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CLC number: TP393

On-line Access: 2015-07-06

Received: 2014-11-28

Revision Accepted: 2015-06-07

Crosschecked: 2015-06-12

Cited: 2

Clicked: 4352

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Hong-wu Lv

http://orcid.org/0000-0002-1917-3978

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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.7 P.553-567

http://doi.org/10.1631/FITEE.1400410


Analyzing the service availability of mobile cloud computing systems by fluid-flow approximation


Author(s):  Hong-wu Lv, Jun-yu Lin, Hui-qiang Wang, Guang-sheng Feng, Mo Zhou

Affiliation(s):  College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China

Corresponding email(s):   lvhongwu@hrbeu.edu.cn

Key Words:  Service availability, Mobile cloud computing, Fluid-flow approximation, Ordinary differential equations


Hong-wu Lv, Jun-yu Lin, Hui-qiang Wang, Guang-sheng Feng, Mo Zhou. Analyzing the service availability of mobile cloud computing systems by fluid-flow approximation[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(7): 553-567.

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Abstract: 
mobile cloud computing (MCC) has become a promising technique to deal with computation- or data-intensive tasks. It overcomes the limited processing power, poor storage capacity, and short battery life of mobile devices. Providing continuous and on-demand services, MCC argues that the service must be available for users at anytime and anywhere. However, at present, the service availability of MCC is usually measured by some certain metrics of a real-world system, and the results do not have broad representation since different systems have different load levels, different deployments, and many other random factors. Meanwhile, for large-scale and complex types of services in MCC systems, simulation-based methods (such as Monte-Carlo simulation) may be costly and the traditional state-based methods always suffer from the problem of state-space explosion. In this paper, to overcome these shortcomings, fluid-flow approximation, a breakthrough to avoid state-space explosion, is adopted to analyze the service availability of MCC. Four critical metrics, including response time of service, minimum sensing time of devices, minimum number of nodes chosen, and action throughput, are defined to estimate the availability by solving a group of ordinary differential equations even before the MCC system is fully deployed. Experimental results show that our method costs less time in analyzing the service availability of MCC than the Markov- or simulation-based methods.

This paper addresses an important problem on the availability of the mobile cloud computing. The authors propose an availability analysis method of MCC based on fluid-flow approximation.

基于流近似的移动云计算系统服务可用性分析方法

目的:为提供持续性的按需服务,移动云计算系统必须确保在任何时间和任何地点的可用性。然而当系统规模巨大、关联关系复杂时,如何实现移动云计算系统可用性的快速分析,是本领域一项富有挑战性的工作。本文目的是利用最近提出的流近似(fluid-flow approximation)技术来实现一种能应用于移动云计算系统部署之前的、快速的服务可用性分析方法。
创新点:由于移动云计算系统负载水平不同、配置部署不同和随机干扰因素,基于实测的方法很难具有代表性;基于随机模拟的方法会随着模拟规模增大和精度提升而计算时间剧增;基于状态空间的方法在系统规模巨大时将面临严重的状态空间爆炸问题。本文方法通过将状态空间转化为常微分方程组求解,可以避免状态空间爆炸,实现移动云计算系统可用性的快速分析。
方法:定义了包括服务反应时间(response time of service)、节点最小感知时间(minimum sensing time of devices)、最少选取节点数量(minimum number of nodes chosen)、动作吞吐量(action throughput)等四个关键指标。通过上述指标来分析移动云计算系统服务可用性的变化,并对系统初始条件、模型核心参数的影响进行讨论。
结论:本文提出的服务可用性分析方法能够适用于移动云计算系统完全部署之前,可以用于系统设计阶段的改进。并且与基于随机模拟方法和状态空间方法相比,时耗更低。

关键词:服务可用性;移动云计算;流近似;常微分方程

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