Efficient provisioning of bursty scientific workloads on the cloud using adaptive elasticity control
Proceedings of the 3rd workshop on Scientific Cloud Computing Date
Proceedings of the 2012 workshop on Cloud services, federation, and the 8th open cirrus summit
Heavy-traffic analysis of cloud provisioning
Proceedings of the 24th International Teletraffic Congress
Using Layered Bottlenecks for Virtual Machine Provisioning in the Clouds
UCC '12 Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing
A Representation Model for Virtual Machine Allocation
UCC '12 Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing
Future Generation Computer Systems
Hybrid modelling and simulation of huge crowd over a hierarchical Grid architecture
Future Generation Computer Systems
Self-adaptive and sensitivity-aware QoS modeling for the cloud
Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
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Cloud computing is the latest computing paradigm that delivers IT resources as services in which users are free from the burden of worrying about the low-level implementation or system administration details. However, there are significant problems that exist with regard to efficient provisioning and delivery of applications using Cloud-based IT resources. These barriers concern various levels such as workload modeling, virtualization, performance modeling, deployment, and monitoring of applications on virtualized IT resources. If these problems can be solved, then applications can operate more efficiently, with reduced financial and environmental costs, reduced under-utilization of resources, and better performance at times of peak load. In this paper, we present a provisioning technique that automatically adapts to workload changes related to applications for facilitating the adaptive management of system and offering end-users guaranteed Quality of Services (QoS) in large, autonomous, and highly dynamic environments. We model the behavior and performance of applications and Cloud-based IT resources to adaptively serve end-user requests. To improve the efficiency of the system, we use analytical performance (queueing network system model) and workload information to supply intelligent input about system requirements to an application provisioner with limited information about the physical infrastructure. Our simulation-based experimental results using production workload models indicate that the proposed provisioning technique detects changes in workload intensity (arrival pattern, resource demands) that occur over time and allocates multiple virtualized IT resources accordingly to achieve application QoS targets.