Energy-Efficient Real-Time Tasks Scheduling in Cloud Data Centers

Reducing energy consumption in cloud computing systems has been a major concern among the researchers because it not only reduce the operational cost but also increase the system reliability, and efficient scheduling approach is a promising way to achieve this goal. But unfortunately, existing energy-aware scheduling approaches are inadequate  for real-time tasks running in cloud environment because they assumes that cloud computing environment are deterministic and pre-computed schedule decisions are followed  during the execution. The above issues are addressed in this paper by considering the number of energy-efficiency factors such as energy cost, CPU power efficiency, carbon emission rate, and workload, and near-optimal energy efficient scheduling policies are proposed for cloud data center for scheduling real-time, aperiodic, independent tasks that can reduce operational cost and provide Quality of Service (QoS)

Agent based Task Scheduling in Grid

Abstract: Grid computing is considered to be wide area distributed computing which provides sharing, selection and aggregation of distributed resources. Agent paradigm has been widely used in large number of research area and now a days it is widely used in grid computing. In this paper, we proposed agent based strategy that uses knowledge base reasoning framework for task scheduling in grid which minimizes makespan

Task schedul ing for cloud computing using multi-objective hybrid bacteria foraging algorithm

Cloud computing is the delivery of computing services over the internet. Cloud services allow individuals and other businesses organization to use data that are managed by third parties or another person at remote locations. Most Cloud providers support services under constraints of Service Level Agreement (SLA) definitions. The SLAs are composed of different quality of service (QoS) rules promised by the provider. A cloud environment can be classified into two types: computing clouds and data clouds. In computing cloud, task scheduling plays a vital role in maintaining the quality of service and SLA. Efficient task scheduling is one of the major steps for effectively harnessing the potential of cloud computing. This paper explores the task scheduling algorithm using a hybrid approach, which combines desirable characteristics of two of the most widely used biologically-inspired heuristic algorithms, the genetic algorithms (GAs) and the bacterial foraging (BF) algorithms in the computing cloud. The main contributions of this article are twofold. First, the scheduling algorithm minimizes the makespan and second; it reduces the energy consumption, both economic and ecological perspectives. Experimental results show that the performance of the proposed algorithm outperforms than those of other algorithms regarding convergence, stability, and solution diversity