Efficient Task Scheduling in Cloud Environment Based On Dynamic Priority and Optimized Technique

Cloud computing emerging filed process large task to handle the resources. From an application perspective, the study of task scheduling mechanisms for data transfer in large cloud computing environments is performed poorly.Unbalanced scheduling leads to traffic load, loss of energy and hardware controls failure. In addition, resident’s devices are not considered to reduce power consumption delay. So, the Internet of things (IoT) rules the current trends of the Internet. The huge number of things (objects), which are associated with the Internet, produces a large amount of information that needs a ton of exertion and tasks preparation to make it valuable. To resolve this problem, we propose a dynamic multi-level task scheduling (DMLTS) based in cascade shrink priority (CSP) to allocate task to optimize the scheduling. With intent a TacticalLoad Balancer (TLB) and The Preemptive Flow Manager (PFM) is responsible for the application of load balancing strategy based on the mixed load balancing algorithm improves the task allocation better to balance load to improve the response time. Experimental results have been demonstrated with respect to better load balancing, lower power rate, and time consumption rate in both phase and random uniform propagation. Simulated results performance of this process can reduce data processing time and achieve load neutralization.