New Application Task Offloading Algorithms for Edge, Fog, and Cloud Computing Paradigms

Abstract

In the last few years, we have seen an exponential increase in the number of computation-intensive applications, which have resulted in the popularity of fog and cloud computing paradigms among smart-chip-embedded mobile devices. These devices can partially offload computation tasks either using the fog system or using the cloud system. In this study, we design a new task offloading scheme by considering the challenges of future edge, fog and cloud computing paradigms. To provide an effective solution toward an appropriate task offloading problem, we focus on two cooperative bargaining game solutions—Tempered Aspirations Bargaining Solution (TABS) and Gupta-Livne Bargaining Solution (GLBS). To maximize the application service quality, a proper bargaining solution should be properly selected. In the proposed scheme, the TABS method is used for time-sensitive offloading services, and the GLBS method is applied to ensure computation-oriented offloading services. The primary advantage of our bargaining-based approach is to provide an axiom-based strategic solution for the task offloading problem while dynamically responding to the current network environments. Extensive simulation studies are conducted to demonstrate the effectiveness of the proposed scheme, and the superior performance over existing schemes is observed. Finally, we show prime directions for future work and potential research issues.

A Data Protection Focused Adaptation Engine for Distributed Video Analytics Pipelines

“…If the calculated values for a metric are equal the next metric is used for comparison, otherwise M is either better or worse than the best solution (line 12-21). If M is better than the best solution in regard to a specific comparison metric, the best solution is replaced by M , the path to the best solution is stored, and the comparison ends (line [12][13][14][15][16]. The comparison also ends if M is worse than the best solution (line [17][18][19].…”

“…Hence, adaptations in the system have to be designed in a way so that this heterogeneous infrastructural and software environment is also taken into account. Regarding performance related adaptations, several solutions are proposed in literature to optimize either one particular performance aspect of a VAP, e.g., execution time or latency, or focus on minimal energy consumption, or calculate a trade-off including some of those aspects [12]- [15]. While there is state of the art literature dealing with data protection related adaptations, most of them solely focus on increasing certain security or privacy aspects of a system, leaving previously mentioned performance characteristics out of scope [16]- [18].…”

Secure Communication Model for Dynamic Task Offloading in Multi-Cloud Environment

“…A promising network paradigm has been put forth: vehicle edge computing networks (VECNs) is proposed in [8], which combine MEC and vehicular networks. MEC offloading allows for a significant reduction in the computational strain on ICVs by placing MEC servers at the network's edge.…”

Evaluation and Analysis of Traditional Physical Training by Using Mobile Edge Computing and Software-Defined Networking

“…It proposed a light-weight algorithm by using the Markov approximation technique to converge to a bounded near-optimal solution. In [16], a new task offloading scheme by considering the challenges of future edge, fog, and cloud computing paradigms was devised. To provide an effective solution toward an appropriate task offloading problem, it focused on two cooperative bargaining game solutions.…”