Collaborative Resource Sharing Game Based Cloud-Edge Offload Computing Orchestration Scheme

Abstract

In the foreseeable future, the rapid growth of mobile communications and cloud systems has substantially promoted edge computing paradigm. Although edge computing technology has been attracting much interest, most current research is application-specific without considering a control perspective of cloud providers that provides general-purpose edge services. In this study, we present a new cloud-edge computing orchestration scheme that integrates the different resource sharing problems to maximize benefits for offloading computing services. Specifically, we focus on two cooperative game solutions -Sim bargaining solution and interval Banzhaf Value -to effectively share the computing, communication and cache resources in the cloud-edge system platform. In the proposed scheme, we formalize two different cooperative games, and they work together and act cooperatively to satisfy contradictory requirements. Under widely different service request situations, we aim at accelerating an efficient resource orchestration through dynamic workload balancing. The primary goal of our cooperative game approach is to find optimal control decisions toward the appropriate system operation. Finally, we evaluate the proposed scheme in terms of several performance criteria from the numerical simulations. Experimental results show that we can achieve a mutually desirable solution with a good system performance balance while enhancing the resource efficiency better than the existing cloud-edge control protocols. INDEX TERMSCloud edge computing; Internet of Thing; Resource sharing problems; Sim bargaining solution; interval Banzhaf Value.

Decentralized Replica Management in Latency-Bound Edge Environments for Resource Usage Minimization

“…Many solutions are based (totally or partially) on centralized components deployed in a Cloud, such as centralized orchestrators that derive proper application placement plans. Some examples of this kind are the ones of Carrusca et al [12], Ullah et al [13], Siar and Izadi [14], Tang et al [15], Kim [16], Maia et al [17], and Guo et al [18]. These approaches' common feature is exploiting centralized decision-making processes to derive placement solutions that limit communications with remote (thus distant) Clouds .…”