Run Time Application Repartitioning in Dynamic Mobile Cloud Environments

Yang, Lei; Cao, Jiannong; Tang, Shaojie; Han, Di; Suri, Neeraj

doi:10.1109/tcc.2014.2358239

Cited by 46 publications

(26 citation statements)

References 16 publications

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“…MACS solves a new ILP problem to adapt to the new environment after the environmental parameters change. Yang et al designed an online solution named "Foreseer," which repartitions application at runtime in dynamic mobile cloud environments, to shorten the application completion time [27]. Foreseer predicts the network status periodically by users' historical mobility information and then updates the application partitions.…”

Section: Related Workmentioning

confidence: 99%

Cooperative Runtime Offloading Decision Algorithm for Mobile Cloud Computing

Jin

Wang

Hua

2019

Mobile Information Systems

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Mobile cloud computing (MCC) provides a platform for resource-constrained mobile devices to offload their tasks. MCC has the characteristics of cloud computing and its own features such as mobility and wireless data transmission, which bring new challenges to offloading decision for MCC. However, most existing works on offloading decision assume that mobile cloud environments are stable and only focus on optimizing the consumption of offloaded applications but ignore the consumption caused by offloading decision algorithms themselves. This paper focuses on runtime offloading decision in dynamic mobile cloud environments with the consideration of reducing the offloading decision algorithm’s consumption. A cooperative runtime offloading decision algorithm, which takes advantage of the cooperation of online machine learning and genetic algorithm to make offloading decisions, is proposed to address this problem. Simulations show that the proposed algorithm helps offloaded applications save more energy and time while consuming fewer computing resources.

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Section: Related Workmentioning

confidence: 99%

Cooperative Runtime Offloading Decision Algorithm for Mobile Cloud Computing

Jin

Wang

Hua

2019

Mobile Information Systems

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“…In practice, the bandwidth state, network status, and device status (such as CPU load) of the cloud platform may be different [37]. It will have a certain impact on migration performance.…”

Section: Energy Consumption Formulationmentioning

confidence: 99%

A Novel Resource Deployment Approach to Mobile Microlearning: From Energy-Saving Perspective

Zheng

Zhu

Zhang

et al. 2019

Wireless Communications and Mobile Computing

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Mobile Microlearning, a novel fusion form of the mobile Internet, cloud computing, and microlearning, becomes more prevalent in recent years. However, its high deployment and operational costs make energy saving in cloud become a concerning issue. In this paper, to save energy consumption, a resource deployment approach to cloud service provision for Mobile Microlearning is proposed. Chinese Lexical Analysis System and Dynamic Term Frequency-Inverse Document Frequency (D-TF-IDF) are adopted to implement resource classification. Resources are deployed to the 2-tier cloud architecture according to the classification results. Grey Wolf Optimization (GWO) algorithm is used to forecast real-time energy consumption per byte. The simulation results show that, compared to traditional algorithm, the classification accuracy of small sample categories was significantly improved; the forecast energy consumption value and the standard values are 7.67% in private cloud and 2.93% in public cloud; the energy saving reaches 2.22% to 16.23% in 3G and 7.35% to 20.74% in Wi-Fi.

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“…K, i.e., the maximum number of shortest paths that can be used in Anycast-JRE (EP), is 3. The VM bandwidth requirement ς i m is randomly selected from [2,20] Gbps, and the computing requirement ψ i m is randomly selected from [1,3] CPU cores. Parameters which are used for the evaluation are summarized in Table IV. We repeat the simulation 200 times.…”

Section: B Evaluations Of Large Scale Problemmentioning

confidence: 99%

Energy-Aware Virtual Machine Management in Inter-Datacenter Networks Over Elastic Optical Infrastructure

Zhang

Ansari

2018

IEEE Trans. on Green Commun. Netw.

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Datacenters (DCs), deployed in a large scale to support the ever increasing demand for data processing applications, consume tremendous energy. Powering DCs with renewable energy can effectively reduce the brown energy consumption. Owing to geographically distributed deployment of DCs, the renewable energy generation and the data processing demands usually vary in different DCs. Migrating virtual machines (VMs) among DCs according to the availability of renewable energy helps match the energy demands and the renewable energy generation in DCs, and thus maximizes the utilization of renewable energy. We first elicit the renewable energy-aware inter-datacenter (inter-DC) VM migration problem in an inter-DC network over the elastic optical infrastructure, present it as a many-manycast communications problem, and then formulate it as an integer linear programming problem. The objective is to minimize the total cost of the brown energy consumption of DCs in such inter-DC network via VM migration. We use CVX and Gurobi to solve this problem for small network configurations, and we propose a few heuristic algorithms that approximate the optimal solution for large network configurations. Through extensive simulations, we show that the proposed algorithms, by migrating VM among DCs, can reduce up to 19.7% cost of the brown energy consumption.The preliminary idea of this work was presented at IEEE

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Run Time Application Repartitioning in Dynamic Mobile Cloud Environments

Cited by 46 publications

References 16 publications

Cooperative Runtime Offloading Decision Algorithm for Mobile Cloud Computing

Cooperative Runtime Offloading Decision Algorithm for Mobile Cloud Computing

A Novel Resource Deployment Approach to Mobile Microlearning: From Energy-Saving Perspective

Energy-Aware Virtual Machine Management in Inter-Datacenter Networks Over Elastic Optical Infrastructure

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