An Energy-Saving Task Scheduling Strategy Based on Vacation Queuing Theory in Cloud Computing

High energy consumption is one of the key issues of cloud computing systems. Incoming jobs in cloud computing environments have the nature of randomness, and compute nodes have to be powered on all the time to await incoming tasks. This results in a great waste of energy. An energy-saving task scheduling algorithm based on the vacation queuing model for cloud computing systems is proposed in this paper. First, we use the vacation queuing model with exhaustive service to model the task schedule of a heterogeneous cloud computing system.Next, based on the busy period and busy cycle under steady state, we analyze the expectations of task sojourn time and energy consumption of compute nodes in the heterogeneous cloud computing system. Subsequently, we propose a task scheduling algorithm based on similar tasks to reduce the energy consumption. Simulation results show that the proposed algorithm can reduce the energy consumption of the cloud computing system effectively while meeting the task performance.

An Improved Whale Algorithm and Its Application in Truss Optimization

The current Whale Optimization Algorithm(WOA)has several drawbacks,such as slow convergence,low solution accuracy and easy to fall into the local optimal solution.To overcome these drawbacks,an improved Whale Optimization Algorithm(IWOA)is proposed in this study.IWOA can enhance the global search capability by two measures.First,the crossover and mutation operations in Differential Evolutionary algorithm(DE)are combined with the whale optimization algorithm.Second,the cloud adaptive inertia weight is introduced in the position update phase of WOA to divide the population into two subgroups,so as to balance the global search ability and local development ability.ANSYS and Matlab are used to establish the structure model.To demonstrate the application of the IWOA,truss structural optimizations on 52-bar plane truss and 25-bar space truss were performed,and the results were are compared with that obtained by other optimization algorithm.It is verified that,compared with WOA,the IWOA has higher efficiency,fast convergence speed,better solution accuracy and stability.So IWOA can be used in the optimization design of large truss structures.

Layered virtual machine migration algorithm for network resource balancing in cloud computing

Due to the increasing sizes of cloud data centers, the number of virtual machines （VMs） and applications rises quickly. The rapid growth of large scale Internet services re- suits in unbalanced load of network resource. The bandwidth utilization rate of some physical hosts is too high, and this causes network congestion. This paper presents a layered VM migration algorithm （LVMM）. At first, the algorithm will divide the cloud data center into several regions according to the bandwidth utilization rate of the hosts. Then we bal- ance the load of network resource of each region by VM migrations, and ultimately achieve the load balance of net- work resource in the cloud data center. Through simulation experiments in different environments, it is proved that the LVMM algorithm can effectively balance the load of network resource in cloud computing.

An Uncertainty Enhanced Trust Evolution Strategy for e-Science

Resources shared in e-Science have critical requirements on security.Thus subjective trust management is essential to guarantee users＇ collaborations and communications on such a promising infrastructure.As an important nature of subjective trust,uncertainty should be preserved and exhibited in trust definition,representation and evolution.Consider the drawbacks of existing mechanisms based on random mathematics and fuzzy theory,this paper designs an uncertainty enhanced trust evolution strategy based on cloud model theory.We define subjective trust as trust cloud.Then we propose new algorithms to propagate,aggregate and update trust.Furthermore,based on the concept of similar cloud,a method to assess trust level is put forward.The simulation results show the effiectiveness,rationality and efficiency of our proposed strategy.

Cloud-Data Envelopment Analysis Method Used for Assessment of Restoration Building Block Schemes

The selection of an optimized restoration building block(RBB)scheme among all available schemes is one of the most important factors impacting the power system restoration process after a complete or partial blackout.This paper presents a data envelopment analysis(DEA)model used as an empirical method to assess the RBB schemes.An N-level evaluation scale cloud system is built based on cloud theory to transform qualitative I/O indices of DEA model into quantitative values.Through joint utilization of the CCR(Charnes,Cooper and Rhodes)model and the LJK(Li,Jahanshahloo and Khodabakhshi)model,the established Joint-DEA model makes the newly proposed Cloud-DEA method a more feasible and robust method in assessment of RBB schemes.