Energy-efficient multisite offloading policy using Markov decision process for mobile cloud computing

Terefe, Mati B.; Lee, Heezin; Heo, Nojung; Fox, Geoffrey; Oh, Sangyoon

doi:10.1016/j.pmcj.2015.10.008

Cited by 71 publications

(21 citation statements)

References 8 publications

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“…Terefe et al [47] have proposed a multi-site offloading algorithm and show that their energy-efficient multi-site offloading policy algorithm is capable of reducing energy consumption compared to its single-site computation offloading counterpart.…”

Section: Kristensen Et Al Have Proposed Scavengermentioning

confidence: 99%

Modeling Multi-Site Computation Offloading in Unreliable Cloud Environments

Pirbasti¹

2021

Preprint

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Offloading heavy computations from a mobile device to cloud servers can reduce the power consumption of the mobile device and improve the response time of mobile applications. However, the gains of offloading can be significantly affected by failures of cloud servers and network links. In this thesis, we propose a fault-aware, multi-site computation offloading model capable of finding efficient allocations of tasks to resources. Our model reduces both response time and energy consumption by incorporating the effect of failures and recovery mechanisms for various offloading allocations. In addition, we create a fault-injection framework to evaluate an allocation under various failure rates and recovery mechanisms. The experiments carried out with our fault-injection framework demonstrate that our fault-aware model can determine an allocation—based on the type of failures, failure rates, and the employed recovery mechanisms—that improves both response time and lower energy consumption compared to model without failures.

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Section: Kristensen Et Al Have Proposed Scavengermentioning

confidence: 99%

Modeling Multi-Site Computation Offloading in Unreliable Cloud Environments

Pirbasti¹

2021

Preprint

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“…Terefe et al [100] proposed a multi-site offloading strategy for mobile devices. By analyzing the data-intensive and computation-intensive modules of the application, they use a mathematical model to simulate the energy consumption of the multi-site application execution.…”

Section: B Heuristics Based Computation Offloadingmentioning

confidence: 99%

Toward Computation Offloading in Edge Computing: A Survey

et al. 2019

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The explosive growth of massive data generation from Internet of Things in industrial, agricultural and scientific communities has led to a rapid increase for data analytics in cloud data centers. The ubiquitous and pervasive demand for near-data processing urges the edge computing paradigm in recent years. Edge computing is promising for less network backbone bandwidth usage and thus less data center side processing pressure, as well as enhanced service responsiveness and data privacy protection. Computation offloading plays a crucial role in edge computing in terms of network packets transmission and system responsiveness through dynamic task partitioning between cloud data centers and edge servers and edge devices. In this paper a thorough literature review is conducted to reveal the state-of-the-art of computation offloading in edge computing. Various aspects of computation offloading, including energy consumption minimization, Quality of Services guarantee, and Quality of Experiences enhancement are surveyed. Moreover, resource scheduling approaches, gaming and tradeoffing among system performance and overheads for computation offloading decision making are also reviewed.INDEX TERMS Edge computing, computation offloading, task partitioning, game theory, edge-cloud collaboration.

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“…As is mentioned in [28], intermittent issues may due to the user mobility, device connection policy and dynamic changing network condition. Terefe et al [29] use a discrete time Markov chain (DTMC) to model fading wireless mobile channels. To solve limited battery problem, Chen et al [27] present a semi-Markov decision process (SMDP)-based optimization framework aimed for modulation scheme transmission bit rates and various DVFS levels, in order to minimize both the average latency and the energy drawn from the battery.…”

Section: 3mentioning

confidence: 99%

Online learning offloading framework for heterogeneous mobile edge computing system

Zhang

Wong

et al. 2019

Journal of Parallel and Distributed Computing

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Cloud of Things (CoT) is a significant paradigm for bridging cloud resource and mobile terminals. Mobile edge computing (MEC) is a supporting architecture for CoT. The objectives of this paper are to describe and evaluate a method to handle the computation offloading problem during user mobility which minimizes the offloading failure rate in heterogeneous network. Furthermore, users' mobility and their choices for offloading lead to the everchanging condition of wireless network and opportunistic resource available. By modeling such dynamic mobile edge environment, quantizing the user cost, failure penalty and diversified QoS requirements, computation offloading problem is converted into an online decision-making problem in a stochastic process. We divide the decisionmaking into two phases: offloading planning phase and offloading running phase. In both phases the learning agent can continuously improve the control policy. We also conduct a failure recovery policy to tackle different types of failure and is included in the decision-making process. The numerical results show that the proposed online learning offloading method for mobile users can derive the optimal offloading scheme compared with the baseline algorithms.

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Energy-efficient multisite offloading policy using Markov decision process for mobile cloud computing

Cited by 71 publications

References 8 publications

Modeling Multi-Site Computation Offloading in Unreliable Cloud Environments

Modeling Multi-Site Computation Offloading in Unreliable Cloud Environments

Toward Computation Offloading in Edge Computing: A Survey

Online learning offloading framework for heterogeneous mobile edge computing system

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