期刊
KNOWLEDGE-BASED SYSTEMS
卷 217, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.knosys.2021.106831
关键词
Mobile edge computing; Edge service; Service instance selection; Genetic algorithm; Response time
资金
- National Key Research and Development Program of China [2017YFC0907505]
- Shanghai Natural Science Foundation, China [18ZR1414400]
- National Natural Science Foundation of China [61772128]
Mobile edge computing (MEC) aims to reduce response time for service invocations by deploying service instances on edge servers and selecting them based on user proximity. However, service instance selection faces challenges such as server limitations, user mobility, and request interference. A novel genetic algorithm-based approach called GASISMEC is proposed to tackle these challenges and outperforms six baseline approaches in extensive experiments.
Mobile edge computing (MEC) has been proposed to significantly reduce the response time of service invocations for end users. In MEC environment, a service provider can create multiple instances from a service and deploy them to different hired edge servers, where the deployed instances can be selected and invoked to decrease the network latency by nearby users. However, service instance selection in MEC is a challenging research problem from threefold aspects. First, the limitations of an edge server in terms of computation capacity and coverage range result in serving for only a certain number of users at the same time. Second, due to variable geographical locations from user mobility paths in MEC, the mobility of edge users is highly related to data transmission rate and affects the delay of service invocations. Furthermore, when many users in an edge server covered region request the same service instance at the same time, they interfere with each other and may reduce the experience of service invocations if there is no effective strategy to distribute these requests to appropriate instances deployed on different edge servers. To improve the user experience on service invocations with a lower response time, we take the above three factors into account and model the service instance selection problem (SISP) in MEC as an optimization problem, and propose a novel genetic algorithm-based approach with a response time-aware mutation operation with normalization for service instance selection called GASISMEC to find approximately optimal solution. Extensive experiments are conducted on two widely-used real-world datasets. The results demonstrate that our approach significantly outperforms the six baseline competing approaches. (C) 2021 Elsevier B.V. All rights reserved.
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