Joint Task Offloading and Resource Allocation in Heterogeneous Edge Environments
Joint Task Offloading and Resource Allocation in Heterogeneous Edge Environments
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DOI：
10.1109/infocom53939.2023.10229015
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Yu Liu;Yingling Mao;Z. Liu;Fan Ye;Yuanyuan Yang
中科院分区：
文献类型：
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作者：
Yu Liu;Yingling Mao;Z. Liu;Fan Ye;Yuanyuan Yang
移动的边缘计算正在成为无处不在的计算范例之一，以支持需要低延迟和高计算能力的应用。与通用服务器相比，基于FPGA的可重构加速器具有高能效和低延迟。因此，在移动的边缘计算系统中结合可重新配置的加速器是很自然的。本文阐述和研究的问题，联合任务卸载，接入点的选择，并在异构边缘环境中的延迟最小化的资源分配。由于边缘计算设备的异构性以及卸载、接入点选择和资源分配决策之间的耦合，同时优化它们是一项挑战。我们将所提出的问题分解成两个不相交的子问题，并为他们开发算法。第一个子问题是共同确定卸载和计算资源分配的决定，是NP-难的，我们开发了一个算法的基础上半定松弛。第二子问题是联合确定接入点选择和通信资源分配决策，其中我们提出了一个算法，可证明的近似比为2.62。我们进行了广泛的数值模拟，以评估所提出的算法。结果强调，所提出的算法优于基线，并在广泛的设置接近最佳。
Mobile edge computing is becoming one of the ubiquitous computing paradigms to support applications requiring low latency and high computing capability. FPGA-based reconfigurable accelerators have high energy efficiency and low latency compared to general-purpose servers. Therefore, it is natural to incorporate reconfigurable accelerators in mobile edge computing systems. This paper formulates and studies the problem of joint task offloading, access point selection, and resource allocation in heterogeneous edge environments for latency minimization. Due to the heterogeneity in edge computing devices and the coupling between offloading, access point selection, and resource allocation decisions, it is challenging to optimize over them simultaneously. We decomposed the proposed problem into two disjoint subproblems and developed algorithms for them. The first subproblem is to jointly determine offloading and computing resource allocation decisions and is NP-hard, where we developed an algorithm based on semidefinite relaxation. The second subproblem is to jointly determine access point selection and communication resource allocation decisions, where we proposed an algorithm with a provable approximation ratio of 2.62. We conducted extensive numerical simulations to evaluate the proposed algorithms. Results highlighted that the proposed algorithms outperformed baselines and were near-optimal over a wide range of settings.