Cloud Computing environment provisions the supply of computing resources on the basis of demand, as and when needed. It builds upon advances of virtualisation and distributed computing to support cost efficient usage of computing resources, emphasizing on resource scalability and on-demand services. It allows business outcomes to scale up and down their resources based on needs. Managing the customer demand creates the challenges of ondemand resource allocation. Virtual Machine (VM) technology has been employed for resource provisioning. It is expected that using virtualized environment will reduce the average job response time as well as executes the task according to the availability of resources. Hence VMs are allocated to the user based on characteristics of the job. Effective and dynamic utilization of the resources in cloud can help to balance the load and avoid situations like slow run of systems. This paper mainly focuses on allocation of VM to the user, based on analyzing the characteristics of the job. Main principle of this work is that low priority jobs (deadline of the job is high) should not delay the execution of high priority jobs (deadline of the job is low) and to dynamically allocate VM resources for a user job within deadline
Analyze or diagnose of Cardiovascular activity under abnormal heart beat is extremely an intricate and vital job to the medical experts, made more complicated to a novice persons. Electrocardiogram is a way to measure or diagnose for research on human beings to spot heart disease by abnormal heart rhythms. These streaming medical signals can be well analyzed or diagnosed only with the prior knowledge. This paper proposes the methodology; Multivariate Maximal Time Series Motif with Naïve Bayes Classifier to classify the ECG abnormalities. The proposed model of predicting Time Series Motif is evaluated with the dataset contains the collection of ECG signals of patients recorded using Holter Monitor. The efficiency of the proposed work is proved by comparing the precision of existing with various Feature extraction Techniques.
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