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An Efficient Service-Aware Virtual Machine Scheduling Approach Based on Multi-Objective Evolutionary Algorithm
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AI
DOI：10.1109/TSC.2023.3341842.png)
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Abstract
En 中文
Service providers tend to deploy application services to several different virtual machines (VMs) to improve the scalability and manageability of the cloud data center (CDC). Therefore, high frequency communication traffic is always involved among those VMs that are deployed the same application service. In order to reduce the communication cost (CC) of CDC, all VMs running the same service should be redeployed in the same subnet as much as possible by using live migration technology, because CC between VMs in different subnets is much higher than that within the same subnet. On the other hand, the migration time (MT) to complete all migration tasks is also crucial for providers and customers, because a prolonged MT will lead to the increased maintenance cost and the deterioration of quality of service (QoS). To address the aforementioned issues, this paper proposes an efficient service-aware VM scheduling approach (ES-VSA) based on a multi-objective evolutionary algorithm to minimize CC and MT, simultaneously. Finally, experiments are conducted on four different scenarios, and the simulation results demonstrate that our proposed algorithm is superior to several state-of-the-art algorithms in terms of both CC and MT.
Keywords:
Costs
Optimization
Virtual machining
Evolutionary computation
Cloud computing
Heuristic algorithms
Scalability
multi-objective optimization
evolutionary algorithm
virtual machine live migration
communication cost
migration time
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