Joint offloading decision and resource allocation for multi-user multi-task mobile cloud

Chen, Meng-Hsi; Liang, Ben; Dong, Min

doi:10.1109/icc.2016.7510999

Cited by 167 publications

(118 citation statements)

References 17 publications

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“…Service placement is a problem with significant interest in the current literature. In [1], authors considered the energy cost incurred by offloading tasks from the user onto a cloudlet. In particular, this work proposed a sub-optimal algorithm for approaching when offloading should occur (based on the energy cost), but does not consider the users' QoS constraints.…”

Section: Related Workmentioning

confidence: 99%

Meeting Users’ QoS in a Edge-to-Cloud Platform via Optimally Placing Services and Scheduling Tasks

Turner

Khamfroush

2020

2020 International Conference on Computing, Networking and Communications (ICNC)

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This paper considers the problem of service placement and task scheduling on a three-tiered edgeto-cloud platform when user requests must be met by a certain deadline. Time-sensitive applications (e.g., augmented reality, gaming, real-time video analysis) have tight constraints that must be met. With multiple possible computation centers, the "where" and "when" of solving these requests becomes paramount when meeting their deadlines. We formulate the problem of meeting users' deadlines while minimizing the total cost of the edge-tocloud service provider as an Integer Linear Programming (ILP) problem. We show the NP-hardness of this problem, and propose two heuristics based on making decisions on a local vs global scale. We vary the number of users, the QoS constraint, and the cost difference between remote cloud and cloudlets(edge clouds), and run multiple Monte-Carlo runs for each case. Our simulation results show that the proposed heuristics are performing close to optimal while reducing the complexity.

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Section: Related Workmentioning

confidence: 99%

Meeting Users’ QoS in a Edge-to-Cloud Platform via Optimally Placing Services and Scheduling Tasks

Turner

Khamfroush

2020

2020 International Conference on Computing, Networking and Communications (ICNC)

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“…Till now, lots of work have been carried out for the resources allocation problem of the MEC system. At first, the offloading decision and resources allocation depend on the properties of the task, ie, binary offloadable task or partial offloadable task . For the former, the tasks are either locally implemented or offloaded to the cloud to execute, while for the latter, a task can be segmented into at least two parts, and one of them is performed at local device, and the other is offloaded to the cloud platform.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the system design objective or the user‐perceived performance metric also plays a leading role in the offloading decision and resources allocation. Typically, system design objectives are latency minimization, energy consumption reduction, and delay‐energy tradeoff …”

Section: Introductionmentioning

confidence: 99%

“…Typically, system design objectives are latency minimization, 12 energy consumption reduction, 12,13,15,16 and delay-energy tradeoff. [5][6][7][8][9][10] From a methodological point of view, the most aforementioned work of the resources allocation problems are modeled by the optimization theory. However, as an important branch of game theory, 20 the auction theory is widely used in the field of wireless network resource allocation recently, such as cognitive network, 21 HetNets, 22 and the mobile cloud networks.…”

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confidence: 99%

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Online combinatorial based mechanism for MEC network resource allocation

Jiang

et al. 2019

Int J Communication

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Summary In this paper, the resource allocation problem for mobile edge computing (MEC) network is discussed. We focus on the design of online resource allocation strategy by considering the time‐varying demands of the combinatorial resources for the mobile users. To handle this issue, at first, a time splitting‐based online allocation mechanism is introduced. Then, for a given resource allocation period of the time splitting, a combinatorial auction mechanism for the combination resources allocation is proposed. We further prove that the proposed combinatorial auction is both individual rational and incentive‐compatible, and which can bring a higher revenue to the service provider. In order to verify the performance of the proposed mechanism, a variable‐relaxed‐based performance‐bound algorithm and a greedy optimization‐based suboptimal algorithm are presented for comparison purpose. Finally, the performance of these proposed algorithms are testified by numerical simulations, which confirm the analysis results.

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“…The authors in [12], consider the problem of resource scheduling for multi service multi user MCC networks. Also, in [13] a heuristic approach is adopted to minimize the energy consumption of all users while making decision on offloading and resource allocation for each task. The study in [14], models the decision offloading in a multi radio interface to figure out the optimal solution of the conflicting objectives, namely, computation costs and the execution time of the application.…”

Section: Introductionmentioning

confidence: 99%

Green Cloud Computing for Multi Cell Networks

Masoudi

Khamidehi

Çavdar

2017

2017 IEEE Wireless Communications and Networking Conference (WCNC)

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Abstract-This paper investigates the power minimization problem for the user terminals by application offloading in multicell multi-user OFDMA mobile cloud computing networks where some practical constraints such as backhaul capacity limitation, interference level on each channel and maximum tolerable delay as user's quality of service is taken into account. Furthermore, the mixed integer nonlinear problem is converted into a convex form using D.C. approximation. Moreover, to solve the optimization problem, we have proposed joint power allocation and decision making (J-PAD) algorithm which can make offloading decision and allocate power at the same time. Simulation results illustrate that by utilizing the J-PAD algorithm, in comparison with baselines, considerable power saving could be achieved e.g. about 30% for delays more than 100 ms.

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Joint offloading decision and resource allocation for multi-user multi-task mobile cloud

Cited by 167 publications

References 17 publications

Meeting Users’ QoS in a Edge-to-Cloud Platform via Optimally Placing Services and Scheduling Tasks

Meeting Users’ QoS in a Edge-to-Cloud Platform via Optimally Placing Services and Scheduling Tasks

Online combinatorial based mechanism for MEC network resource allocation

Green Cloud Computing for Multi Cell Networks

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