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mobile computation offloading for Application Throughput Fairness and Energy Efficiency

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2019年第1期18卷 3-19页

作者： Kim, Yeongjin Lee, Hyang-Won Chong, Song Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 305701 South Korea Konkuk Univ Internet & Multimedia Engn Seoul 143701 South Korea

mobile computation offloading is emerging as a promising technology to enhance the computation power of mobile devices by borrowing processing resources from the cloud. However, using cloud resource is a double-edged sword because of the potentially enormous network energy consumption of mobile devices. In this paper, we study the mobile device resource management problem for application throughput fairness and energy efficiency in computation offloading environment. Our problem seeks to optimize task arrival rates, scheduling for local processing and offloading, CPU clock speed, and network interface selection, so as to maximize the energy-utility efficiency defined as achievable utility per unit energy consumption. The efficiency metric has a fractional form that is hard to deal with in general. To address this difficulty, we modify a general Lyapunov optimization technique and derive a series of short-term problems that change over time with respect to an unknown objective parameter. Then, we derive an offloading algorithm and prove that the algorithm maximizes the long-term energy-utility efficiency. Trace-driven simulations demonstrate that our algorithm achieves high-energy efficiency while maintaining throughput fairness among applications running on a mobile device.

关键词： Energy-utility efficiency mobile computation offloading application throughput fairness

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Optimal multi-part mobile computation offloading with hard deadline constraints

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COMPUTER COMMUNICATIONS 2020年 160卷 614-622页

作者： Hekmati, Arvin Teymoori, Peyvand Todd, Terence D. Zhao, Dongmei Karakostas, George Univ Southern Calif Dept Elect & Comp Engn Los Angeles CA 90007 USA McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada McMaster Univ Dept Comp & Software Hamilton ON Canada

This paper considers mobile computation offloading when concurrent local execution (CLE) is used to enforce task execution time constraints. This mechanism can ensure that hard task deadlines are satisfied regardless of any randomness induced by the wireless channel, network or cloud servers. In this type of system, mobile device energy may be reduced by segmenting a task upload into multiple parts, rather than doing a conventional contiguous task upload. The paper uses this mechanism to adapt to changes in channel conditions during the offload. Unlike the contiguous task offload case however, the upload initiation times of each part must be determined dynamically. This is done while ensuring that hard task deadlines are always satisfied. In this paper, the multi-part computation offloading case is considered. In multi-part offloading, the task to be offloaded is partitioned into K upload parts before any offload initiation decisions are made. In this case, current channel state information is incorporated into the offload decisions, and the system must always satisfy a hard task execution time constraint using concurrent local execution. The paper considers the case for Markovian wireless channels. A provably energy-optimal online computation offloading algorithm (MuliOpt) is introduced for multi-part offloading. MultiOpt is shown to be optimal using Markovian decision process stopping theory. Since the computational complexity of MultiOpt can be significant, simpler and more computationally efficient heuristics, which also respect the hard task execution deadline, may be used. The paper introduces two such heuristics, the Immediate offloading, and Multi Threshold algorithms. The mobile energy use of MultiOpt is compared to these heuristics, as well as to local execution without offloading and an offline energy bound. Simulation results show that MultiOpt performs significantly better when compared to the proposed heuristics, as well as when K increases.

关键词： Cloud computing mobile computation offloading Energy efficiency mobile task execution performance Hard job deadline constraints

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Collaborative mobile computation offloading to Vehicle-Based Cloudlets

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第1期70卷 768-781页

作者： Wang, Zhe Zhao, Dongmei Ni, Minming Li, Lu Li, Cheng China Acad Informat & Commun Technol Beijing 100191 Peoples R China McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4K1 Canada Ciena Canada Inc Innovat Dr Ottawa ON K2K 0J2 Canada

This paper investigates collaborative computation offloading in a vehicular network. Although there are increasingly more smart vehicles on roads, a significant number of legacy vehicles that are not equipped with powerful computing devices are expected to exist for a long time. When mobile devices located in these legacy vehicles require computation offloading, they can offload the tasks to nearby smart vehicles that are available to serve as cloudlet servers. Due to high mobility of the vehicles, multiple tasks of an application may have to be offloaded to different vehicle-based cloudlets. The offloading problem is formulated as a Markov decision process (MDP) by considering the randomness of the vehicle moving speeds and wireless channel conditions. The objective is to minimize the average completion time of the application. The complexity for solving the problem directly, however, is prohibitively high due to the large size of the state space and state transition probability matrix. The problem is solved by exploring the special structure of the state space, which helps reduce the computational complexity. A heuristic solution, namely, site-by-site and task-by-task (SSTT), is then proposed that makes the offloading decisions for individual tasks with much lower complexity. Simulation results show that the proposed SSTT solution not only achieves much lower average completion time, compared to executing all tasks locally and using distance-based offloading decisions, but also significantly reduces the energy consumption of the mobile device.

关键词： Task analysis Cloud computing Servers mobile handsets Roads Complexity theory Space vehicles Markov decision process mobile computation offloading vehicle-based cloudlets vehicular networks

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Energy-Efficient and Delay-Fair mobile computation offloading

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第12期69卷 15746-15759页

作者： Mu, Siqi Zhong, Zhangdui Zhao, Dongmei Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L8 Canada

mobile edge computing has been regarded as a new paradigm to achieve mobile computation offloading (MCO), which enables resource-limited mobile devices (MDs) to offload part or all of computation-intensive applications to more powerful computing entities. In this work we study MCO for data partitioned oriented applications. This type of services are usually delay-elastic, but shorter processing delay helps improve the user experience. We consider energy consumption of MDs and latency fairness of the applications. The latency fairness is achieved by allowing multiple offloading MDs to share one channel and allocating a fraction of the channel time for each application, and minimizing total energy consumption of the MDs is achieved by jointly optimizing the offloading ratio, channel assignments, and channel time allocations. For a special case that assigns at most one MD to each channel, a closed-form offloading ratio is derived and optimal channel assignment is obtained through transforming the problem into a weighted bipartite matching. The general problem is then solved by embedding the branch-and-bound iterations with successive convex approximation that solves a series of geometric programming problems. To reduce the complexity, a two-layer recursive method is then proposed that finds the channel assignments, offloading ratio and channel time allocation for each MD. Simulation results demonstrate that the proposed method can achieve low energy consumption and high latency fairness;and much lower energy consumption can be achieved with a slight decrease in latency fairness.

关键词： mobile handsets Energy consumption Servers Resource management Channel allocation Wireless communication Programming Bipartite matching branch-and-bound geometric programming latency fairness mobile computation offloading wireless resources allocation

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Optimal mobile computation offloading with Hard Deadline Constraints

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IEEE TRANSACTIONS ON mobile COMPUTING 2020年第9期19卷 2160-2173页

作者： Hekmati, Arvin Teymoori, Peyvand Todd, Terence D. Zhao, Dongmei Karakostas, George McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L8 Canada McMaster Univ Dept Comp & Software Hamilton ON L8S 4L8 Canada

This paper considers mobile computation offloading where task completion times are subject to hard deadline constraints. Hard deadlines are difficult to meet in conventional computation offloading due to the stochastic nature of the wireless channels involved. Rather than using binary offload decisions, we permit concurrent remote and local job execution when it is needed to ensure task completion deadlines. The paper addresses this problem for homogeneous Markovian wireless channel models. An online energy-optimal computation offloading algorithm, OnOpt, is proposed. Its energy optimality is shown by constructing a time-dilated absorbing Markov process and applying dynamic programming. Closed form results are derived for general Markovian processes, and the Gilbert-Elliott channel model is used to show how the particular structure of the Markov chain can be exploited in computing optimal offload initiation times more efficiently. It is shown that job completion time probabilities can be computed recursively, which leads to a significant reduction in the computational complexity of OnOpt. The performance of the proposed algorithm is compared to three others, namely, Immediate offloading, Channel Threshold, and Local Execution. Performance results show that the proposed algorithm can significantly improve mobile device energy consumption compared to the other approaches while guaranteeing hard task execution deadlines.

关键词： Cloud computing Task analysis Wireless communication mobile handsets Channel models Performance evaluation Heuristic algorithms Cloud computing mobile computation offloading energy efficiency mobile task execution performance hard job deadline constraints

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Preemptive mobile computation offloading With Hard Deadlines and Concurrent Local Execution

IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING

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IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING 2021年第2期5卷 950-963页

作者： Teymoori, Peyvand Hekmati, Arvin Todd, Terence D. Zhao, Dongmei Karakostas, George McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L Canada Univ Southern Calif Dept Comp Sci Los Angeles CA 90007 USA McMaster Univ Dept Comp & Software Hamilton ON L8S 4K1 Canada

This paper considers Preemptive mobile computation offloading when concurrent local execution (CLE) is used to guarantee task execution time constraints. By allowing simultaneous local and remote execution, CLE ensures that job deadlines are always satisfied in the face of unforeseen wireless channel conditions. In the preemptive offloading case, at the start of each time slot, a decision is made to either continue or temporarily interrupt the offload. This mechanism allows the system to adapt when channel conditions change. The paper considers the case for homogeneous Markovian wireless channels. Using Markovian decision process stopping theory, an online energy-optimal computation offloading algorithm is formulated for preemptive offloading, referred to as Optimal Preemptive offloading (OPO). Since the computational complexity of OPO can be prohibitive, the paper introduces three computationally efficient techniques motivated by OPO: 1) water-filling;2) water-filling with scheduling;and 3) generalized water-filling. For each method, two variations are considered. The first (Equ) uses the equilibrium channel state probabilities in offloading decision calculations, and the second (Exp) uses Markovian transition matrix exponentiation. This results in six algorithms with a wide variety of energy performance and computational complexity. Performance of the algorithms is compared that shows the tradeoffs between complexity and mobile energy saving performance.

关键词： Task analysis Wireless communication mobile handsets Processor scheduling Servers Scheduling Time factors Cloud computing mobile computation offloading energy efficiency mobile task execution performance hard job deadline constraints

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Vehicle-Based Cloudlet Relaying for mobile computation offloading

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2018年第11期67卷 11181-11191页

作者： Wang, Zhe Zhong, Zhangdui Zhao, Dongmei Ni, Minming Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Beijing Jiaotong Univ Beijing Engn Res Ctr High Speed Railway Broadband Beijing 100044 Peoples R China McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4K1 Canada

This paper studies computation offloading for mobile users in legacy vehicles that are not equipped with powerful computing devices. While running a time-sensitive and computation-intensive application on a mobile device (MD) of these users, the MD may offload part or all the applications to a nearby smart vehicle that can serve as a cloudlet. This provides a possibility to complete the application in time while saving the battery energy of the MD. However, high mobility of the vehicles causes short-lived communication links between the vehicles. As a result, the MD may have to offload tasks to a new cloudlet each time it lases the connection to the current cloudlet. In this paper, we propose a vehicle-based cloudlet relaying (VCR) scheme for mobile computation offloading. The objective is to effectively utilize the computation resources available in surrounding smart vehicles of the MD in the highly dynamic network environment, where each vehicle-based cloudlet may only be available to help the MD execute a small portion of the application. Instead of offloading tasks directly from the MD to individual cloudlets, which may consume high power and a long time, especially when the link just becomes available or is about to disconnect, we propose computation relaying that allows the current executing cloudlet to offload tasks to the next executing cloudlet on behalf of the MD. A set of conditions are established to enable the inter-cloudlet offloading so that the computation relaying can be achieved between the vehicle cloudlets, while the results can be returned to the MD. In addition, the computation relaying is always backed up by direct offloading between the MD and individual cloudlets. An algorithm is proposed to find the time to switch to a new cloudlet and select the best cloudlet as the next execution site or return the execution back to the MD, in order to minimize the energy consumption of the MD while satisfying the delay constraint of the application. Exte

关键词： mobile computation offloading vehicular networks cloudlet relaying

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Wireless and Service Allocation for mobile computation offloading With Task Deadlines

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IEEE TRANSACTIONS ON mobile COMPUTING 2024年第5期23卷 5054-5068页

作者： Chen, Hong Todd, Terence D. Zhao, Dongmei Karakostas, George McMaster Univ Dept Elect Comp Engn Hamilton ON L8S 4L8 Canada McMaster Univ Dept Comp Software Hamilton ON L8S 4L8 Canada

In mobile computation offloading (MCO), mobile devices (MDs) can choose to either execute tasks locally or have them executed on a remote edge server (ES). This paper addresses the problem of assigning the wireless communication bandwidth and the ES capacity used for the task execution, so that task completion time constraints are satisfied. The objective is to minimize the average power consumption of the mobile devices, subject to a cost budget constraint for obtaining the communication and computation resources. The paper includes contributions for both soft and hard task completion deadline constraints. The problems are first formulated as mixed integer nonlinear programs (MINLPs). Approximate solutions are then obtained by decomposing the problems into a collection of convex subproblems that can be efficiently solved. Results are presented that demonstrate the quality of the proposed solutions, which can achieve near optimum performance over a wide range of system parameters.

关键词： Task analysis Wireless communication Servers Delays Costs Power demand Optimization Cost budget constraints mobile computation offloading power efficiency soft and hard task completion deadlines edge computing

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Energy Efficient mobile computation offloading through Workload Migration

Energy Efficient Mobile Computation Offloading through Workl...

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2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

作者： Gao, Chengxi Lee, Victor C. S. City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781509018932

The popularity of mobile cloud computing has provisioned a new paradigm for mobile devices to offload their computation to remote mobile cloud data center for task execution. However, the mobile cloud data center may suffer from dramatic energy consumption. To solve this problem, we propose to migrate the workload from remote cloud data center to nearby cloudlets, so as to relieve the pressure of cloud data center and save the energy consumption. Specifically, we propose a lightweight and decentralized algorithm based on the Alternating Direction Method of Multipliers (ADMM) algorithm to construct the migration scheme. Simulations show that our algorithm can fast converge in tens of iterations, and decrease the overall energy consumption.

关键词： Energy efficiency mobile computation offloading Workload migration Decentralized algorithm Cloudlets

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Optimal Multi-Decision mobile computation offloading With Hard Task Deadlines

Optimal Multi-Decision Mobile Computation Offloading With Ha...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Hekmati, Arvin Teymoori, Peyvand Todd, Terence D. Zhao, Dongmei Karakostas, George McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada McMaster Univ Dept Comp & Software Hamilton ON Canada

ISBN: (纸本)9781728129990

Multi-decision mobile computation offloading occurs when a task to be remotely executed is uploaded in separate parts. Since the upload is partitioned, separate decisions are needed to determine the best time to initiate each upload. The multi-decision problem is considered for the case where execution completion times are subject to hard deadline constraints and where task offloads occur over a Markovian wireless channel. An online energy-optimal computation offloading algorithm, MultiOpt (Multi-decision online Optimum), is introduced, whose optimality is proven using Markovian stopping theory. The paper presents results using the Gilbert-Elliott channel model, where task completion time probabilities can be efficiently computed using Dynamic Programming. Although the proposed algorithm is proven to be energy optimal, its performance is also compared to four others, namely, Immediate offloading, Channel Threshold, Local Execution, as well as optimal single-part offloading. Results show that the proposed algorithm can significantly improve mobile device energy consumption compared to the other approaches while guaranteeing hard task execution deadlines.

关键词： Green wireless communications cloud computing mobile computation offloading energy efficiency hard task deadline constraints

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