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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >A Game-Based Approach for Cost-Aware Task Assignment With QoS Constraint in Collaborative Edge and Cloud Environments
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A Game-Based Approach for Cost-Aware Task Assignment With QoS Constraint in Collaborative Edge and Cloud Environments

机译：基于游戏的成本感知任务分配与协作边缘和云环境中的QoS约束

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With the development of the Internet of Things, the data that needs to be processed is increasing rapidly. Therefore, the collaboration of cloud and edge emerges as the times require. Edge nodes are mainly responsible for collecting data, and decide to process the data locally or offload to cloud data centers. Cloud data centers are suitable for data analysis, model training, and managing edge nodes. In this article, we focus on the task assignment problems in collaborative edge and cloud environments and study it in a distributed, non-cooperative environment. An M/M/1 queueing model is established to characterize the task transmission. Because of the multi-core processors, we set an M/M/C queueing model to characterize the task computation. We consider the problem from the perspective of game theory and formulate it into a non-cooperative game among multi-agents (multiple edge data centers) in which each agent is informed with incomplete information (allocation strategies) of others. For each agent, we define a function of the expected cost of tasks as the disutility function, and minimize it subject to the QoS constraint. We analyze the existence of Nash equilibrium and develop a Greedy Energy-aware Algorithm (GEA) to choose active servers using the Limit Searching Algorithm (LSA) to find the ceiling utilization. Then we propose the Best Response Algorithm (BRA) to optimize the utility function. The convergence of the BRA algorithm has been discussed. Finally, the results demonstrate that the BRA algorithm can get a solution close to Nash equilibrium and reach it quickly.
机译：随着物联网的发展，需要处理的数据正在迅速增加。因此，云和边缘的协作随着时间的需求而出现。边缘节点主要负责收集数据，并决定在本地处理数据或卸载到云数据中心。云数据中心适用于数据分析，模型培训和管理边缘节点。在本文中，我们专注于协作边缘和云环境中的任务分配问题，并在分布式非合作环境中研究。建立M / M / 1排队模型以表征任务传输。由于多核处理器，我们设置了M / M / C排队模型，以表征任务计算。我们认为从博弈论的角度考虑了这个问题，并将其制定到多个代理（多个边缘数据中心）中的非合作游戏中，其中每个代理商都被告知他人的不完整信息（分配策略）。对于每个代理，我们将预期任务成本的函数定义为百所欲易函数，并最大限度地减少其受QoS约束的影响。我们分析了NASH均衡的存在，并开发了一种贪婪的能量感知算法（GEA），可以使用极限搜索算法（LSA）选择活动服务器来查找天花板利用率。然后我们提出了最佳响应算法（BRA）以优化实用程序功能。已经讨论了BRA算法的收敛。最后，结果表明，BRA算法可以获得靠近纳什均衡的解决方案，并快速达到它。

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