Cache Oriented Migration Decision and Resource Allocation in Edge Computing

YANG Shouyi; HAN Haojin; HAO Wanming; CHEN Yihang

doi:10.11999/JEIT240427

Volume 46 Issue 12

Dec. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(12): 4391-4398

YANG Shouyi, HAN Haojin, HAO Wanming, CHEN Yihang. Cache Oriented Migration Decision and Resource Allocation in Edge Computing[J]. Journal of Electronics & Information Technology, 2024, 46(12): 4391-4398. doi: 10.11999/JEIT240427

Citation:

YANG Shouyi, HAN Haojin, HAO Wanming, CHEN Yihang. Cache Oriented Migration Decision and Resource Allocation in Edge Computing[J]. Journal of Electronics & Information Technology, 2024, 46(12): 4391-4398. doi: 10.11999/JEIT240427

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Cache Oriented Migration Decision and Resource Allocation in Edge Computing

doi: 10.11999/JEIT240427 cstr: 32379.14.JEIT240427

School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China

Funds: The National Natural Science Foundation of China (U1604159)

Received Date: 2024-05-29
Rev Recd Date: 2024-11-07

Available Online: 2024-11-12

Publish Date: 2024-12-01

Abstract

Abstract

Edge computing provides computing resources and caching services at the network edge, effectively reducing execution latency and energy consumption. However, due to user mobility and network randomness, caching services and user tasks frequently migrate between edge servers, increasing system costs. The migration computation model based on pre-caching is constructed and the joint optimization problem of resource allocation, service caching and migration decision-making is investigated. To address this mixed-integer nonlinear programming problem, the original problem is decomposed to optimize the resource allocation using Karush-Kuhn-Tucker condition and bisection search iterative method. Additionally, a Joint optimization algorithm for Migration decision-making and Service caching based on a Greedy Strategy (JMSGS) is proposed to obtain the optimal migration and caching decisions. Simulation results show the effectiveness of the proposed algorithm in minimizing the weighted sum of system energy consumption and latency.
- Edge computing,
- Migrate strategy,
- Service cache,
- Resource allocation

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References(20)

References

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