A Survey on Matching Theory for Distributed Computation Offloading in IoT-Fog-Cloud Systems: Perspectives and Open Issues

Abstract

Fog computing has been widely integrated in the IoT-based systems, creating IoT-Fog-Cloud (IFC) systems to improve the system performances and satisfy the quality of services (QoS) and quality of experience (QoE) requirements for the end users (EUs). This improvement is enabled by computational offloading schemes, which perform the task computation nearby the task generation sources (i.e., IoT devices, EUs) on behalf of remote cloud servers. To realize the benefits of offloading techniques, however, there is a need to incorporate efficient resource allocation frameworks, which can deal effectively with intrinsic properties of computing environment in the IFC systems such as resource heterogeneity of computing devices, various requirements of computation tasks, high task request rates, and so on. While the centralize optimization and non-cooperative game theory based solutions are applicable in a certain number of application scenarios, they fail to be efficient in many of cases, where the global information and control might be unavailable or cost-intensive to achieve it in the large-scale systems. The need of distributed computational offloading algorithms with low computation complexity has motivated a surge of solutions using matching theory. In the present review, we first describe the fundamental concept of this emerging tool enabling the distributed implementation in the computing environment. Then the key solution concepts and algorithmic implementations proposed in the framework of literature are highlighted and discussed. Given the powerful tool of matching theory, its full capability is still unexplored and unexploited in the literature. We thereby discover and discuss existing challenges and corresponding solutions that the matching theory can be applied to resolve them. Furthermore, new problems and open issues for application scenarios of modern IFC systems are also investigated thoroughly. INDEX TERMSIoT-Fog-Cloud systems, Matching theory, Distributed algorithm, Computational offloading. I. INTRODUCTION Practically, the Internet of Things (IoT) has become an integral element for realizing smart practical systems such as smart cities [1], smart grids [2], smart factories [3], smart logistics, and supply chain [4], [5]. The fundamental aspect of IoT-based systems is to connect all devices through the Internet protocol to exchange high volume data and pro-cess them to create smart services and applications [6], [7]. Owing to limited computation resources, network, storage, and energy, IoT devices are inadequate for executing all computational tasks, especially tasks with huge volumes and complex data structures. Cloud computing is an essential solution to this problem because it provides powerful resources to fulfill tasks efficiently [8], [9]. However, cloud

Multi-Objective Optimization Based Distributed Task Offloading in Fog Computing

“…The primary goal of the study is to minimize the user's power consumption along with efficient resource allocation. By utilizing matching theory, Tran et al, investigated several offloading strategies for resource management [15]. In the NSGA-III algorithm, which is based on GA-based offloading, A queueing theory based task-offloading algorithm is suggested to minimize execution delay and optimize energy consumption in [16].…”

A Blockchain-Based Audit Mechanism for Trust and Integrity in IoT-Fog Environments

“…Prior to mutual authentication, IoT devices must select to which fog node it wishes to contact for service. There exist many, more sophisticated matching algorithms for pairing IoT devices with fog nodes based on proximity and available fog resource capacity [48].…”

Dynamic collaborative task offloading for delay minimization in the heterogeneous fog computing systems

“…To efficiently deal with the dynamic nature of fog computing environment, distributed algorithms using matching theory are developed in the literature [27]. In this class of algorithms, the task offloading problem is translated into the matching game between the task set and HN set.…”