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Special Issue on Cloud Computing
J. Parallel Distrib. Comput. 71 (2011) 731
Contents lists available at ScienceDirect
J. Parallel Distrib. Comput. journal homepage: www.elsevier.com/locate/jpdc
Editorial
Special Issue on Cloud Computing Cloud computing has emerged as an extremely popular and cost-effective model for the on-demand provisioning of virtualized resources as a service. Major commercial Cloud providers are supported by large collections of physical resources that are spread over geographically distributed data centers. The unprecedented scale of such environments raises new challenging research issues in organizing and managing the physical resources, especially in trying to explore their efficient use in supporting compute and data intensive scientific applications. This special issue focuses on a number of research questions dealing with enabling parallel and distributed computing within a cloud environment. More specifically, the three papers appearing in this special issue deal with (i) energy-efficient policies to schedule resources in a distributed cloud environment; (ii) the design of virtual cluster computing environments to address the needs of high throughput workloads; and (iii) the development of scheduling policies to bridge the semantic gap in CPU management in a virtualized environment. Out of the fifteen papers submitted to this special issue, only three papers were accepted. The papers were subject to a rigorous refereeing process. The first paper, ‘‘Environment-Conscious Scheduling of HPC Applications on Distributed Cloud-oriented Data Centers,’’ by Saurabh Kumar Garg et al., addresses the critical issue of efficient energy management in a cloud environment using geographically distributed data centers. The authors propose near-optimal scheduling policies that exploit the characteristics of the various data centers for a Cloud provider. Their model includes a number of factors such as energy cost, carbon emission rate, workload, and CPU power efficiency, which are expected to vary significantly among the data centers. The authors show that their scheduling policies can achieve on average up to 25% energy savings compared to policies focusing on maximizing profit. The second paper, ‘‘An Elasticity Model for High Throughput Computing Clusters,’’ by Ruben S. Montero et al., proposes a virtual computing cluster framework that can provide cluster consolidation and partition, and support for heterogeneous environments. The main goal is to enable the efficient execution of High Throughput Computing workloads on such virtual clusters.
0743-7315/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.jpdc.2011.04.001
The authors also introduce a performance model to characterize cluster environments. This model can be used to dynamically configure the cluster to use cloud resources under a fixed budget. The third paper, ‘‘Transparently Bridging Semantic Gap in CPU Management for Virtualized Environments,’’ by Hwanju Kim et al., addresses the problems encountered in trying to bridge the gap between the Virtual Machine Monitor (VMM) and the guest Operating Systems. The main difficulty lies in the widely diverse and unpredictable workloads that are supposed to be managed by the VMM. The authors present scheduling techniques for bridging the semantic gap by ensuring that the VMM is aware of tasklevel I/O requirements inside a virtual machine. They also address performance anomalies arising from indirect use of I/O devices through a driver virtual machine at the scheduling level. Guest Editors Dr. Gregory Chockler Dr. Eliezer Dekel IBM Research - Haifa, Haifa University Campus, Mount Carmel, 31905 Haifa, Israel E-mail addresses: [email protected] (G. Chockler), [email protected] (E. Dekel). Dr. Joseph JaJa Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742, United States E-mail address: [email protected]. Dr. Jimmy Lin College of Information Studies, University of Maryland, College Park, MD 20742, United States E-mail address: [email protected]. Available online 27 April 2011
Contents lists available at ScienceDirect
J. Parallel Distrib. Comput. journal homepage: www.elsevier.com/locate/jpdc
Editorial
Special Issue on Cloud Computing Cloud computing has emerged as an extremely popular and cost-effective model for the on-demand provisioning of virtualized resources as a service. Major commercial Cloud providers are supported by large collections of physical resources that are spread over geographically distributed data centers. The unprecedented scale of such environments raises new challenging research issues in organizing and managing the physical resources, especially in trying to explore their efficient use in supporting compute and data intensive scientific applications. This special issue focuses on a number of research questions dealing with enabling parallel and distributed computing within a cloud environment. More specifically, the three papers appearing in this special issue deal with (i) energy-efficient policies to schedule resources in a distributed cloud environment; (ii) the design of virtual cluster computing environments to address the needs of high throughput workloads; and (iii) the development of scheduling policies to bridge the semantic gap in CPU management in a virtualized environment. Out of the fifteen papers submitted to this special issue, only three papers were accepted. The papers were subject to a rigorous refereeing process. The first paper, ‘‘Environment-Conscious Scheduling of HPC Applications on Distributed Cloud-oriented Data Centers,’’ by Saurabh Kumar Garg et al., addresses the critical issue of efficient energy management in a cloud environment using geographically distributed data centers. The authors propose near-optimal scheduling policies that exploit the characteristics of the various data centers for a Cloud provider. Their model includes a number of factors such as energy cost, carbon emission rate, workload, and CPU power efficiency, which are expected to vary significantly among the data centers. The authors show that their scheduling policies can achieve on average up to 25% energy savings compared to policies focusing on maximizing profit. The second paper, ‘‘An Elasticity Model for High Throughput Computing Clusters,’’ by Ruben S. Montero et al., proposes a virtual computing cluster framework that can provide cluster consolidation and partition, and support for heterogeneous environments. The main goal is to enable the efficient execution of High Throughput Computing workloads on such virtual clusters.
0743-7315/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.jpdc.2011.04.001
The authors also introduce a performance model to characterize cluster environments. This model can be used to dynamically configure the cluster to use cloud resources under a fixed budget. The third paper, ‘‘Transparently Bridging Semantic Gap in CPU Management for Virtualized Environments,’’ by Hwanju Kim et al., addresses the problems encountered in trying to bridge the gap between the Virtual Machine Monitor (VMM) and the guest Operating Systems. The main difficulty lies in the widely diverse and unpredictable workloads that are supposed to be managed by the VMM. The authors present scheduling techniques for bridging the semantic gap by ensuring that the VMM is aware of tasklevel I/O requirements inside a virtual machine. They also address performance anomalies arising from indirect use of I/O devices through a driver virtual machine at the scheduling level. Guest Editors Dr. Gregory Chockler Dr. Eliezer Dekel IBM Research - Haifa, Haifa University Campus, Mount Carmel, 31905 Haifa, Israel E-mail addresses: [email protected] (G. Chockler), [email protected] (E. Dekel). Dr. Joseph JaJa Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742, United States E-mail address: [email protected]. Dr. Jimmy Lin College of Information Studies, University of Maryland, College Park, MD 20742, United States E-mail address: [email protected]. Available online 27 April 2011