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TASK-DRIVEN RESOURCE ASSIGNMENT IN MOBILE EDGE COMPUTING EXPLOITING EVOLUTIONARY COMPUTATION
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DOI：10.1109/MWC.001.1800582.png)
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Abstract
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The IoT network allows IoT devices to communicate with other devices, applications, and services by exploiting existing network infrastructure. Recently, a promising paradigm, MEC, emerging for alleviating high latency data services in cloud computing framework plays an important role in the IoT network. Network performance and intelligence can be improved by integrating cognitive and cooperative mechanisms in the MEC framework. However, the QoS of computation-intensive tasks may degrade because of the limited available computational resources in MEC servers. Moreover, the characteristics of resources belonging to MEC servers and cloud servers are commonly different. In order to optimize the strategy of resource assignment, the tasks of assigning the limited computational resources in MEC servers and resolving the high latency problem in cloud servers have attracted growing interest from researchers. In this article, we propose a joint optimization paradigm for task-driven resource assignment based on evolutionary computation considering the power consumption and computation/communication delay simultaneously. The MEC framework consists of MEC servers, mobile devices, and cloud servers, and offloads the computational resources to the edge of end users. Additionally, we introduce and analyze three typical task-driven cases, which are the server-determined condition, server-flexible condition, and server-uncertain condition, respectively. Finally, we present the existing technical challenges and discuss the open research issues.
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