An autonomic provisioning framework for outsourcing data center based on virtual appliances

Abstract

As outsourcing data centers emerge to host applications and services from many different organizations, it is critical for data center owners to isolate different applications while dynamically and optimally allocate sharable resources among them. To address this issue, we propose a virtual-appliance-based autonomic resource provisioning framework for large virtualized data centers. We present the architecture of the data center with enriched autonomic features. We define a non-linear constrained optimization model for dynamic resource provisioning and present a novel analytic solution. Key factors, including virtualization overhead and reconfiguration delay, are incorporated into the model. Experimental results based on a prototype demonstrate that the system-level performance has been greatly improved by taking advantage of fine-grained server consolidation, and the whole system exhibits flexible adaptation in failure scenarios. Experiments with the impact of switching delay also show the efficiency of the framework due to significantly reduced provisioning time.

Adaptive model predictive control of autonomic distributed parallel computations with variable horizons and switching costs

“…The goal of this feature is to model the delay in adding/removing servers in data centers or the time-to-deploy of virtual machines in Cloud environments [59]. Reconfigurations are implemented as simple modifications of the parallelism degree attribute of the Operating Part.…”

“…The simulator can reproduce a reconfiguration overhead implemented by a variable waiting time before re-starting the execution with a different parallelism degree. The goal of this feature is to model the delay in adding/removing servers in data centers or the time-to-deploy of virtual machines in Cloud environments [59].…”

CloudPick: a framework for QoS‐aware and ontology‐based service deployment across clouds

“…Sun et al [4] showed that, by utilizing virtual appliances, the deployment process of virtual machines can be made simpler and easier. Wang et al [12] presented a framework to improve the efficiency of resource provisioning in large data centers using virtual appliances. Similarly, a framework for service deployment in cloud based on virtual appliances and virtual machines has been introduced in our previous work [13].…”

“…In FCBB, the process of searching for providers begins from a start node (vertex) S v , which has a minimum deployment cost (including appliance and virtual unit cost), and for all its children, there can be found at least one provider that satisfies all constraints (partial forward checking; Algorithm 2, lines [12][13][14]. The partial forward checking on the problem constraints is added to the algorithm to avoid back jumps in the circumstances where latency constraints of the users are comparatively tight.…”

Design of distributed microcell-based MMOG hosting platforms: impact study of dynamic relocations

“…To simplify the problem of resource assignments one could use virtualization of hardware resources to dynamically assign extra resources to an application as suggested by [19,20]. However, that approach still requires support from the application for deployment on a cluster, be it virtual or not.…”