Resource pre-allocation algorithms for low-energy task scheduling of cloud computing

In order to lower the power consumption and improve the coefficient of resource utilization of current cloud computing systems, this paper proposes two resource pre-allocation algorithms based on the ＂shut down the redundant, turn on the demanded＂ strategy here. Firstly, a green cloud computing model is presented, abstracting the task scheduling problem to the virtual machine deployment issue with the virtualization technology. Secondly, the future workloads of system need to be predicted： a cubic exponential smoothing algorithm based on the conservative control（CESCC） strategy is proposed, combining with the current state and resource distribution of system, in order to calculate the demand of resources for the next period of task requests. Then, a multi-objective constrained optimization model of power consumption and a low-energy resource allocation algorithm based on probabilistic matching（RA-PM） are proposed. In order to reduce the power consumption further, the resource allocation algorithm based on the improved simulated annealing（RA-ISA） is designed with the improved simulated annealing algorithm. Experimental results show that the prediction and conservative control strategy make resource pre-allocation catch up with demands, and improve the efficiency of real-time response and the stability of the system. Both RA-PM and RA-ISA can activate fewer hosts, achieve better load balance among the set of high applicable hosts, maximize the utilization of resources, and greatly reduce the power consumption of cloud computing systems.

Data-driven resource allocation with trac load prediction

Wireless big data is attracting extensive attention from operators,vendors and academia,which provides new freedoms in improving the performance from various levels of wireless networks.One possible way to leverage big data analysis is predictive resource allocation,which has been reported to increase spectrum and energy resource utilization eciency with the predicted user behavior including user mobility.However,few works address how the trac load prediction can be exploited to optimize the data-driven radio access.We show how to translate the predicted trac load into the essential information used for resource optimization by taking energy-saving transmission for non-real-time user as an example.By formulating and solving an energy minimizing resource allocation problem with future instantaneous bandwidth information,we not only provide a performance upper bound,but also reveal that only two key parameters are related to the future information.By exploiting the residual bandwidth probability derived from the trac volume prediction,the two parameters can be estimated accurately when the transmission delay allowed by the user is large,and the closed-form solution of global optimal resource allocation can be obtained when the delay approaches in nity.We provide a heuristic resource allocation policy to guarantee a target transmission completion probability when the delay is no-so-large.Simulation results validate our analysis,show remarkable energy-saving gain of the proposed predictive policy over non-predictive policies,and illustrate that the time granularity in predicting trac load should be identical to the delay allowed by the user.