Energy-Aware Online Task Offloading and Resource Allocation for Mobile Edge Computing
Energy-Aware Online Task Offloading and Resource Allocation for Mobile Edge Computing
复制标题
DOI：
10.1109/icdcs57875.2023.00073
复制
发表时间：
2023-07
期刊：
影响因子：
--
通讯作者：
Yu Liu;Yingling Mao;Xiaojun Shang;Z. Liu;Yuanyuan Yang
中科院分区：
文献类型：
--
作者：
Yu Liu;Yingling Mao;Xiaojun Shang;Z. Liu;Yuanyuan Yang
具有近数据处理范式的移动的边缘计算可以支持需要低延迟和高计算能力的应用，其中能源成本是支出的重要部分。针对移动的边缘计算系统，提出了一种基于时间平均能量成本约束的在线联合任务卸载和资源分配问题.公式化的问题具有四个时变系统状态，即，数据长度、任务大小、信道条件和电价，这些都是基于真实世界的数据建模的。在每个时隙开始时，系统必须联合做出五个在线决策：基站选择、用于任务卸载的服务器选择、通信带宽分配、计算资源分配和频率缩放。我们证明了离线版本的制定的问题是NP难的。我们设计了一个在线算法，具有可证明的近似比和低计算复杂度的建议的问题。特别是，它平衡的漂移加惩罚算法的基础上的能源成本和延迟，并使用基于博弈论的算法，使服务器和基站的选择决定。我们进行了广泛的现实世界的数据驱动的模拟，以评估所提出的算法。仿真结果表明，该方法优于流行的基线。
Mobile edge computing with the near-data processing paradigm can support applications requiring low latency and high computing capability, where energy cost is a significant part of the expenditure. This paper formulates and studies the problem of online joint task offloading and resource allocation for latency minimization subjecting to a time average energy cost constraint in mobile edge computing systems. The formulated problem has four time-variant system states, i.e., data lengths, task sizes, channel conditions, and electricity prices, which are modeled based on real-world data. At the beginning of each time slot, the system has to make five online decisions jointly: base station selection, server selection for task offloading, communication bandwidth allocation, computing resource allocation, and frequency scaling. We prove the offline version of the formulated problem is NP-hard. We design an online algorithm with a provable approximation ratio and low computational complexity for the proposed problem. In particular, it balances energy cost and latency based on the drift-plus-penalty algorithm and makes server and base station selection decisions using a game theoretic-based algorithm. We conduct extensive real-world data-driven simulations to evaluate the proposed algorithm. Simulation results show that the proposed approach outperforms popular baselines.