Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel

By Weiwen Zhang, Yonggang Wen, K. Guan, D. Kilper, Haiyun Luo, Dapeng Oliver Wu. IEEE Transactions on Wireless Communications, 2013

789 citations recorded by the source corpus.

This paper provides a theoretical framework of energy-optimal mobile cloud computing under stochastic wireless channel. Our objective is to conserve energy for the mobile device, by optimally executing mobile applications in the mobile device (i.e., mobile execution) or offloading to the cloud (i.e., cloud execution). One can, in the former case sequentially reconfigure the CPU frequency; or in the latter case dynamically vary the data transmission rate to the cloud, in response to the stochastic channel condition. We formulate both scheduling problems as constrained optimization problems, and obtain closed-form solutions for optimal scheduling policies. Furthermore, for the energy-optimal execution strategy of applications with small output data (e.g., CloudAV), we derive a threshold policy, which states that the data consumption rate, defined as the ratio between the data size (L) and the delay constraint (T), is compared to a threshold which depends on both the energy consumption model and the wireless channel model. Finally, numerical results suggest that a significant amount of energy can be saved for the mobile device by optimally offloading mobile applications to the cloud in some cases. Our theoretical framework and numerical investigations will shed lights on system implementation of mobile cloud computing under stochastic wireless channel.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/1bcfd4d6eaa28a667caab4c02c0802f79980ffc6

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Citations in the corpus

Joint Offloading and Trajectory Design for UAV-Enabled Mobile Edge Computing Systems (2019, background): “[8] have developed an offloading strategy to reduce the energy consumption under the delay constraints.”
Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems (2019, background): “The SeV may deal with multiple computation tasks simultaneously, and adopt dynamic frequency and voltage scaling (DVFS) technique to dynamically adjust the CPU frequency [27], and thus we have f t,n ∈ [0 , F n ] .”
EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks (2017, background): “proposed in [10] by comparing the energy consumption of local execution and offloading, while a delay-optimal task scheduling policy with random task arrivals is proposed in [11] based on Markov Decision Process (MDP).”
Latency Minimization for Intelligent Reflecting Surface Aided Mobile Edge Computing (2019, background): “More explicitly, the binary off-loading strategy of [6] was proposed to decide whether the task is executed locally at the mobile device or remotely at the edge-cloud node.”
Energy-Latency Tradeoff for Energy-Aware Offloading in Mobile Edge Computing Networks (2018, background): “where κ = 10−26, and it is a coefficient depending on the chip architecture [28], [29].”
Optimization of Radio and Computational Resources for Energy Efficiency in Latency-Constrained Application Offloading (2014, methodology): “This radio–cloud interaction is addressed in [13], by considering the Gilbert–Elliott channel model for the wireless transmission.”
A Survey on Mobile Edge Computing: The Communication Perspective (2017, background): “It is further noticed in [76] and [79] that, the clock frequency of the CPU chip is approximately linear proportional to the voltage supply when operating at the low voltage limits.”
Mobile Cooperative Computing: Energy-Efficient Peer-to-Peer Computation Offloading (2017, methodology): “In particular, energy-efficient techniques are designed in [4] for controlling the CPU frequency for local computing and transmission rate for offloading.”
Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems (2018, methodology): “Although resource allocation problems have been well studied in MEC systems [5]–[10], MEC systems relying on energy harvesting [11]–[16] and UAV-enabled MEC systems [21]–[25], few investigations have been conducted for designing resource allocation schemes in the UAV-enabled wireless powered MEC sys”
Imperfect Information Dynamic Stackelberg Game Based Resource Allocation Using Hidden Markov for Cloud Computing (2018, background): “[3] proposed a migration decision strategy that decides whether to perform an application migration based on different applications of energy consumption in the cloud.”
Joint Load Balancing and Offloading in Vehicular Edge Computing and Networks (2019, methodology): “Different from the task offloading in previous works which aim to optimize energy consumption [7], [8], [10], [12], [14], [15], the task processing delay is a critical metric for vehicles.”
Collaborative Cloud and Edge Computing for Latency Minimization (2019, background): “There are mainly two objectives for the joint resource allocation: maximizing the energy efficiency [7]–[13] and minimizing the end-to-end latency [14]–[23].”