Adaptive resource allocation for preemptable jobs in cloud systems

In cloud computing, computational resources are provided to remote users in the form of leases. For a cloud user, he/she can request multiple cloud services simultaneously. In this case, parallel processing in the cloud system can improve the performance. When applying parallel processing in cloud computing, it is necessary to implement a mechanism to allocate resource and schedule the tasks execution order. Furthermore, a resource allocation mechanism with preemptable task execution can increase the utilization of clouds. In this paper, we propose an adaptive resource allocation algorithm for the cloud system with preemptable tasks. Our algorithms adjust the resource allocation adaptively based on the updated of the actual task executions. And the experimental results show that our algorithms works significantly in the situation where resource contention is fierce.

[1]  Oscar H. Ibarra,et al.  Heuristic Algorithms for Scheduling Independent Tasks on Nonidentical Processors , 1977, JACM.

[2]  Omer F. Rana,et al.  The Convergence of Clouds, Grids, and Autonomics , 2009, IEEE Internet Comput..

[3]  Dongyan Xu,et al.  VioCluster: Virtualization for Dynamic Computational Domains , 2005, 2005 IEEE International Conference on Cluster Computing.

[4]  Bernd Freisleben,et al.  Xen and the Art of Cluster Scheduling , 2006, First International Workshop on Virtualization Technology in Distributed Computing (VTDC 2006).

[5]  Anthony A. Maciejewski,et al.  Static mapping of subtasks in a heterogeneous ad hoc grid environment , 2004, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings..

[6]  Jan Janecek,et al.  A high performance, low complexity algorithm for compile-time task scheduling in heterogeneous systems , 2004, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings..

[7]  Minyi Guo,et al.  Loop scheduling and bank type assignment for heterogeneous multi-bank memory , 2009, J. Parallel Distributed Comput..

[8]  Atakan Dogan,et al.  Matching and Scheduling Algorithms for Minimizing Execution Time and Failure Probability of Applications in Heterogeneous Computing , 2002, IEEE Trans. Parallel Distributed Syst..

[9]  Dongyan Xu,et al.  Autonomic Live Adaptation of Virtual Computational Environments in a Multi-Domain Infrastructure , 2006, 2006 IEEE International Conference on Autonomic Computing.

[10]  Meikang Qiu,et al.  Cost minimization while satisfying hard/soft timing constraints for heterogeneous embedded systems , 2009, TODE.

[11]  Borja Sotomayor,et al.  Virtual Infrastructure Management in Private and Hybrid Clouds , 2009, IEEE Internet Computing.

[12]  Borja Sotomayor,et al.  Resource Leasing and the Art of Suspending Virtual Machines , 2009, 2009 11th IEEE International Conference on High Performance Computing and Communications.

[13]  Dan Stanzione,et al.  Efficient virtual machine caching in dynamic virtual clusters , 2007 .

[14]  Borja Sotomayor,et al.  Combining batch execution and leasing using virtual machines , 2008, HPDC '08.