEEE Middle East and North Africa Communications Conference (MENACOMM).

[107]  Reagan Moore,et al.  Network Policy and Services: A Report of a Workshop on Middleware , 2000, RFC.

[108]  John Kim,et al.  TCEP: Traffic Consolidation for Energy-Proportional High-Radix Networks , 2018, 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA).

[109]  Silvio Savarese,et al.  MEVBench: A mobile computer vision benchmarking suite , 2011, 2011 IEEE International Symposium on Workload Characterization (IISWC).

[110]  Yasushi Inoguchi,et al.  Performance evaluation of a Green Scheduling Algorithm for energy savings in Cloud computing , 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW).

[111]  L. Kazmerski,et al.  Thin‐film CuInSe2/CdS heterojunction solar cells , 1976 .

[112]  Hung Cao,et al.  Developing an edge computing platform for real-time descriptive analytics , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[113]  Xin Zhou,et al.  Toward Computation Offloading in Edge Computing: A Survey , 2019, IEEE Access.

[114]  Khaled Ben Letaief,et al.  Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices , 2016, IEEE Journal on Selected Areas in Communications.

[115]  Michael D. Howard,et al.  HBA Vision Architecture: Built and Benchmarked , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[116]  Haijian Sun,et al.  Joint Offloading and Computation Energy Efficiency Maximization in a Mobile Edge Computing System , 2019, IEEE Transactions on Vehicular Technology.

[117]  Haichen Shen,et al.  TVM: An Automated End-to-End Optimizing Compiler for Deep Learning , 2018, OSDI.

[118]  Patrick Crowley,et al.  Named data networking , 2014, CCRV.

[119]  Erwan Nogues,et al.  Low power HEVC software decoder for mobile devices , 2015, Journal of Real-Time Image Processing.

[120]  Raphaël Couturier,et al.  An energy efficient IoT data compression approach for edge machine learning , 2019, Future Gener. Comput. Syst..

[121]  Xavier Masip-Bruin,et al.  Managing resources continuity from the edge to the cloud: Architecture and performance , 2018, Future Gener. Comput. Syst..

[122]  L. Kazmerski,et al.  Growth and characterization of thin‐film compound semiconductor photovoltaic heterojunctions , 1977 .

[123]  Jong Min Kim,et al.  Power Adaptive Data Encryption for Energy-Efficient and Secure Communication in Solar-Powered Wireless Sensor Networks , 2016, J. Sensors.

[124]  Ralf Stetter,et al.  Towards Robust Predictive Fault–Tolerant Control for a Battery Assembly System , 2015 .

[125]  Naga K. Govindaraju,et al.  GPGPU: general-purpose computation on graphics hardware , 2006, SC.

[126]  Claudia Linnhoff-Popien,et al.  Mobile Edge Computing , 2016, Informatik-Spektrum.

[127]  Thomas C. Schmidt,et al.  Information centric networking in the IoT: experiments with NDN in the wild , 2014, ICN '14.

[128]  John L. Henning SPEC CPU2006 benchmark descriptions , 2006, CARN.

[129]  T. D. Lee,et al.  A review of thin film solar cell technologies and challenges , 2017 .

[130]  Ying Jun Zhang,et al.  Computation Rate Maximization for Wireless Powered Mobile-Edge Computing With Binary Computation Offloading , 2017, IEEE Transactions on Wireless Communications.

[131]  Bastien Confais,et al.  An Object Store Service for a Fog/Edge Computing Infrastructure Based on IPFS and a Scale-Out NAS , 2017, 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC).

[132]  Gongbin Qian,et al.  Energy Efficient Power Allocation Based on Machine Learning Generated Clusters for Distributed Antenna Systems , 2019, IEEE Access.

[133]  Jim Gao,et al.  Machine Learning Applications for Data Center Optimization , 2014 .

[134]  Xiongwen Zhao,et al.  3D MIMO for 5G NR: Several Observations from 32 to Massive 256 Antennas Based on Channel Measurement , 2018, IEEE Communications Magazine.

[135]  Weisong Shi,et al.  EdgeOS_H: A Home Operating System for Internet of Everything , 2017, ICDCS 2017.

[136]  Guangjie Han,et al.  Hybrid-LRU Caching for Optimizing Data Storage and Retrieval in Edge Computing-Based Wearable Sensors , 2019, IEEE Internet of Things Journal.

[137]  Jianfeng Ma,et al.  Trustworthy service composition with secure data transmission in sensor networks , 2017, World Wide Web.

[138]  Lingjia Tang,et al.  The Architectural Implications of Autonomous Driving: Constraints and Acceleration , 2018, ASPLOS.

[139]  Lei Yang,et al.  HAPPE: Human and Application-Driven Frequency Scaling for Processor Power Efficiency , 2013, IEEE Transactions on Mobile Computing.

[140]  Lilian C. Freitas,et al.  SensorBus: a middleware model for wireless sensor networks , 2005, LANC '05.

[141]  Jing Wang,et al.  In-Situ AI: Towards Autonomous and Incremental Deep Learning for IoT Systems , 2018, 2018 IEEE International Symposium on High Performance Computer Architecture (HPCA).

[142]  Arno Luppold,et al.  Measuring and Modeling Energy Consumption of Embedded Systems for Optimizing Compilers , 2018, SCOPES.

[143]  Abhishek Chandra,et al.  Nebula: Distributed Edge Cloud for Data Intensive Computing , 2014, 2014 IEEE International Conference on Cloud Engineering.

[144]  Zhuo Chen,et al.  Edge Analytics in the Internet of Things , 2015, IEEE Pervasive Computing.

[145]  Paolo Bellavista,et al.  Feasibility of Fog Computing Deployment based on Docker Containerization over RaspberryPi , 2017, ICDCN.

[146]  Kin K. Leung,et al.  Dynamic service migration in mobile edge-clouds , 2015, 2015 IFIP Networking Conference (IFIP Networking).

[147]  Honghao Gao,et al.  An Edge Computing Platform for Intelligent Operational Monitoring in Internet Data Centers , 2019, IEEE Access.

[148]  Sannasi Ganapathy,et al.  Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT , 2019, Comput. Networks.

[149]  Guangjie Han,et al.  Partial offloading strategy for mobile edge computing considering mixed overhead of time and energy , 2019, Neural Computing and Applications.

[150]  Keqiu Li,et al.  Performance Guaranteed Computation Offloading for Mobile-Edge Cloud Computing , 2017, IEEE Wireless Communications Letters.

[151]  Rizwana Begum,et al.  Energy-Performance Trade-offs on Energy-Constrained Devices with Multi-component DVFS , 2015, 2015 IEEE International Symposium on Workload Characterization.

[152]  Zdenek Becvar,et al.  Mobile Edge Computing: A Survey on Architecture and Computation Offloading , 2017, IEEE Communications Surveys & Tutorials.

[153]  Stefano Secci,et al.  Cloud-based computation offloading for mobile devices: State of the art, challenges and opportunities , 2013, 2013 Future Network & Mobile Summit.

[154]  James C. Hoe,et al.  Single-Chip Heterogeneous Computing: Does the Future Include Custom Logic, FPGAs, and GPGPUs? , 2010, 2010 43rd Annual IEEE/ACM International Symposium on Microarchitecture.

[155]  Tulika Mitra,et al.  OPTiC: Optimizing Collaborative CPU–GPU Computing on Mobile Devices With Thermal Constraints , 2019, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[156]  Ra Inta,et al.  The "Chimera": An Off-The-Shelf CPU/GPGPU/FPGA Hybrid Computing Platform , 2012, Int. J. Reconfigurable Comput..

[157]  Xavier Masip-Bruin,et al.  Foggy clouds and cloudy fogs: a real need for coordinated management of fog-to-cloud computing systems , 2016, IEEE Wireless Communications.

[158]  Claus Pahl,et al.  Containers and Clusters for Edge Cloud Architectures -- A Technology Review , 2015, 2015 3rd International Conference on Future Internet of Things and Cloud.

[159]  Henri Casanova,et al.  Using Replication for Resilience on Exascale Systems , 2015 .

[160]  Kin K. Leung,et al.  Energy-Efficient Radio Resource Allocation for Federated Edge Learning , 2019, 2020 IEEE International Conference on Communications Workshops (ICC Workshops).

[161]  Kaushik Roy,et al.  Integrated Systems in the More-than-Moore Era: Designing Low-Cost Energy-Efficient Systems Using Heterogeneous Components , 2010, 2010 23rd International Conference on VLSI Design.

[162]  Peter Rosengren,et al.  A Development Platform for Integrating Wireless Devices and Sensors into Ambient Intelligence Systems , 2009, 2009 6th IEEE Annual Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks Workshops.

[163]  Bukhary Ikhwan Ismail,et al.  Evaluation of Docker as Edge computing platform , 2015, 2015 IEEE Conference on Open Systems (ICOS).

[164]  Arun Kumar Sangaiah,et al.  An Energy-Efficient Off-Loading Scheme for Low Latency in Collaborative Edge Computing , 2019, IEEE Access.

[165]  Schahram Dustdar,et al.  A Serverless Real-Time Data Analytics Platform for Edge Computing , 2017, IEEE Internet Computing.

[166]  Zhenyu Zhou,et al.  Energy-Efficient Edge Computing Service Provisioning for Vehicular Networks: A Consensus ADMM Approach , 2019, IEEE Transactions on Vehicular Technology.

[167]  Chunlong He,et al.  Power Allocation Schemes Based on Machine Learning for Distributed Antenna Systems , 2019, IEEE Access.

[168]  F. Richard Yu,et al.  Energy-efficient resource allocation in software-defined mobile networks with mobile edge computing and caching , 2017, 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[169]  Simon A. Dobson,et al.  Compression in wireless sensor networks , 2013 .

[170]  Eriko Nurvitadhi,et al.  Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC , 2016, 2016 International Conference on Field-Programmable Technology (FPT).

[171]  Arvind Shah,et al.  Complete microcrystalline p-i-n solar cell—Crystalline or amorphous cell behavior? , 1994 .

[172]  Ilyas Alper Karatepe,et al.  Big data caching for networking: moving from cloud to edge , 2016, IEEE Communications Magazine.

[173]  Qingyuan Deng,et al.  MemScale: active low-power modes for main memory , 2011, ASPLOS XVI.

[174]  Basem Shihada,et al.  Enhanced machine learning scheme for energy efficient resource allocation in 5G heterogeneous cloud radio access networks , 2017, 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[175]  Lei Yan,et al.  Energy-efficient and secure transmission scheme based on chaotic compressive sensing in underwater wireless sensor networks , 2018, Digit. Signal Process..

[176]  John D. Owens,et al.  GPU Computing , 2008, Proceedings of the IEEE.

[177]  Gabriel H. Loh,et al.  PIPP: promotion/insertion pseudo-partitioning of multi-core shared caches , 2009, ISCA '09.

[178]  Tao Zhang,et al.  MicroThings: A Generic IoT Architecture for Flexible Data Aggregation and Scalable Service Cooperation , 2017, IEEE Communications Magazine.

[179]  Weisong Shi,et al.  Energy efficiency comparison of hypervisors , 2019, Sustain. Comput. Informatics Syst..

[180]  Winfried Lamersdorf,et al.  CloudAware: A Context-Adaptive Middleware for Mobile Edge and Cloud Computing Applications , 2016, 2016 IEEE 1st International Workshops on Foundations and Applications of Self* Systems (FAS*W).

[181]  Ulrich Brunsmann,et al.  FPGA-GPU architecture for kernel SVM pedestrian detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[182]  Ying Wang,et al.  A Reinforcement Learning Approach to Energy Efficiency and QoS in 5G Wireless Networks , 2019, IEEE Journal on Selected Areas in Communications.

[183]  Zhendong Zhang,et al.  Real-time diagnosis of micro-short circuit for Li-ion batteries utilizing low-pass filters , 2019, Energy.

[184]  Xiaomin Zhu,et al.  MECCAS: Collaborative Storage Algorithm Based on Alternating Direction Method of Multipliers on Mobile Edge Cloud , 2017, 2017 IEEE International Conference on Edge Computing (EDGE).

[185]  Sherali Zeadally,et al.  Container-as-a-Service at the Edge: Trade-off between Energy Efficiency and Service Availability at Fog Nano Data Centers , 2017, IEEE Wireless Communications.

[186]  Ricardo Bianchini,et al.  DejaVu: accelerating resource allocation in virtualized environments , 2012, ASPLOS XVII.

[187]  Henry Hoffmann,et al.  Maximizing Performance Under a Power Cap: A Comparison of Hardware, Software, and Hybrid Techniques , 2016, ASPLOS.

[188]  James R. Larus,et al.  A reconfigurable fabric for accelerating large-scale datacenter services , 2014, 2014 ACM/IEEE 41st International Symposium on Computer Architecture (ISCA).

[189]  Jordi Torres,et al.  Towards energy-aware scheduling in data centers using machine learning , 2010, e-Energy.

[190]  Xiaohui Peng,et al.  EveryLite: A Lightweight Scripting Language for Micro Tasks in IoT Systems , 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC).

[191]  Jacques Demerjian,et al.  Using DWT Lifting Scheme for Lossless Data Compression in Wireless Body Sensor Networks , 2018, 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC).

[192]  Jerry M. Mendel,et al.  User-Satisfaction-Aware Power Management in Mobile Devices Based on Perceptual Computing , 2018, IEEE Transactions on Fuzzy Systems.

[193]  Hongjun Dai,et al.  A distributed multi-level model with dynamic replacement for the storage of smart edge computing , 2018, J. Syst. Archit..

[194]  Jorge G. Barbosa,et al.  A comparative cost analysis of fault-tolerance mechanisms for availability on the cloud , 2018, Sustain. Comput. Informatics Syst..