Abstract
Multi-access Edge Computing (MEC) is recognised as a solution in future networks to offload computation and data storage from mobile and IoT devices to the servers at the edge of mobile networks. It reduces the network traffic and service latency compared to passing all data to cloud data centers while offering greater processing power than handling tasks locally at terminals. Since MEC servers are scattered throughout the radio access network, their computation capacities are modest in comparison to large cloud data centers. Therefore, offloading decision between MEC and cloud server should minimize the usage of the resources while maximizing the number of accepted delay critical requests. In this work we formulate the joint optimization of communication and computation resources allocation for computation offloading (CO) requests with strict latency constraints. We show that the global optimization problem is NP-hard and propose an efficient heuristic solution based on the single user optimal solution. Simulation results are presented to show the effectiveness of the proposed algorithm, compared to optimal and baseline solution where tasks are allocated in the order of arrival, with different system parameters. They show that our algorithm performs close to the optimal in terms of resource utilization and outperforms the baseline algorithm in terms of acceptance rate.
Highlights
Emerging applications, such as augmented and virtual reality, face recognition and language processing, are becoming more computationally demanding
This is a consequence of the single-task allocation using more bandwidth resources when the request is sent to the Cloud service, and it is important to intelligently sort the request to those to be sent to the cloud and those to be sent to the Multi-access Edge Computing (MEC)
In this paper we study the problem of two-tier computational offloading of latency limited tasks
Summary
Emerging applications, such as augmented and virtual reality, face recognition and language processing, are becoming more computationally demanding. I. Kovacevic et al.: Cloud and Edge CO for Latency Limited Services delay-critical applications that cannot be offloaded to distant cloud, require sophisticated decision-making algorithms. We propose joint allocation of wireless (communication) and computational resources for MEC and Cloud server offloading with strict delay requirements for multiple computational tasks. We consider the deterministic latency definition and focus on the computational tasks that have strict requirement to be completed within given time period This definition is appropriate for use cases such as VR (virtual reality), AR, and real-time control, where, for example, images have to be processed before human eye can detect the lagging. Efficient heuristic solution for dynamic CO resource allocation decision depending on the instantaneous network conditions and computational demands, including the trade-off between selection of low-latency MEC and high capacity cloud server.
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