An Adaptable Application Offloading Scheme Based on Application Behavior

Huerta-Cánepa, Gonzalo; Lee, Dongman

doi:10.1109/waina.2008.148

Cited by 50 publications

(15 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rudenko et al in [11] demonstrated by experiments that significant energy can be saved by computation offloading. Gonzalo et al in [12] developed an adaptive offloading algorithm based on both the execution history of applications and the current system conditions. Xian et al in [13] introduced an efficient timeout scheme for computation offloading to increase the energy efficiency on mobile devices.…”

Section: Related Workmentioning

confidence: 99%

Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

Chen

Jiao

et al. 2016

IEEE/ACM Trans. Networking

2,386

1,136

View full text Add to dashboard Cite

Mobile-edge cloud computing is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we first study the multi-user computation offloading problem for mobile-edge cloud computing in a multi-channel wireless interference environment. We show that it is NP-hard to compute a centralized optimal solution, and hence adopt a game theoretic approach for achieving efficient computation offloading in a distributed manner. We formulate the distributed computation offloading decision making problem among mobile device users as a multi-user computation offloading game. We analyze the structural property of the game and show that the game admits a Nash equilibrium and possesses the finite improvement property. We then design a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics. We further extend our study to the scenario of multi-user computation offloading in the multi-channel wireless contention environment. Numerical results corroborate that the proposed algorithm can achieve superior computation offloading performance and scale well as the user size increases.

show abstract

Section: Related Workmentioning

confidence: 99%

Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

Chen

Jiao

et al. 2016

IEEE/ACM Trans. Networking

2,386

1,136

View full text Add to dashboard Cite

show abstract

“…This method is efficient only if all the factors are accurately known in advance. Prediction algorithms include history-based prediction [14,15], fuzzy control [16], and probabilistic prediction [17]. It involves onetime partitioning of the application, i.e., during its design time.…”

Section: Static and Dynamic Partitioningmentioning

confidence: 99%

Offloading for Application Optimization Using Mobile Cloud Computing

Singla

Kaushal

2016

Proceedings of the International Congress on Information and Communication Technology

View full text Add to dashboard Cite

Mobile applications are increasingly becoming ubiquitous which provides rich functionalities and services to mobile users. But despite having some technological advancements, smart mobile devices (SMDs) are still minimum potential computing devices enforced by battery life, storage capacity, and network bandwidth which obstructs the possible execution of computational intensive applications. Thus, mobile cloud computing is employed to optimize the computationally intensive applications which use computation offloading techniques for increasing SMD's capabilities. The aim of this paper is to emphasize the specific issues related to mobile cloud computing, offloading, and thus concluding the paper by analyzing the challenges that have not been met perfectly and charts a roadmap towards this direction.

show abstract

“…A task scheduling algorithm based on the Lyapunov optimization theory was proposed in [33], which combined task offloading, device-base station association, and the optimal scheduling of sleep mode on base stations to minimize the overall energy consumption of equipment and base stations. An adaptive sequential offloading task was proposed to solve these problems in [8] that mobile users could make offloading decisions sequentially based on the current interference environment and available computing resources [34][35][36]. This could achieve good results in reducing delay and energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Computation Offloading Strategy in Mobile Edge Computing

Sheng

Teng

et al. 2019

Information

View full text Add to dashboard Cite

Mobile phone applications have been rapidly growing and emerging with the Internet of Things (IoT) applications in augmented reality, virtual reality, and ultra-clear video due to the development of mobile Internet services in the last three decades. These applications demand intensive computing to support data analysis, real-time video processing, and decision-making for optimizing the user experience. Mobile smart devices play a significant role in our daily life, and such an upward trend is continuous. Nevertheless, these devices suffer from limited resources such as CPU, memory, and energy. Computation offloading is a promising technique that can promote the lifetime and performance of smart devices by offloading local computation tasks to edge servers. In light of this situation, the strategy of computation offloading has been adopted to solve this problem. In this paper, we propose a computation offloading strategy under a scenario of multi-user and multi-mobile edge servers that considers the performance of intelligent devices and server resources. The strategy contains three main stages. In the offloading decision-making stage, the basis of offloading decision-making is put forward by considering the factors of computing task size, computing requirement, computing capacity of server, and network bandwidth. In the server selection stage, the candidate servers are evaluated comprehensively by multi-objective decision-making, and the appropriate servers are selected for the computation offloading. In the task scheduling stage, a task scheduling model based on the improved auction algorithm has been proposed by considering the time requirement of the computing tasks and the computing performance of the mobile edge computing server. Extensive simulations have demonstrated that the proposed computation offloading strategy could effectively reduce service delay and the energy consumption of intelligent devices, and improve user experience.

show abstract

An Adaptable Application Offloading Scheme Based on Application Behavior

Cited by 50 publications

References 6 publications

Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing

Offloading for Application Optimization Using Mobile Cloud Computing

Computation Offloading Strategy in Mobile Edge Computing

Contact Info

Product

Resources

About