An Optimal Offloading Partitioning Algorithm in Mobile Cloud Computing

Wu, Huaming; Knottenbelt, William J.; Wolter, Katinka; Sun, Yi

doi:10.1007/978-3-319-43425-4_21

Cited by 47 publications

(36 citation statements)

References 23 publications

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“…For a single user offloading its entire application, the tradeoff between energy saving and computing performance was studied in [5], [6], [27]. Different from the above wholeapplication offloading, the authors of [10]- [16] considered partitioning an application into multiple tasks. Specifically, the authors of [10]- [12] focus on the implementation of offloading mechanisms from the mobile device to the cloud, while the discussion on optimizing the offloading decisions was limited.…”

Section: A Two-tier Offloading Systemmentioning

confidence: 99%

“…E l ij local processing energy of user i's task j E t ij , E r ij uplink transmitting energy and downlink receiving energy of user i's task j between the mobile user and the AP T l ij , T a ij , T c ij local processing time, CAP processing time, and cloud processing time of user i's task j T t ij , T r ij uplink transmission time and downlink transmission time of user i's task j between the mobile user and the AP T ac ij transmission time of user i's task j between the AP and the cloud CUL, CDL uplink bandwidth and downlink bandwidth for transmission between mobile users and the AP CTotal total transmission bandwidth between mobile users and the AP c u i , c d i uplink bandwidth and downlink bandwidth assigned to user i η u i , η d i spectral efficiency of uplink and downlink transmission between user i and the AP r ac transmission rate between the AP and the cloud f a i CAP processing rate assigned to user i's tasks fA total CAP processing rate f c cloud processing rate for each user C a ij CAP usage cost of user i's task j C c ij cloud usage cost of user i's task j Remark: Our system model is a common one considered in many previous studies [7], [9], [13], [14], [16], [28]- [31], [34], where all M tasks of each user are assumed to be available at the starting time. For a dynamic system where the tasks arrive at different times, we may apply our model and the proposed solution in a quasi-static manner, where the system processes the tasks in batches as they are collected [39].…”

Section: Symbol Descriptionmentioning

confidence: 99%

“…and D a i being the auxiliary variables introduced corresponding to the uplink transmission time, downlink transmission time, and the CAP processing time, respectively. Auxiliary variables D u i and D d i are similarly defined as in (15) and (16), except that x ij in (15) and (16) is now replaced by (x a ij + x c ij ). Similar to these two constraints, the new auxiliary variable D a i for the CAP processing time also introduces a new constraint…”

Section: Multi-user Multi-task Offloading With Cap (Mumto-c) Algormentioning

confidence: 99%

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Multi-User Multi-Task Offloading and Resource Allocation in Mobile Cloud Systems

Chen

Liang

Dong

2018

IEEE Trans. Wireless Commun.

130

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We consider a general multi-user Mobile Cloud Computing (MCC) system where each mobile user has multiple independent tasks. These mobile users share the computation and communication resources while offloading tasks to the cloud. We study both the conventional MCC where tasks are offloaded to the cloud through a wireless access point, and MCC with a computing access point (CAP), where the CAP serves both as the network access gateway and a computation service provider to the mobile users. We aim to jointly optimize the offloading decisions of all users as well as the allocation of computation and communication resources, to minimize the overall cost of energy, computation, and delay for all users. The optimization problem is formulated as a non-convex quadratically constrained quadratic program, which is NP-hard in general. For the case without a CAP, an efficient approximate solution named MUMTO is proposed by using separable semidefinite relaxation (SDR), followed by recovery of the binary offloading decision and optimal allocation of the communication resource. To solve the more complicated problem with a CAP, we further propose an efficient three-step algorithm named MUMTO-C comprising of generalized MUMTO SDR with CAP, alternating optimization, and sequential tuning, which always computes a locally optimal solution. For performance benchmarking, we further present numerical lower bounds of the minimum system cost with and without the CAP. By comparison with this lower bound, our simulation results show that the proposed solutions for both scenarios give nearly optimal performance under various parameter settings, and the resultant efficient utilization of a CAP can bring substantial cost benefit.

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Section: A Two-tier Offloading Systemmentioning

confidence: 99%

Section: Symbol Descriptionmentioning

confidence: 99%

Section: Multi-user Multi-task Offloading With Cap (Mumto-c) Algormentioning

confidence: 99%

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Multi-User Multi-Task Offloading and Resource Allocation in Mobile Cloud Systems

Chen

Liang

Dong

2018

IEEE Trans. Wireless Commun.

130

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“…Apart from optimal resource allocation, for computational offloading, approaches like task partitioning and scheduling have been proposed in [12], [13]. In [12], the author presents an algorithm to partition a single task and optimally offload these partitioned task by analyzing their dependencies.…”

Section: A Related Workmentioning

confidence: 99%

Device-Centric Energy Optimization for Edge Cloud Offloading

Tayade

Rost

Maeder

et al. 2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

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A wireless system is considered, where, computationally complex algorithms are offloaded from user devices to an edge cloud server, for the purpose of efficient battery usage. The main focus of this paper is to characterize and analyze, the trade-off between the energy consumed for processing the data locally, and for offloading. An analytical framework is presented, that minimizes the in-device energy consumption, by providing an optimal offloading decision for multiple user devices. A closed form solution is obtained for the offloading decision. The solution also provides the amount of computational data that should be offloaded, for the given computational and communication resources. Consequently, reduction in the energy consumption is observed.

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“…Although the computation offloading issue in MCC has been well investigated (to list a few here [28][29][30][31][32][33], they cannot be used directly in computation offloading in MEC. The main reason is that MEC and MCC have completely different architectures.…”

Section: Introductionmentioning

confidence: 99%

An energy- and cost-aware computation offloading method for workflow applications in mobile edge computing

Peng

Zhu

Zhang

et al. 2019

J Wireless Com Network

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Mobile edge computing is becoming a promising computing architecture to overcome the resource limitation of mobile devices and bandwidth bottleneck of the core networks in mobile cloud computing. Although offloading applications to the cloud can extend the performance for the mobile devices, it may also lead to greater processing latency. Usually, the mobile users have to pay for the cloudlet resource or cloud resource they used. In this paper, we bring a thorough study on the energy consumption, time consumption, and cost of using the resource of cloudlet and cloud for workflow applications in mobile edge computing. Based on theoretical analysis, a multi-objective optimization model is established. In addition, a corresponding multi-objective computation offloading method based on non-dominated sorting genetic algorithm II is proposed to find the optimal offloading strategy for all the workflow applications. Finally, extensive experimental evaluations are performed to show that our proposed method is effective and energy-and cost-aware for workflow applications in MEC.

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An Optimal Offloading Partitioning Algorithm in Mobile Cloud Computing

Cited by 47 publications

References 23 publications

Multi-User Multi-Task Offloading and Resource Allocation in Mobile Cloud Systems

Multi-User Multi-Task Offloading and Resource Allocation in Mobile Cloud Systems

Device-Centric Energy Optimization for Edge Cloud Offloading

An energy- and cost-aware computation offloading method for workflow applications in mobile edge computing

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