Providing an improved greedy approach for increasing the number of Served users in cloud-edge networks

Document Type : Original Article

Authors

Department of Computer Engineering, Islamic Azad University, Sanandaj Branch, Sanandaj, Iran.

Abstract

One of the most significant challenges in edge computing is increasing the number of served users without changing the complexity of the problem and imposing further delays on cloud-edge network services. In edge networks, every user initially requests the desired service from their edge server, which edges provide it. If the requested service is not available in the cloud-edge network, the inquiry is made from the adjacent edge server based on the distance. The nearest edge clouds that can connect to it wirelessly provide the desired service. If unsuccessful, requests are forwarded to the backhaul link and other edge servers are searched for the desired service.  Finally, if all else fails, the service is obtained from the cloud. Due to the complexity examined, the strategy proposed in the paper provides the conditions to increase the number of served users without changing the service delay. The proposed algorithm improves served users by 0.4% and 1.1% over the greedy method and maximum flow method, respectively. Furthermore, it demonstrates a 20% and 22% improvement over the maximum flow and greedy methods, respectively, in users' scheduling.

Keywords


[1] Borcoci E., Vochin M., and Obreja S., "Mobile Edge Computing versus Fog Computing in Internet of Vehicles," in research gate, Bucharest, Romania, 2018.
[2] Chunlin L., Sun H., Yi C., and Luo Y., "Edge cloud resource expansion and shrinkage based on workload for minimizing the cost," Future Generation Computer Systems, 101:327-340, 2019. 
[3] Li X. and Xu L. D., "A Review of Internet of Things-Resource Allocation," IEEE Internet of Things Journal, 8(11), 2021. 
[4] Jia M., Cao J., and Liang W., "Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks," IEEE Transactions on Cloud Computing, 5(4):725-737, 2017. 
[5] Xu Z., Liang W., Xu W., Jia M., and Guo S., "Efficient algorithms for capacitated cloudlet placements," IEEE Transactions on Parallel and Distributed Systems, 27: 2866–2880, 2016. 
[6] Ceselli A., Premoli M., and Secci S., "Mobile edge cloud network design optimization," IEEE/ACM Transactions on Networking, 25:1818–1831, 2017. 
[7] Wang L., Jiao L., Li J., and Muhlhauser M., "Online resource allocation for arbitrary user mobility in distributed edge clouds," in 37th International Conference on Distributed Computing Systems (ICDCS), 2017. 
[8] Tan H., Han Z., Li X.-Y., and Lau F. C. M., "Online job dispatching and scheduling in edge-clouds," in IEEE INFOCOM, 2017. 
[9] Sabaghian K., Khamforoosh K., and Ghaderzadeh A., "Presentation of a new method based on modern multivariate approaches for big data replication in distributed environments," Plos one 16, no. 7 (2021): e0254210.
[10] Llorca J., Tulino A. M., Sforza A., and Sterle C., "Optimal content distribution and multi-resource allocation in software defined virtual," in AIRO ODS, 2017. 
[11] Zhao T., Zhou S., Guo X., and Niu Z., "Tasks Scheduling and Resource Allocation in Heterogeneous Cloud for Delay-bounded Mobile Edge Computing," in IEEE ICC 2017 SAC Symposium Cloud Communications and Networking Track, 2017. 
[12] Chen X., Li W., Lu S., Zhou Z., and Fu X., "Efficient Resource Allocation for On-Demand Mobile-Edge Cloud Computing," IEEE Transactions on Vehicular Technology , 67(9): 8769 - 8780, 2018. 
[13] He T., Khamfroush H., Wang S., La Porta T., and Stein S., "Joint Service Placement and Request Scheduling in Edge Clouds with Sharable and Non-Sharable Resources," in 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), Vienna, Austria, 2018. 
[14] Farhadi V., Mehmeti F., He T., La Porta T., Khamfroush H., Wang S., and Chan K. S., "Service Placement and Request Scheduling for Data-Intensive Applications in Edge Clouds," in IEEE INFOCOM 2019 - IEEE Conference on Computer Communications, Paris, 2019. 
[15] Zhao J., Li Q., Gong Y., and Zhang K., "Computation Offloading and Resource Allocation For Cloud Assisted Mobile Edge Computing in Vehicular Networks," IEEE Transactions on Vehicular Technology, 68: 7944 - 7956, 2019. 
[16] Li Y., Dai W., Gan X., Jin H., Fu L., Ma H., and Wang X., "Cooperative Service Placement and Scheduling in Edge Clouds: A Deadline-Driven Approach," IEEE Transactions on Mobile Computing, p. 1, 2021. 
[17] Han T. and Ansari N., "Network Utility Aware Traffic Load Balancing in Backhaul-Constrained Cache-Enabled Small Cell Networks with Hybrid Power Supplies," IEEE Transactions on Mobile Computing, 16(10): 2819 - 2832, 2017. 
[18] Wei Y., Zhang  Z., Yu F. R., and Han Z., "Joint User Scheduling and Content Caching Strategy for Mobile Edge Networks Using Deep Reinforcement Learning," in IEEE International Conference on Communications Workshops, Kansas City, MO, USA, 2018. 
[19] Li C., Bai J., and Tang J. H., "Joint optimization of data placement and scheduling for improving user experience in edge computing," Journal of Parallel and Distributed Computing, 125:93-105, 2020. 
[20] Yin B., Chen Z., and Tao M., "Joint User Scheduling and Resource Allocation for Federated Learning over Wireless Networks," in IEEE Global Communications Conference, 2020.
[21] Wang S., Chen M., Liu X., and Yin C., "A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks," IEEE Internet of Things Journal, 8(3): 1358-1372, 2020. 
[22] Wang J. and Wang L., "A Computing Resource Allocation Optimization Strategy for Massive Internet of Health Things Devices Considering Privacy Protection in Cloud Edge Computing Environment," Journal of Grid Computing, vol. 19, 2021. 
[23] Molner N., de la Oliva A., Stavrakakis I., and Azcorra A., "Optimization of an integrated fronthaul/backhaul network under path and delay constraints," Ad Hoc Networks, 83: 41-54, 2019.
[24] Korte B. and Vygen J., "Network flows," Combinatorial Optimization, 8: 153-184, 2000.
[25] Wang J., Hu J., Min G., Zhan W., Zomaya A., and Georgalas N., "Dependent Task Offloading for Edge Computing based on Deep Reinforcement Learning," in IEEE Transactions on Computers, 2021, doi: 10.1109/TC.2021.3131040.