Social Attribute Aware Task Scheduling Strategy in Edge Computing
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摘要:
边缘计算服务器的负载不均衡将严重影响服务能力，该文提出一种适用于边缘计算场景的任务调度策略(RQ-AIP)。首先，根据服务器的负载分布情况衡量整个网络的负载均衡度，结合强化学习方法为任务匹配合适的边缘服务器，以满足传感器节点任务的资源差异化需求；进而，构造任务时延和终端发射功率的映射关系来满足物理域的约束，结合终端用户社会属性，为任务不断地选择合适的中继终端，通过终端辅助调度的方式实现网络的负载均衡。仿真结果表明，所提出的策略与其他负载均衡策略相比能有效地缓解边缘服务器之间的负载和核心网的流量，降低任务处理时延。
Abstract:Unbalanced load on the edge computing server will seriously affect service capabilities, a task scheduling strategy Reinforced Q-learning-Automatic Intent Picking (RQ-AIP) for edge computing scenarios is proposed. Firstly, the load balance of the entire network is measured based on the load distribution of the server. By combining the reinforcement learning method, the appropriate edge server is matched for the task to meet the resource differentiation needs of sensor node tasks. Then, a mapping relationship between task delay and terminal transmit power is constructed to satisfy the constraints of the physical domain. Combining the social attributes of terminal, the appropriate relay terminal is continuously selected for the task to achieve the load balancing of network by terminal-assisted scheduling. Simulation results show that compared with other load balancing strategies, the proposed strategy can effectively alleviate the load between the edge servers and the traffic of the core network, reduce task processing latency.
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Key words:
- Computer network /
- Edge computing /
- Social attribute /
- Load balancing
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表 1 仿真参数设置
参数设定 参数数值 任务到达率(个/s) [0, 4] 任务所需内存(GB) [1, 10] 任务所需CPU周期(MHz) 50 任务时延(s) [200, 1500] 边缘服务器CPU频率(GHz) 3 无线信道带宽(MHz) 5 边缘服务器数量(个) 5 学习因子 0.5 终端发射功率(W) [0.1, 2] 噪声功率(dBm/Hz) –170 
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