Abstract
AbstractNowadays, Big data processing in cloud is becoming an inevitable trend. For Big data processing, a specially designed cloud resource allocation approach is required. However, it is challenging how to efficiently allocate resources dynamically based on Big data applications’ QoS demands and support energy and cost savings by optimizing the number of servers in use. In order to solve this problem, a general problem formulation is established in this paper. By giving certain assumptions, we prove that the reduction of resource waste has a direct relation with cost minimization. Based on that, we develop efficient heuristic algorithms with tuning parameters to find cost minimized dynamic resource allocation solutions for the above-mentioned problem. In paper, we study and test the workload of Big data by running a group of typical Big data jobs, i.e., video surveillance services, on Amazon Cloud EC2. Then we create a large simulation scenario and compare our proposed method with other approaches.KeywordsBig dataresource allocationcloud computingoptimization
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
