Abstract
Cloud Computing is a utility model that offers everything as a service and supports dynamical resource provisioning and auto-scaling in datacenter. Cloud datacenter must envision resource allocation and reallocation to meet out the unpredictable user demand that touted gains in the model. This impact a need of adaptive and automated provisioning of resources aligned with clients SLA amidst the time variant environment in cloud. The robustness of dynamic resource provisioning is based on quick multiplexing virtual resources into physical servers. In this paper we put forward a prediction based automated resource allocation model induced by speculation mechanism. Our model guarantees dodging over/under utilization of resources and minimizes the cost economically without compromising Quality of Service. We regain the speculation concept that uses a past resource access pattern to predict future possible resource access. We introduce the confidence estimation factor to address the historical variability of the current pattern to improve the prediction accuracy. Experimental results show that our proposed model offer more adaptive resource provisioning as compared to heuristic and other machine learning algorithms.
Highlights
Cloud computing made the computing services as a utility model
We measured the response time of the service using Neural Network, Linear Regression and Speculative resource provisioning for the dataset generated by TPC-App benchmarking tools to mimic the real time scenario as mentioned in the section 3 and 4
The response time of the neural network algorithm with varying workload in a day is given in Fig.[5].The time spent for forecasting the resources are inherently related to system response time
Summary
Cloud computing made the computing services as a utility model. Cloud attracts many users for its dynamic resource provisioning and auto scaling behavior. Cloud users have no knowledge or control over how their demands get processed in cloud infrastructure. The Resource Allocation System (RAS) mechanism in the cloud infrastructure exposes the finite resources in the data center as unlimited resources for both the cloud consumer and developer.[15] The virtual machines (instances) are multiplexed with physical servers based on user demands. Studies show that the time taken for instantiating VM’s into the particular host is tentatively 5-15 minutes termed as instance setup time.[29] The instance set up time must be optimized and speed-up to improve the resource provisioning strategies. The total phases involved in automatic resource provisioning are resource modeling, offering, monitoring, and selection.[14]
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