Treffer: 云边协同框架下视频处理任务实时调度算法.

In the video task processing of cloud-edge collaboration, due to the existence of a large number of processing and transmission tasks, it is necessary to consider the success rates of task processing and the processing time of tasks to ensure the quality of service. At the same time, various resource costs need to be taken into account to save system operation costs. To address the above issues, this paper formally models the video task scheduling problem under the cloud-edge collaborative framework and transforms it into a multi-objective optimization problem. For this problem, an algorithm called OCES is proposed. This algorithm sorts tasks within the same time slice to determine task priorities. For each task, it combines task information with the current status information of each edge node and cloud center node, and uses a neural network to judge and select the strategy with the maximum Q-value for scheduling, so as to specify the specific execution node of the task. OCES is an algorithm based on DDQN, which improves the reward function and strategy selection method. By integrating a noise network into the deep neural network, it avoids the algorithm from converging to a local optimal solution prematurely. Compared with the current internationally advanced CPSA algorithm, the proposed algorithm reduces the execution cost by 10.56% and 5.85% respectively in two scenarios with different average arrival rates and different task types, while achieving similar success rates and completion times. [ABSTRACT FROM AUTHOR]

在云边协同的视频任务处理中,由于存在大量的处理和传输任务,需要考虑任务处理的成功 率、任务的处理时间,以保证服务质量。同时,还需要考虑各种资源开销以节省系统运营成本。为了解决 上述难题,对云边协同框架下的视频任务调度问题进行了形式化建模,将问题转化为多目标优化问题。针 对上述问题,提出了OCES算法,以权衡任务的时延与其在不同节点上产生的开销,并适应不同的动态场 景。该算法对相同时间片内的任务进行排序以确定任务优先级,对于每个任务,结合任务信息与当前各边 缘节点、云中心节点的状态信息,通过神经网络判断选取Q 值最大策略的方法进行调度,用于指定任务的 具体执行节点。OCES是基于DDQN 的算法,对奖励函数和策略选择方法进行了改进,通过在深度神经 网络中结合噪声网络,避免算法过早收敛于局部最优解。相比目前国际先进的CPSA 算法,所提出的算 法在成功率与完成时间相近的情况下,执行开销在不同平均到达速率与不同任务类型比例的2个场景中 分别降低了10.56% 与5.85%。 [ABSTRACT FROM AUTHOR]