Resource Management in Fog/Edge Computing

Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets, is an upcoming computing paradigm, referred to as fog/edge computing. Fog/edge resources are typically resource-constrained, heterogeneous, and dynamic compared to the cloud, thereby making resource management an important challenge that needs to be addressed. This article reviews publications as early as 1991, with 85% of the publications between 2013 and 2018, to identify and classify the architectures, infrastructure, and underlying algorithms for managing resources in fog/edge computing.

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[68]  Albert Y. Zomaya,et al.  Secure and Sustainable Load Balancing of Edge Data Centers in Fog Computing , 2018, IEEE Communications Magazine.

[69]  Ellen W. Zegura,et al.  Serendipity: enabling remote computing among intermittently connected mobile devices , 2012, MobiHoc '12.

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[71]  Weihua Zhuang,et al.  Software Defined Networking Enabled Wireless Network Virtualization: Challenges and Solutions , 2017, IEEE Network.

[72]  Jörg Ott,et al.  Consolidate IoT Edge Computing with Lightweight Virtualization , 2018, IEEE Network.

[73]  Chuan Wu,et al.  Aggregation Latency-Energy Tradeoff in Wireless Sensor Networks with Successive Interference Cancellation , 2013, IEEE Transactions on Parallel and Distributed Systems.

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[75]  Chuan Li,et al.  Enabling Campus Edge Computing Using GENI Racks and Mobile Resources , 2016, 2016 IEEE/ACM Symposium on Edge Computing (SEC).

[76]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[77]  H. T. Kung,et al.  Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).

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[81]  Xu Chen,et al.  When D2D meets cloud: Hybrid mobile task offloadings in fog computing , 2017, 2017 IEEE International Conference on Communications (ICC).

[82]  Pradipta De,et al.  Computation Offloading from Mobile Devices: Can Edge Devices Perform Better Than the Cloud? , 2016, ARMS-CC@PODC.

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[93]  Khaled A. Harras,et al.  Femto Clouds: Leveraging Mobile Devices to Provide Cloud Service at the Edge , 2015, 2015 IEEE 8th International Conference on Cloud Computing.

[94]  Flavio Esposito,et al.  Elastic urban video surveillance system using edge computing , 2017, SmartIoT@SEC.

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[99]  Blesson Varghese,et al.  Cloud Benchmarking for Performance , 2014, 2014 IEEE 6th International Conference on Cloud Computing Technology and Science.

[100]  Oriol Sallent,et al.  On Radio Access Network Slicing from a Radio Resource Management Perspective , 2017, IEEE Wireless Communications.

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[102]  Karsten Schwan,et al.  SOUL: An Edge-Cloud System for Mobile Applications in a Sensor-Rich World , 2016, 2016 IEEE/ACM Symposium on Edge Computing (SEC).

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[104]  Ada Gavrilovska,et al.  AppSachet: Distributed App Delivery from the Edge Cloud , 2015, MobiCASE.

[105]  Franco Callegati,et al.  Clouds of virtual machines in edge networks , 2013, IEEE Communications Magazine.

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[119]  Özgür B. Akan,et al.  Gravity gradient routing for information delivery in fog Wireless Sensor Networks , 2016, Ad Hoc Networks.

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[155]  Roberto Beraldi,et al.  Cooperative load balancing scheme for edge computing resources , 2017, 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC).

[156]  Xu Chen,et al.  Decentralized Computation Offloading Game for Mobile Cloud Computing , 2014, IEEE Transactions on Parallel and Distributed Systems.

[157]  Shudong Jin,et al.  Prediction or Not? An Energy-Efficient Framework for Clustering-Based Data Collection in Wireless Sensor Networks , 2011, IEEE Transactions on Parallel and Distributed Systems.

[158]  Khaled A. Harras,et al.  Towards resource sharing in mobile device clouds: power balancing across mobile devices , 2013, MCC '13.

[159]  Symeon Chatzinotas,et al.  Energy-efficient design for edge-caching wireless networks: When is coded-caching beneficial? , 2017, 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[160]  Rajkumar Buyya,et al.  Distributed data stream processing and edge computing: A survey on resource elasticity and future directions , 2017, J. Netw. Comput. Appl..

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[181]  Richard O. Sinnott,et al.  A performance comparison of container-based technologies for the Cloud , 2017, Future Gener. Comput. Syst..

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[188]  Steven J. Vaughan-Nichols,et al.  New Approach to Virtualization Is a Lightweight , 2006, Computer.

[189]  Kazuhiro Tokunaga,et al.  High-speed uploading architecture using distributed edge servers on multi-RAT heterogeneous networks , 2016, 2016 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN).

[190]  Cheol-Ho Hong,et al.  Heterogeneous Secure Multi-level Remote Acceleration Service for Low-power Integrated Systems and Devices , 2016, Cloud Forward.

[191]  Kin K. Leung,et al.  Dynamic Service Placement for Mobile Micro-Clouds with Predicted Future Costs , 2015, IEEE Transactions on Parallel and Distributed Systems.

[192]  Sandra Gesing,et al.  Enhancing the Usability and Utilization of Accelerated Architectures via Docker , 2015, 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC).

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