A computational field framework for collaborative task execution in volunteer clouds

The increasing diffusion of cloud technologies offers new opportunities for distributed and collaborative computing. Volunteer clouds are a prominent example, where participants join and leave the platform and collaborate by sharing computational resources. The high complexity, dynamism and unpredictability of such scenarios call for decentralized self-* approaches. We present in this paper a framework for the design and evaluation of self-adaptive collaborative task execution strategies in volunteer clouds. As a byproduct, we propose a novel strategy based on the Ant Colony Optimization paradigm, that we validate through simulation-based statistical analysis over Google cluster data.

[1]  Zbigniew Michalewicz,et al.  Evolutionary Computation 1 , 2018 .

[2]  Jarke J. van Wijk,et al.  A user study on visualizing directed edges in graphs , 2009, CHI.

[3]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[4]  Raffaela Mirandola,et al.  On exploiting decentralized bio-inspired self-organization algorithms to develop real systems , 2009, 2009 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems.

[5]  Rocco De Nicola,et al.  A Formal Approach to Autonomic Systems Programming: The SCEL Language , 2014, TAAS.

[6]  Daniel Stutzbach,et al.  Understanding churn in peer-to-peer networks , 2006, IMC '06.

[7]  Al-Dahoud Ali,et al.  Multiple Ant Colonies Optimization for Load Balancing in Distributed Systems , 2007 .

[8]  Chris Rose,et al.  A Break in the Clouds: Towards a Cloud Definition , 2011 .

[9]  Corrado Santoro,et al.  A peer‐to‐peer decentralized strategy for resource management in computational Grids , 2007, Concurr. Comput. Pract. Exp..

[10]  Dan Wang,et al.  Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization , 2011, 2011 Sixth Annual Chinagrid Conference.

[11]  Chita R. Das,et al.  Towards characterizing cloud backend workloads: insights from Google compute clusters , 2010, PERV.

[12]  Michele Amoretti,et al.  Simulating mobile and distributed systems with DEUS and ns-3 , 2013, 2013 International Conference on High Performance Computing & Simulation (HPCS).

[13]  David P. Anderson,et al.  BOINC: a system for public-resource computing and storage , 2004, Fifth IEEE/ACM International Workshop on Grid Computing.

[14]  Al-Dahoud Ali,et al.  Load Balancing of Distributed Systems Based on Multiple Ant Colonies Optimization , 2010 .

[15]  Alberto Lluch-Lafuente,et al.  A Cooperative Approach for Distributed Task Execution in Autonomic Clouds , 2013, 2013 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing.

[16]  Douglas Thain,et al.  Distributed computing in practice: the Condor experience , 2005, Concurr. Pract. Exp..

[17]  Francesco Tiezzi,et al.  Reputation-Based Cooperation in the Clouds , 2014, IFIPTM.

[18]  Yong Zhao,et al.  Cloud Computing and Grid Computing 360-Degree Compared , 2008, GCE 2008.

[19]  Wonho Kim,et al.  Understanding and characterizing PlanetLab resource usage for federated network testbeds , 2011, IMC '11.

[20]  Michele Amoretti,et al.  DEUS: a discrete event universal simulator , 2009, SimuTools.

[21]  Corrado Santoro,et al.  A peer-to-peer decentralized strategy for resource management in computational Grids: Research Articles , 2007 .

[22]  Fabián E. Bustamante,et al.  Friendships that Last: Peer Lifespan and its Role in P2P Protocols , 2003, WCW.

[23]  Ratan Mishra,et al.  Ant colony Optimization: A Solution of Load balancing in Cloud , 2012 .

[24]  Alexandru Iosup,et al.  The Grid Workloads Archive , 2008, Future Gener. Comput. Syst..

[25]  Stefano Sebastio,et al.  MultiVeStA: statistical model checking for discrete event simulators , 2013, VALUETOOLS.

[26]  Antonio Puliafito,et al.  Volunteer Computing and Desktop Cloud: The Cloud@Home Paradigm , 2009, 2009 Eighth IEEE International Symposium on Network Computing and Applications.

[27]  Franco Zambonelli,et al.  Spatial Computing: An Emerging Paradigm for Autonomic Computing and Communication , 2004, WAC.

[28]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[29]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[30]  Francisco Vilar Brasileiro,et al.  Bridging the High Performance Computing Gap: the OurGrid Experience , 2007, Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07).

[31]  Kwang Mong Sim,et al.  Ant colony optimization for routing and load-balancing: survey and new directions , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[32]  Domenico Talia,et al.  Cloud Computing and Software Agents: Towards Cloud Intelligent Services , 2011, WOA.

[33]  Ivan Beschastnikh,et al.  Seattle: a platform for educational cloud computing , 2009, SIGCSE '09.

[34]  Francesco Tiezzi,et al.  The Autonomic Cloud: A Vision of Voluntary, Peer-2-Peer Cloud Computing , 2013, 2013 IEEE 7th International Conference on Self-Adaptation and Self-Organizing Systems Workshops.

[35]  David P. Anderson,et al.  SETI@home: an experiment in public-resource computing , 2002, CACM.

[36]  George Pavlou,et al.  A toolchain for simplifying network simulation setup , 2013, SimuTools.

[37]  Gianni A. Di Caro,et al.  AntNet: A Mobile Agents Approach to Adaptive Routing , 1999 .