Heterogeneity-aware elastic provisioning in cloud-assisted edge computing systems

Abstract Edge computing is the provision of cloud services and IT environment services to application developers and service providers on the edge of the network. Edge computing faces some challenges, such as dealing with randomly varying workloads, which is an important issue. Thus, a cloud-assisted edge computing system (CAECS) is studied. A replica placement strategy is proposed to satisfy the diversity of user demands and reduce the response time. A data migration strategy is proposed to guarantee data reliability if there exist the released instances. A heterogeneity-aware elastic provisioning strategy is proposed to rent the cloud instances. Finally, the performance of the proposed algorithms is evaluated via extensive experiments. The results imply that the total tenanted cost of the heterogeneity-aware elastic provisioning algorithm can averagely achieve up to 19.23% and 9.50% reduction over that of ARP algorithm and MADRP algorithm, respectively.

[1]  Athanasios V. Vasilakos,et al.  Energy-Efficient Provisioning for Service Function Chains to Support Delay-Sensitive Applications in Network Function Virtualization , 2020, IEEE Internet of Things Journal.

[2]  Diego López-de-Ipiña,et al.  Interactive live-streaming technologies and approaches for web-based applications , 2018, Multimedia Tools and Applications.

[3]  Athanasios V. Vasilakos,et al.  An effective service-oriented networking management architecture for 5G-enabled internet of things , 2020, Comput. Networks.

[4]  Jiannong Cao,et al.  Joint Computation Offloading and Bandwidth Assignment in Cloud-Assisted Edge Computing , 2019, IEEE Transactions on Cloud Computing.

[5]  Mirto Musci,et al.  Deep Recurrent Neural Networks for Edge Monitoring of Personal Risk and Warning Situations , 2019, Sci. Program..

[6]  Athanasios V. Vasilakos,et al.  Mobile Cloud Computing: A Survey, State of Art and Future Directions , 2013, Mobile Networks and Applications.

[7]  Athanasios V. Vasilakos,et al.  An Advanced MapReduce: Cloud MapReduce, Enhancements and Applications , 2014, IEEE Transactions on Network and Service Management.

[8]  Athanasios V. Vasilakos,et al.  A Framework for Truthful Online Auctions in Cloud Computing with Heterogeneous User Demands , 2016, IEEE Trans. Computers.

[9]  Jun Yang,et al.  Artificial agent: The fusion of artificial intelligence and a mobile agent for energy-efficient traffic control in wireless sensor networks , 2019, Future Gener. Comput. Syst..

[10]  Aiman Erbad,et al.  Edge computing for interactive media and video streaming , 2017, 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC).

[11]  Murugaboopathi Gurusamy,et al.  Load monitoring and system-traffic-aware live VM migration-based load balancing in cloud data center using graph theoretic solutions , 2018, Cluster Computing.

[12]  Mahmoud Al-Ayyoub,et al.  Towards improving resource management in cloud systems using a multi-agent framework , 2016, Int. J. Cloud Comput..

[13]  Athanasios V. Vasilakos,et al.  MAPCloud: Mobile Applications on an Elastic and Scalable 2-Tier Cloud Architecture , 2012, 2012 IEEE Fifth International Conference on Utility and Cloud Computing.

[14]  Athanasios V. Vasilakos,et al.  Managing Performance Overhead of Virtual Machines in Cloud Computing: A Survey, State of the Art, and Future Directions , 2014, Proceedings of the IEEE.

[15]  Mohamed Faten Zhani,et al.  Efficient Replica Migration Scheme for Distributed Cloud Storage Systems , 2018, IEEE Transactions on Cloud Computing.

[16]  Ke Zhang,et al.  Computation Offloading and Resource Allocation For Cloud Assisted Mobile Edge Computing in Vehicular Networks , 2019, IEEE Transactions on Vehicular Technology.

[17]  Haruhisa Hasegawa,et al.  Elevator Monitoring System to Guide User's Behavior by Visualizing the State of Crowdedness , 2019, BCD.

[18]  Hongming Cai,et al.  A short-term energy prediction system based on edge computing for smart city , 2019, Future Gener. Comput. Syst..

[19]  Athanasios V. Vasilakos,et al.  A Survey on Service-Oriented Network Virtualization Toward Convergence of Networking and Cloud Computing , 2012, IEEE Transactions on Network and Service Management.

[20]  Giuseppe M. L. Sarnè,et al.  Multi-agent technology and ontologies to support personalization in B2C E-Commerce , 2014, Electron. Commer. Res. Appl..

[21]  Zhang Jian Wei,et al.  On-demand physical resource allocation method for cloud virtual machine to support random service requests , 2017 .

[22]  Laxmi N. Bhuyan,et al.  Maintaining Data Consistency in Structured P2P Systems , 2012, IEEE Transactions on Parallel and Distributed Systems.

[23]  Athanasios V. Vasilakos,et al.  A Framework for Truthful Online Auctions in Cloud Computing with Heterogeneous User Demands , 2016, IEEE Transactions on Computers.

[24]  Athanasios V. Vasilakos,et al.  GreenDCN: A General Framework for Achieving Energy Efficiency in Data Center Networks , 2013, IEEE Journal on Selected Areas in Communications.

[25]  Athanasios V. Vasilakos,et al.  On Optimal and Fair Service Allocation in Mobile Cloud Computing , 2013, IEEE Transactions on Cloud Computing.

[26]  Jun Zhang,et al.  Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches , 2015, ACM Comput. Surv..

[27]  Chen Wang,et al.  Dynamic Request Redirection and Resource Provisioning for Cloud-Based Video Services under Heterogeneous Environment , 2016, IEEE Transactions on Parallel and Distributed Systems.

[28]  Jagruti Sahoo,et al.  A Survey on Replica Server Placement Algorithms for Content Delivery Networks , 2016, IEEE Communications Surveys & Tutorials.

[29]  Ivan Merelli,et al.  Combining Edge and Cloud computing for low-power, cost-effective metagenomics analysis , 2019, Future Gener. Comput. Syst..

[30]  Rajkumar Buyya,et al.  Workload Prediction Using ARIMA Model and Its Impact on Cloud Applications’ QoS , 2015, IEEE Transactions on Cloud Computing.

[31]  Wenbin Yao,et al.  DARS: A dynamic adaptive replica strategy under high load Cloud-P2P , 2018, Future Gener. Comput. Syst..

[32]  Long Hu,et al.  iRobot-Factory: An intelligent robot factory based on cognitive manufacturing and edge computing , 2019, Future Gener. Comput. Syst..

[33]  Jinho Hwang,et al.  Toward Beneficial Transformation of Enterprise Workloads to Hybrid Clouds , 2016, IEEE Transactions on Network and Service Management.

[34]  Athanasios V. Vasilakos,et al.  MuSIC: Mobility-Aware Optimal Service Allocation in Mobile Cloud Computing , 2013, 2013 IEEE Sixth International Conference on Cloud Computing.

[35]  Brunilde Sansò,et al.  A Tabu Search Algorithm for the Location of Data Centers and Software Components in Green Cloud Computing Networks , 2013, IEEE Transactions on Cloud Computing.

[36]  Sheng Wang,et al.  Joint VM placement and topology optimization for traffic scalability in dynamic datacenter networks , 2015, Comput. Networks.

[37]  Athanasios V. Vasilakos,et al.  Joint virtual machine assignment and traffic engineering for green data center networks , 2014, PERV.

[38]  Xiuqi Li,et al.  Virtual machine consolidated placement based on multi-objective biogeography-based optimization , 2016, Future Gener. Comput. Syst..

[39]  Youlong Luo,et al.  Collaborative cache allocation and task scheduling for data-intensive applications in edge computing environment , 2019, Future Gener. Comput. Syst..

[40]  Naixue Xiong,et al.  A game-theoretic method of fair resource allocation for cloud computing services , 2010, The Journal of Supercomputing.

[41]  Bo Cheng,et al.  A cost-aware auto-scaling approach using the workload prediction in service clouds , 2014, Inf. Syst. Frontiers.

[42]  Athanasios V. Vasilakos,et al.  Energy-efficient and traffic-aware service function chaining orchestration in multi-domain networks , 2019, Future Gener. Comput. Syst..