Cooperative Runtime Offloading Decision Algorithm for Mobile Cloud Computing

Jin, Xiaomin; Wang, Zhongmin; Hua, Wenqiang

doi:10.1155/2019/8049804

Cited by 11 publications

(8 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, the latter fulfills the offloading process before executing the requests. 37,38 Although some researches present a solution to fulfill runtime migration, but it is not still included enough works in the literature and consequently it can be considered in open fields to researchers.…”

Section: Runtime Migrationmentioning

confidence: 99%

See 1 more Smart Citation

A review on the computation offloading approaches in mobile edge computing: A game‐theoreticperspective

2020

View full text Add to dashboard Cite

In recent years, novel mobile applications such as augmented reality, virtual reality, and three-dimensional gaming, running on handy mobile devices have been pervasively popular. With rapid developments of such mobile applications, decentralized mobile edge computing (MEC) as an emerging distributed computing paradigm is developed for serving them near the smart devices, usually in one hop, to meet their computation, and delay requirements. In the literature, offloading mechanisms are designed to execute such mobile applications in the MEC environments through transferring resource-intensive tasks to the MEC servers. On the other hand, due to the resource limitations, resource heterogeneity, dynamic nature, and unpredictable behavior of MEC environments, it is necessary to consider the computation offloading issues as the challenging problem in the MEC environment. However, to the best of our knowledge, despite its importance, there is not any systematic, comprehensive, and detailed survey in game theory (GT)-based computation offloading mechanisms in the MEC environment. In this article, we provide a systematic literature review on the GT-based computation offloading approaches in the MEC environment in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and to provide open issues as well. The proposed taxonomy is classified into four main fields: classical game mechanisms, auction theory, evolutionary game mechanisms, and hybrid-base game mechanisms. Next, these classes are compared with each other according to the important factors such as performance metrics, case studies, utilized techniques, and evaluation tools, and their advantages and disadvantages are discussed, as well. Finally, open issues and future uncovered or weakly covered research challenges are discussed and the survey is concluded. K E Y W O R D S game theory, mobile edge computing, offloading, systematic review 1 INTRODUCTION Thanks to the recent improvements in hardware and software capabilities and accordingly complex mobile applications of mobile devices, they are becoming more popular in recent years. In the presence of emerging technologies,

show abstract

Section: Runtime Migrationmentioning

confidence: 99%

“…In the former, the application is offloaded while it is executing the requests. On the other hand, the latter fulfills the offloading process before executing the requests …”

Section: Introductionmentioning

confidence: 99%

A review on the computation offloading approaches in mobile edge computing: A game‐theoreticperspective

2020

View full text Add to dashboard Cite

show abstract

“…Offloading techniques may be grouped into three broad categories: client-server communication; virtualization; and mobile agents. For further information, see [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…In comparison to single-site computation offloading, multisite computation offloading gives consumers additional options (see Fig. 1) [5]. Numerous academics have investigated and built methods that concentrate on multisite offloading in recent years.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Offloading Mechanism for Mobile Cloud Computing: A Niching Genetic Algorithm Perspective

Zalat¹,

Madbouly

Darwish

2022

IEEE Access

View full text Add to dashboard Cite

The fast evolution of mobile applications demonstrates the growing need for more resources and processing power on mobile devices. Mobile Cloud Computing (MCC) combines cloud computing with mobile devices, enabling sophisticated and resource-intensive applications to operate on mobile devices regardless of their performance limits (e.g., battery life, memory utilization, and computation). Overcoming these constraints is accomplished using a well-known technique called computation offloading, which entails offloading heavy processing to resourceful servers and getting the results from these servers. Many studies have been done on mobile code offloading, with the goal of avoiding the stated limits and reducing execution time or battery consumption through single-or multiple-site offloading, so that people can use their phones and tablets more. However, the majority of existing techniques make offloading choices based on profile data, which implies a stable network environment and forces an object to be offloaded to the same site. As a consequence of these challenges, this research proposes a novel strategy for enhancing the multisite offloading mechanism by combining a Niching Genetic Algorithm (NGA) with a Markov Decision Process (MDP). MDP is used to determine the most optimal location for each application's modules to be executed. GA was used to determine the optimal transition probability for components operating on several sites. To aid in initial population selection, the proposed model employs a niche model in the form of a context-based clearing (CBC) technique to improve the variability of the genes inside the chromosome in order to minimize their association. The simulation results reveal that the proposed technique consumes little power and executes quickly while determining the optimal offloading decision with lower generation numbers.INDEX TERMS Mobile cloud computing, offloading, niching genetic algorithm, markov decision process.

show abstract

“…e aforementioned applications are computation-intensive and energy-hungry and require large storage and faster execution [3]. To meet these requirements, an attempt is the cloud computing system that contains centralized servers having higher processing powers and storage capabilities [4,5]. However, the delay-sensitive applications such as online simulators, live streaming, and online games cannot be executed through cloud servers due to the inherited problem of high latency caused by long distances of cloud servers from the MTs [6,7].…”

Section: Introductionmentioning

confidence: 99%

Smart Application Division and Time Allocation Policy for Computational Offloading in Wireless Powered Mobile Edge Computing

Numani

Ali

Abbas

et al. 2021

Mobile Information Systems

View full text Add to dashboard Cite

Limited battery life and poor computational resources of mobile terminals are challenging problems for the present and future computation-intensive mobile applications. Wireless powered mobile edge computing is one of the solutions, in which wireless energy transfer technology and cloud server’s capabilities are brought to the edge of cellular networks. In wireless powered mobile edge computing systems, the mobile terminals charge their batteries through radio frequency signals and offload their applications to the nearby hybrid access point in the same time slot to minimize their energy consumption and ensure uninterrupted connectivity with hybrid access point. However, the smart division of application into k subtasks as well as intelligent partitioning of time slot for harvesting energy and offloading data is a complex problem. In this paper, we propose a novel deep-learning-based offloading and time allocation policy (DOTP) for training a deep neural network that divides the computation application into optimal number of subtasks, decides for the subtasks to be offloaded or executed locally (offloading policy), and divides the time slot for data offloading and energy harvesting (time allocation policy). DOTP takes into account the current battery level, energy consumption, and time delay of mobile terminal. A comprehensive cost function is formulated, which uses all the aforementioned metrics to calculate the cost for all k number of subtasks. We propose an algorithm that selects the optimal number of subtasks, partial offloading policy, and time allocation policy to generate a huge dataset for training a deep neural network and hence avoid huge computational overhead in partial offloading. Simulation results are compared with the benchmark schemes of total offloading, local execution, and partial offloading. It is evident from the results that the proposed algorithm outperforms the other schemes in terms of battery life, time delay, and energy consumption, with 75% accuracy of the trained deep neural network. The achieved decrease in total energy consumption of mobile terminal through DOTP is 45.74%, 36.69%, and 30.59% as compared to total offloading, partial offloading, and local offloading schemes, respectively.

show abstract

Cooperative Runtime Offloading Decision Algorithm for Mobile Cloud Computing

Cited by 11 publications

References 30 publications

A review on the computation offloading approaches in mobile edge computing: A game‐theoreticperspective

A review on the computation offloading approaches in mobile edge computing: A game‐theoreticperspective

An Adaptive Offloading Mechanism for Mobile Cloud Computing: A Niching Genetic Algorithm Perspective

Smart Application Division and Time Allocation Policy for Computational Offloading in Wireless Powered Mobile Edge Computing

Contact Info

Product

Resources

About