Optimizing Resource Consumptions in Clouds

This paper considers the scenario where multiple clusters of Virtual Machines (i.e., termed as Virtual Clusters) are hosted in a Cloud system consisting of a cluster of physical nodes. Multiple Virtual Clusters (VCs) cohabit in the physical cluster, with each VC offering a particular type of service for the incoming requests. In this context, VM consolidation, which strives to use a minimal number of nodes to accommodate all VMs in the system, plays an important role in saving resource consumption. Most existing consolidation methods proposed in the literature regard VMs as "rigid" during consolidation, i.e., VMs' resource capacities remain unchanged. In VC environments, QoS is usually delivered by a VC as a single entity. Therefore, there is no reason why VMs' resource capacity cannot be adjusted as long as the whole VC is still able to maintain the desired QoS. Treating VMs as being "mouldable" during consolidation may be able to further consolidate VMs into an even fewer number of nodes. This paper investigates this issue and develops a Genetic Algorithm (GA) to consolidate mouldable VMs. The GA is able to evolve an optimized system state, which represents the VM-to-node mapping and the resource capacity allocated to each VM. After the new system state is calculated by the GA, the Cloud will transit from the current system state to the new one. The transition time represents overhead and should be minimized. In this paper, a cost model is formalized to capture the transition overhead, and a reconfiguration algorithm is developed to transit the Cloud to the optimized system state at the low transition overhead. Experiments have been conducted in this paper to evaluate the performance of the GA and the reconfiguration algorithm.

[1]  Calton Pu,et al.  Generating Adaptation Policies for Multi-tier Applications in Consolidated Server Environments , 2008, 2008 International Conference on Autonomic Computing.

[2]  Richard Wolski,et al.  The Eucalyptus Open-Source Cloud-Computing System , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[3]  Hai Jin,et al.  Magnet: A novel scheduling policy for power reduction in cluster with virtual machines , 2008, 2008 IEEE International Conference on Cluster Computing.

[4]  T. Chiueh,et al.  A Survey on Virtualization Technologies , 2005 .

[5]  Rajkumar Buyya,et al.  Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities , 2008, 2008 10th IEEE International Conference on High Performance Computing and Communications.

[6]  Lakshmi Sobhana Kalli,et al.  Market-Oriented Cloud Computing : Vision , Hype , and Reality for Delivering IT Services as Computing , 2013 .

[7]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

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

[9]  Calton Pu,et al.  Mistral: Dynamically Managing Power, Performance, and Adaptation Cost in Cloud Infrastructures , 2010, 2010 IEEE 30th International Conference on Distributed Computing Systems.

[10]  Hui Wang,et al.  Multi-Tiered On-Demand Resource Scheduling for VM-Based Data Center , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[11]  Ítalo S. Cunha,et al.  Self-Adaptive Capacity Management for Multi-Tier Virtualized Environments , 2007, 2007 10th IFIP/IEEE International Symposium on Integrated Network Management.

[12]  Zhenlin Wang,et al.  Dynamic memory balancing for virtual machines , 2009, OPSR.

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

[14]  Ravi Iyer,et al.  Modeling virtual machine performance: challenges and approaches , 2010, PERV.

[15]  Jordi Torres,et al.  SLA-Driven Semantically-Enhanced Dynamic Resource Allocator for Virtualized Service Providers , 2008, 2008 IEEE Fourth International Conference on eScience.