Abstract
Mobile Edge Computing (MEC) plays key role for providing fast-response and high interactivity in the emerging 5G network. Edge service providers (ESP) are responsible for serving edge users or IoT devices running latency-critical applications. An MEC server provides faster response to ESPs but has limited computation resources, hence, it can be overloaded due to extensive resource demand. Thus, federation of multiple MEC servers offers an opportunity for dynamic resource allocation in a distributed manner. The federation objective is to maximize the usage of underutilized edge resources and reduction of service provision time simultaneously. As all the ESPs and MECs act autonomously, it is quite impossible for all the individuals to achieve optimal behavior simultaneously. In this paper, we develop a Stackelberg Game ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$SBG$ </tex-math></inline-formula> ) based dynamic resource allocation method to reach the expected performance. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$SBG$ </tex-math></inline-formula> analyzes the pricing of MECs and the resource-purchasing problem of ESPs. We also develop a many-to-many matching algorithm for resource sharing among the MECs and a one-to-many matching algorithm for that between an MEC server and ESPs. The results from an extensive performance evaluation demonstrate effectiveness of the proposed system in increasing utilities for MECs and ESPs, reducing the turnaround time of application tasks, and ensuring fair resource distribution compared to state-of-the-art works.
Highlights
The cloud computing and wireless communication integration created enormous scope for faster real-time communication
The first one is enhanced Mobile Broadband, which is characterized by high data rate requirements as virtual reality (VR), augmented reality (AR), etc
To serve the edge users, Edge service providers (ESP) have to pay for purchasing computational resource blocks (CRB) from Mobile Edge Computing (MEC), following the model presented in [26], the utility of ESP can be measured by the revenue received from the incoming workload of ESP and subtracting the cost for the payment of MEC and the cost incurred by the service delay, represented as following
Summary
The cloud computing and wireless communication integration created enormous scope for faster real-time communication. Different applications require heterogeneous resources and it is quite challenging to meet the ongoing resource demand for mobile devices and IoT present at the 5G edge network [4]–[6]. In the situation of resource shortage of any particular MEC server, the solution could be purchasing virtualized computational resource blocks (CRB) from different cloud servers [12] In this case, the service latency may become intolerable, as ESPs are providing latency-critical applications, web services, etc. We propose a distributed edge federation model for allocating resources dynamically between MEC servers and ESPs. The objective of each entity is to maximize its utility. We formulated the Stackelberg game to solve optimal requirement of resource and the pricing problem, observing the interaction between ESP and MEC servers.
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