An Online Orchestration Mechanism for General-Purpose Edge Computing

TL;DR

This paper proposes an online orchestration mechanism for general-purpose edge computing, focusing on maximizing revenue for edge cloud providers through resource allocation and data placement, with theoretical guarantees.

Contribution

It introduces a novel online framework combining drift-plus-penalty and primal-dual methods for edge resource management, addressing a gap in business-oriented edge computing research.

Findings

The proposed method effectively approximates the optimal solution.

The framework adapts to dynamic environments without future system knowledge.

Simulation results validate the approach's efficiency.

Abstract

In recent years, the fast development of mobile communications and cloud systems has substantially promoted edge computing. By pushing server resources to the edge, mobile service providers can deliver their content and services with enhanced performance, and mobile-network carriers can alleviate congestion in the core networks. Although edge computing has been attracting much interest, most current research is application-specific, and analysis is lacking from a business perspective of edge cloud providers (ECPs) that provide general-purpose edge cloud services to mobile service providers and users. In this article, we present a vision of general-purpose edge computing realized by multiple interconnected edge clouds, analyzing the business model from the viewpoint of ECPs and identifying the main issues to address to maximize benefits for ECPs. Specifically, we formalize the long-term revenue of ECPs as a function of server-resource allocation and public data-placement decisions subject to the amount of physical resources and inter-cloud data-transportation cost constraints. To optimize the long-term objective, we propose an online framework that integrates the drift-plus-penalty and primal-dual methods. With theoretical analysis and simulations, we show that the proposed method approximates the optimal solution in a challenging environment without having future knowledge of the system.