Resource Modeling and Scheduling for Mobile Edge Computing: A Service Provider’s Perspective

• Main Authors: Shuaishuai Guo, Dalei Wu, Haixia Zhang, Dongfeng Yuan
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Mobile edge computing (MEC); queueing network model; admission control; resource management; quality-of-service (QoS); latency
• Online Access: https://ieeexplore.ieee.org/document/8399804/

This paper investigates resource modeling and management for a base station (BS) providing mobile edge computing (MEC) service. In the proposed modeling, BS is recognized as a queueing network consisting of multiple multi-type servers. The uplink transmission users, downlink transmission users, and MEC users with different priority levels are jointly considered. It is assumed that their service-requests arrive dynamically and are also served dynamically. With such a general resource modeling, the interaction among these users can be analyzed based on the queueing network theory. The average delay of each service-type with different priority levels is derived. Based on the derived results, two resource management optimization problems are formulated and solved from the perspective of a service provider. The revenue brought by MEC services is first maximized by doing user admission control while provisioning the quality-of-service (QoS) of all admitted users with the given amount of communication and computation resources. Then, the capital expenditure of resource deployment is minimized by satisfying the QoS of all users. It is formulated as an integer programming problem. An algorithm is developed to solve it, which can help service providers to determine the optimal amount of communication and computation resources to be placed in a BS to guarantee QoS for all users at a minimal total capital expenditure. Computer simulations are done to validate all analysis and comparisons are made with BS serving multi-type users of single priority level. Through comparison, an insight is gained that service providers can obtain more revenue or spare less capital expenditure by differentiating the user priority levels.