DRA‐MQoS : an MQoS scheduling algorithm based on resource feature matching in federated edge cloud - Institut Polytechnique de Paris Access content directly
Journal Articles Concurrency and Computation: Practice and Experience Year : 2023

DRA‐MQoS : an MQoS scheduling algorithm based on resource feature matching in federated edge cloud

Abstract

Federated edge cloud (FEC) is an edge computing environment where servers in the same edge management domain could collaborate to handle latency-sensitive services, thus better guaranteeing users' requirements on multiple quality of service (MQoS). Traditional scheduling methods only consider whether the server meets the resource requirements of the service, without paying attention to whether their resource characteristics match. In scenarios where server's resources are dynamically changing, this may reduce the resource utilization and the efficiency of service execution. To address this challenge, a dynamic resource adaptation-multiple quality of service (DRA-MQoS) algorithm is proposed for service scheduling in this environment. DRA-MQoS could dynamically evaluate the resource characteristics of servers and services from the perspectives of “individual” and “overall” by combining the historical scheduling data of services and the utilization of different resources of server clusters. By scheduling the services to servers with the same resource characteristics for execution, the proposed policy fusion algorithm efficiently responds to the dynamically changing quality of service (QoS) demands of users by changing the weight parameters of policies. Simulation results in CloudSimSDN show that the energy consumption and execution time of DRA-MQoS are reduced by 23% and 12%, respectively, compared with existing methods.
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Dates and versions

hal-04021516 , version 1 (09-03-2023)

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Cite

Yujin Li, Bo Liu, Enju Wu, Jianqiang Li, Zhangbing Zhou, et al.. DRA‐MQoS : an MQoS scheduling algorithm based on resource feature matching in federated edge cloud. Concurrency and Computation: Practice and Experience, 2023, 35 (2), ⟨10.1002/cpe.7478⟩. ⟨hal-04021516⟩
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