Cost-benefit analysis game for efficient storage allocation in cloud-centric Internet of Things systems: A game theoretic perspective

Abstract

The advances in Internet of Everything (IoE) and the market-oriented cloud computing have provided opportunities to resolve the challenges caused by the Internet of Things (IoT) infrastructure virtualization, capacity planning, data storage or complexity. The volume and types of IoT data motivate the need for a data storage framework towards the integration of both structured and unstructured data. In this paper, we propose a novel game theoretic technique for efficient and dynamic storage allocation in cloud-centric IoT systems. The benefit maximization problem is formulated as a cost-benefit analysis game investigating the storage capacity currently used in the cloud. In view of each player's strategy to lease additional storage capacity, the game property is analyzed and we prove that the game always admits a pure strategy Nash equilibrium. Since the player's decision affects the level of benefit maximization, we elaborate on a cost-optimal storage allocation incentive mechanism, which scales effectively once non-linear or linear demand for storage capacity occurs, towards achieving optimal leasing conditions on cloud storage and computing capacity level. The experimental validation tests prove the effectiveness of the proposed game theoretic approach allocating the requests for more storage capacity in a cost-effective manner, which achieves to maximize the benefits.