Preference Based and Fair Resources Selection in Grid VOs

In this work, a preference-based resources allocation algorithm for a job-flow scheduling in Grid virtual organizations (VOs) is proposed and studied. Users’ and resource providers’ preferences, VOs internal policies, resources geographical distribution along with local private utilization impose specific requirements for efficient scheduling according to different, usually contradictive, criteria. The algorithm performs resources selection optimization according to a specified general criterion and may be used in a variety of scheduling procedures, such as Backfilling or First Fit. Fair scheduling policies in VOs assume resources distribution according to VO stakeholders individual preferences. For this purpose, we consider a target optimization criterion as a linear combination of global (group) and private (user) job scheduling criteria. The mutual importance factor between the private and the global criteria is introduced to achieve a balanced scheduling solution.

[1]  Victor V. Toporkov,et al.  Cyclic Anticipation Scheduling in Grid VOs with Stakeholders Preferences , 2017, PaCT.

[2]  Anthony A. Maciejewski,et al.  Resource Management in Heterogeneous Parallel Computing Environments with Soft and Hard Deadlines , 2015 .

[3]  Rajkumar Buyya,et al.  Fair resource sharing in hierarchical virtual organizations for global grids , 2007, 2007 8th IEEE/ACM International Conference on Grid Computing.

[4]  Daniel Grosu,et al.  Divisible Load Scheduling: An Approach Using Coalitional Games , 2007, Sixth International Symposium on Parallel and Distributed Computing (ISPDC'07).

[5]  Adam Wierzbicki,et al.  Fair Game-Theoretic Resource Management in Dedicated Grids , 2007, Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07).

[6]  Helen D. Karatza,et al.  Job Scheduling in a Distributed System Using Backfilling with Inaccurate Runtime Computations , 2010, 2010 International Conference on Complex, Intelligent and Software Intensive Systems.

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

[8]  Valentin Cristea,et al.  Resource-aware hybrid scheduling algorithm in heterogeneous distributed computing , 2015, Future Gener. Comput. Syst..

[9]  Anthony T. Chronopoulos,et al.  Cost minimization in utility computing systems , 2014, Concurr. Comput. Pract. Exp..

[10]  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..

[11]  Dror G. Feitelson,et al.  Backfilling with lookahead to optimize the packing of parallel jobs , 2005, J. Parallel Distributed Comput..

[12]  Mark J. Clement,et al.  Core Algorithms of the Maui Scheduler , 2001, JSSPP.

[13]  Victor V. Toporkov,et al.  Slot selection algorithms in distributed computing , 2014, The Journal of Supercomputing.

[14]  Peter Merz,et al.  Agent-Based Grid Scheduling with Calana , 2005, PPAM.

[15]  David Abramson,et al.  Economic models for resource management and scheduling in Grid computing , 2002, Concurr. Comput. Pract. Exp..

[16]  Krzysztof Rzadca,et al.  Non-monetary fair scheduling: a cooperative game theory approach , 2013, SPAA.

[17]  Yoshio Tanaka,et al.  An Advance Reservation-Based Co-allocation Algorithm for Distributed Computers and Network Bandwidth on QoS-Guaranteed Grids , 2010, JSSPP.

[18]  Liana L. Fong,et al.  Enabling Interoperability among Grid Meta-Schedulers , 2013, Journal of Grid Computing.

[19]  Richard Wolski,et al.  Eliciting honest value information in a batch-queue environment , 2007, 2007 8th IEEE/ACM International Conference on Grid Computing.

[20]  Victor V. Toporkov,et al.  Heuristic strategies for preference-based scheduling in virtual organizations of utility grids , 2015, J. Ambient Intell. Humaniz. Comput..

[21]  Jarek Nabrzyski,et al.  Multicriteria aspects of Grid resource management , 2004 .