A Game-Theoretic Approach for Increasing Resource Utilization in Edge Computing Enabled Internet of Things

• Published in: IEEE access Vol. 10; pp. 57974 - 57989
• Main Authors: Kumar, Sumit, Gupta, Ruchir, Lakshmanan, K., Maurya, Vipin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; app vendors; Cloud computing; Convergence; Edge computing; Game theory; Games; Geographical locations; Internet of Things; Nash equilibrium; Network latency; Resource allocation; Resource management; Resource utilization; Servers; Stability analysis; Task analysis
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3175850

Edge computing is a new paradigm that reduces latency and saves bandwidth by deploying edge servers in different geographic locations. This technology plays a crucial role in the rapidly growing app market for IoT devices as app vendors can hire computing resources on edge servers to serve their app users. An effective allocation of edge computing resources to different apps is needed to maximize resource utilization and serve the most app users at the lowest cost. We refer to this as an Edge Resource Allocation (ERA) problem. In this paper, we propose an Edge Resource Allocation Game (ERAGame), a game-theoretic approach that formulates the ERA problem by appropriately pricing the multi-tenant edge servers. The proposed approach gives a Pure Nash Equilibrium (PNE) solution to the ERA problem. For this, we design an ERA algorithm using ERAGame under which the system converges to PNE. For fast convergence to PNE, the edge servers are partitioned into different groups, enabling the ERA algorithm to run in parallel on all edge servers within each group. We prove that ERAGame is a potential game that guarantees at least one PNE under the ERA algorithm. We evaluate that the price of stability of ERAGame is at most <inline-formula> <tex-math notation="LaTeX">O (log~n) </tex-math></inline-formula>. The performance of the proposed algorithm is examined through simulation.