Int. J. of Big Data Intelligence   »   2015 Vol.2, No.4

 

 

Title: Energy-aware service provisioning in volunteers clouds

 

Authors: Yanik Ngoko; Christophe Cérin; Paolo Gianessi; Congfeng Jiang

 

Addresses:
Université de Paris 13, PRES Sorbonne Paris Cité, LIPN, UMR CNRS 7030, 99, avenue Jean-Baptiste Clément, 93430 Villetaneuse, France
Université de Paris 13, PRES Sorbonne Paris Cité, LIPN, UMR CNRS 7030, 99, avenue Jean-Baptiste Clément, 93430 Villetaneuse, France
Université de Paris 13, PRES Sorbonne Paris Cité, LIPN, UMR CNRS 7030, 99, avenue Jean-Baptiste Clément, 93430 Villetaneuse, France
Hangzhou Dianzi University, 1, 2nd Street, Xiasha Higher Education Zone, Hangzhou 310018, China

 

Abstract: The goal pursued in this study is to optimise the service provisioning and the energy consumed in a special existing cloud system named SlapOS. The SlapOS cloud innovates in considering that the data centre can be composed by dedicated and volunteer machines. We will use the term volunteer clouds for referring to such organisations. Volunteer clouds offer potential advantages for cloud elasticity, storage of big data and the minimisation of the energy consumption. But, we must manage the potential unavailability of volunteer machines. In this paper, we focus on the design of energy-efficient scheduling approaches in such clouds. Our first contribution is to formulate the scheduling challenges in two new computational problems. These problems are NP-hard. Then, we propose an integer linear programming (ILP) formulation and several greedy heuristics. Finally, we evaluate our approaches throughout various simulations of the SlapOS system in a realistic volunteer computing context.

 

Keywords: energy efficiency; volunteer clouds; desktop grid computing; assignment problems; integer programming; greedy heuristics; energy awareness; service provisioning; cloud computing; energy consumption; cloud elasticity; big data storage; energy-efficient scheduling; integer linear programming; ILP; simulation.

 

DOI: 10.1504/IJBDI.2015.072171

 

Int. J. of Big Data Intelligence, 2015 Vol.2, No.4, pp.262 - 284

 

Submission date: 20 May 2014
Date of acceptance: 17 Dec 2014
Available online: 02 Oct 2015

 

 

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