QoS-aware simulation job scheduling algorithm in virtualized cloud environment

Cloud computing is attracting an increasing number of simulation applications running in the virtualized cloud data center. These applications are submitted to the cloud in the form of simulation jobs. Meanwhile, the management and scheduling of simulation jobs are playing an essential role to offer efficient and high productivity computational service. In this paper, we design a management and scheduling service framework for simulation jobs in two-tier virtualization-based private cloud data center, named simulation execution as a service (SimEaaS). It aims at releasing users from complex simulation running settings, while guaranteeing the QoS requirements adaptively. Furthermore, a novel job scheduling algorithm named adaptive deadline-aware job size adjustment (ADaSA) algorithm is designed to realize high job responsiveness under QoS requirement for SimEaaS. ADaSA tries to make full use of the idle fragmentation resources by tuning the number of requested processes of submitted jobs in the queue adaptively, while guaranteeing that jobs’ deadline requirements are not violated. Extensive experiments with trace-driven simulation are conducted to evaluate the performance of our ADaSA. The results show that ADaSA outperforms both cloud-based job scheduling algorithm KCEASY and traditional EASY in terms of response time (up to 90%) and bounded slow down (up to 95%), while obtains approximately equivalent deadline-missed rate. ADaSA also outperforms two representative moldable scheduling algorithms in terms of deadline-missed rate (up to 60%).

Optimized Job Scheduling Algorithm in Cloud Computing Environment

There is a strong trend towards cloud technologies in the SME(Small and Medium Enterprises) sector, largely due to cost reduction, capex availability, and sometimes by collaboration features. Now a days so many startup companies are getting emerged and for them buying the resources for one time usage it becomes more expensive so they are preferring to using the cloud services(SLA) to overcome all this problems. In Cloud computing it essential to check weather a resource is available for allocating or not and allocating the jobs for the clients request is the big task. There are many job scheduling algorithm already proposed by the researchers to implement in cloud environment. After studying there algorithm we have came up with the most effective job scheduling algorithm. It is totally depending on Size and Arrival time of the job. By implementing our proposed algorithm we can obtain the better optimal solution to improve the overall performance of the system and gives more effective results compared to other job scheduling algorithms.