Joint Task Offloading and Channel Allocation in Spatial-Temporal Dynamic for MEC Networks

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Main Authors: Shi, Tianyi, Zhang, Tiankui, Loo, Jonathan, Huang, Rong, Wang, Yapeng
Format: Preprint
Published: 2025
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author Shi, Tianyi
Zhang, Tiankui
Loo, Jonathan
Huang, Rong
Wang, Yapeng
author_facet Shi, Tianyi
Zhang, Tiankui
Loo, Jonathan
Huang, Rong
Wang, Yapeng
contents Computation offloading and resource allocation are critical in mobile edge computing (MEC) systems to handle the massive and complex requirements of applications restricted by limited resources. In a multi-user multi-server MEC network, the mobility of terminals causes computing requests to be dynamically distributed in space. At the same time, the non-negligible dependencies among tasks in some specific applications impose temporal correlation constraints on the solution as well, leading the time-adjacent tasks to experience varying resource availability and competition from parallel counterparts. To address such dynamic spatial-temporal characteristics as a challenge in the allocation of communication and computation resources, we formulate a long-term delay-energy trade-off cost minimization problem in the view of jointly optimizing task offloading and resource allocation. We begin by designing a priority evaluation scheme to decouple task dependencies and then develop a grouped Knapsack problem for channel allocation considering the current data load and channel status. Afterward, in order to meet the rapid response needs of MEC systems, we exploit the double duel deep Q network (D3QN) to make offloading decisions and integrate channel allocation results into the reward as part of the dynamic environment feedback in D3QN, constituting the joint optimization of task offloading and channel allocation. Finally, comprehensive simulations demonstrate the performance of the proposed algorithm in the delay-energy trade-off cost and its adaptability for various applications.
format Preprint
id arxiv_https___arxiv_org_abs_2505_04272
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Joint Task Offloading and Channel Allocation in Spatial-Temporal Dynamic for MEC Networks
Shi, Tianyi
Zhang, Tiankui
Loo, Jonathan
Huang, Rong
Wang, Yapeng
Networking and Internet Architecture
Signal Processing
Computation offloading and resource allocation are critical in mobile edge computing (MEC) systems to handle the massive and complex requirements of applications restricted by limited resources. In a multi-user multi-server MEC network, the mobility of terminals causes computing requests to be dynamically distributed in space. At the same time, the non-negligible dependencies among tasks in some specific applications impose temporal correlation constraints on the solution as well, leading the time-adjacent tasks to experience varying resource availability and competition from parallel counterparts. To address such dynamic spatial-temporal characteristics as a challenge in the allocation of communication and computation resources, we formulate a long-term delay-energy trade-off cost minimization problem in the view of jointly optimizing task offloading and resource allocation. We begin by designing a priority evaluation scheme to decouple task dependencies and then develop a grouped Knapsack problem for channel allocation considering the current data load and channel status. Afterward, in order to meet the rapid response needs of MEC systems, we exploit the double duel deep Q network (D3QN) to make offloading decisions and integrate channel allocation results into the reward as part of the dynamic environment feedback in D3QN, constituting the joint optimization of task offloading and channel allocation. Finally, comprehensive simulations demonstrate the performance of the proposed algorithm in the delay-energy trade-off cost and its adaptability for various applications.
title Joint Task Offloading and Channel Allocation in Spatial-Temporal Dynamic for MEC Networks
topic Networking and Internet Architecture
Signal Processing
url https://arxiv.org/abs/2505.04272