Optimal Resource Allocation and Quality of Service Prediction in Cloud

In the present scenario,cloud computing service provides on-request access to a collection of resources available in remote system that can be shared by numerous clients.Resources are in self-administration;consequently,clients can adjust their usage according to their requirements.Resource usage is estimated and clients can pay according to their utilization.In literature,the existing method describes the usage of various hardware assets.Quality of Service(QoS)needs to be considered for ascertaining the schedule and the access of resources.Adhering with the security arrangement,any additional code is forbidden to ensure the usage of resources complying with QoS.Thus,all monitoring must be done from the hypervisor.To overcome the issues,Robust Resource Allocation and Utilization(RRAU)approach is developed for optimizing the management of its cloud resources.The work hosts a numerous virtual assets which could be expected under the circumstances and it enforces a controlled degree of QoS.The asset assignment calculation is heuristic,which is based on experimental evaluations,RRAU approach with J48 prediction model reduces Job Completion Time(JCT)by 4.75 s,Make Span(MS)6.25,and Monetary Cost(MC)4.25 for 15,25,35 and 45 resources are compared to the conventional methodologies in cloud environment.

Performance evaluation of a WDM/OCDM based hybrid optical switch utilizing efficient resource allocation

A hybrid optical switch （HOS） with physical layer of wavelength division multiplexing and optical code division multiplexing （WDM/OCDM） scheme is proposed. An additional feature to the HOS than optical cross connect （OXC） is that the controller can process requests for both circuit establishment and burst scheduling. In our study, the measurement criteria of HOS are the blocking probability, probability of error, and probability of outage. To simplify the analysis, no distinction is made between a circuit in progress and a burst in progress. Moreover, a minimum fit （MinF） resource allocation strategy is applied in order to increase the bandwidth efficiency and control the multiplexing interference of the OCDM. A 2D Markov model for the HOS is presented using the MinF strategy. Numerical results reveal that the code parameters and the resource allocation strategy greatly affect the performance. Certain periority can be achieved by assigning shorter codes to high periority users and longer codes to low periority users. Also, the probability of error and outage are reduced bv aonling the MinF strategy.

Adaptive double chain quantum genetic algorithm for constrained optimization problems

Optimization problems are often highly constrained and evolutionary algorithms（EAs）are effective methods to tackle this kind of problems. To further improve search efficiency and convergence rate of EAs, this paper presents an adaptive double chain quantum genetic algorithm（ADCQGA） for solving constrained optimization problems. ADCQGA makes use of doubleindividuals to represent solutions that are classified as feasible and infeasible solutions. Fitness（or evaluation） functions are defined for both types of solutions. Based on the fitness function, three types of step evolution（SE） are defined and utilized for judging evolutionary individuals. An adaptive rotation is proposed and used to facilitate updating individuals in different solutions.To further improve the search capability and convergence rate, ADCQGA utilizes an adaptive evolution process（AEP）, adaptive mutation and replacement techniques. ADCQGA was first tested on a widely used benchmark function to illustrate the relationship between initial parameter values and the convergence rate/search capability. Then the proposed ADCQGA is successfully applied to solve other twelve benchmark functions and five well-known constrained engineering design problems. Multi-aircraft cooperative target allocation problem is a typical constrained optimization problem and requires efficient methods to tackle. Finally, ADCQGA is successfully applied to solving the target allocation problem.