A multi-dimensional job scheduling

With the advent of new computing technologies, such as cloud computing and contemporary parallel processing systems, the building blocks of computing systems have become multi-dimensional. Traditional scheduling systems based on a single-resource optimization, like processors, fail to provide near optimal solutions. The efficient use of new computing systems depends on the efficient use of several resource dimensions. Thus, the scheduling systems have to fully use all resources. In this paper, we address the problem of multi-resource scheduling via multi-capacity bin-packing. We propose the application of multi-capacity-aware resource scheduling at host selection layer and queuing mechanism layer of a scheduling system. The experimental results demonstrate performance improvements of scheduling in terms of waittime and slowdown metrics. A proposal for scheduling problem based on multi-capacity bin-packing algorithms.A proposal for host selection and queuing based on multi-resource scheduling.Getting better waittime and slowdown metrics than the state of the art scheduling.

[1]  Akshat Verma,et al.  pMapper: Power and Migration Cost Aware Application Placement in Virtualized Systems , 2008, Middleware.

[2]  Henri Casanova,et al.  Resource allocation algorithms for virtualized service hosting platforms , 2010, J. Parallel Distributed Comput..

[3]  Rina Panigrahy,et al.  Heuristics for Vector Bin Packing , 2011 .

[4]  Deshi Ye,et al.  Non-cooperative games on multidimensional resource allocation , 2013, Future Gener. Comput. Syst..

[5]  Anand Sivasubramaniam,et al.  Managing server energy and operational costs in hosting centers , 2005, SIGMETRICS '05.

[6]  Bernd Freisleben,et al.  Energy-Efficient Virtual Machine Consolidation , 2013, IT Professional.

[7]  Paolo Cremonesi,et al.  A Constraint Programming Approach for the Service Consolidation Problem , 2010, CPAIOR.

[8]  Chak-Kuen Wong,et al.  An effective quasi-human based heuristic for solving the rectangle packing problem , 2002, Eur. J. Oper. Res..

[9]  Lucio Grandinetti,et al.  Energy Aware Consolidation Policies , 2011, PARCO.

[10]  Rajkumar Buyya,et al.  Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing , 2012, Future Gener. Comput. Syst..

[11]  Edward G. Coffman,et al.  An Application of Bin-Packing to Multiprocessor Scheduling , 1978, SIAM J. Comput..

[12]  Michela Meo,et al.  Probabilistic Consolidation of Virtual Machines in Self-Organizing Cloud Data Centers , 2013, IEEE Transactions on Cloud Computing.

[13]  Aameek Singh,et al.  Shares and utilities based power consolidation in virtualized server environments , 2009, 2009 IFIP/IEEE International Symposium on Integrated Network Management.

[14]  Vipin Kumar,et al.  Multi-capacity bin packing algorithms with applications to job scheduling under multiple constraints , 1999, Proceedings of the 1999 International Conference on Parallel Processing.

[15]  Henri Casanova,et al.  Dynamic Fractional Resource Scheduling versus Batch Scheduling , 2012, IEEE Transactions on Parallel and Distributed Systems.

[16]  Andrew Chi-Chih Yao,et al.  Resource Constrained Scheduling as Generalized Bin Packing , 1976, J. Comb. Theory A.

[17]  Lucio Grandinetti,et al.  Autonomic resource contention‐aware scheduling , 2015, Softw. Pract. Exp..

[18]  Xavier Lorca,et al.  Entropy: a consolidation manager for clusters , 2009, VEE '09.

[19]  Edward G. Coffman,et al.  Dynamic Bin Packing , 1983, SIAM J. Comput..

[20]  César A. F. De Rose,et al.  Server consolidation with migration control for virtualized data centers , 2011, Future Gener. Comput. Syst..

[21]  Lucio Grandinetti,et al.  A Multi-capacity Queuing Mechanism in Multi-dimensional Resource Scheduling , 2014, ARMS-CC@PODC.

[22]  Dang Minh Quan,et al.  Energy Efficient Resource Allocation Strategy for Cloud Data Centres , 2011, ISCIS.

[23]  Andrzej Kochut,et al.  Dynamic Placement of Virtual Machines for Managing SLA Violations , 2007, 2007 10th IFIP/IEEE International Symposium on Integrated Network Management.

[24]  Larry Rudolph,et al.  Metrics and Benchmarking for Parallel Job Scheduling , 1998, JSSPP.

[25]  Albert Y. Zomaya,et al.  Hopfield neural network for simultaneous job scheduling and data replication in grids , 2013, Future Gener. Comput. Syst..

[26]  Jemal H. Abawajy,et al.  An efficient adaptive scheduling policy for high-performance computing , 2009, Future Gener. Comput. Syst..

[27]  Wolfgang Nebel,et al.  Behavioral model for cloud aware load and power management , 2013, HotTopiCS '13.

[28]  Dmytro Dyachuk,et al.  Maximizing Cloud Providers' Revenues via Energy Aware Allocation Policies , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[29]  Ronald L. Graham,et al.  Bounds for Multiprocessor Scheduling with Resource Constraints , 1975, SIAM J. Comput..

[30]  Sameep Mehta,et al.  ReCon: A tool to Recommend dynamic server Consolidation in multi-cluster data centers , 2008, NOMS 2008 - 2008 IEEE Network Operations and Management Symposium.