Task scheduling and virtual machine allocation policy in cloud computing environment

Cloud computing represents a novel computing model in the contemporary technology world. In a cloud system, the com- puting power of virtual machines （VMs） and network status can greatly affect the completion time of data intensive tasks. How- ever, most of the current resource allocation policies focus only on network conditions and physical hosts. And the computing power of VMs is largely ignored. This paper proposes a comprehensive resource allocation policy which consists of a data intensive task scheduling algorithm that takes account of computing power of VMs and a VM allocation policy that considers bandwidth between storage nodes and hosts. The VM allocation policy includes VM placement and VM migration algorithms. Related simulations show that the proposed algorithms can greatly reduce the task comple- tion time and keep good load balance of physical hosts at the same time.

A novel virtual machine deployment algorithm with energy efficiency in cloud computing

In order to improve the energy efficiency of large-scale data centers, a virtual machine(VM) deployment algorithm called three-threshold energy saving algorithm(TESA), which is based on the linear relation between the energy consumption and(processor) resource utilization, is proposed. In TESA, according to load, hosts in data centers are divided into four classes, that is,host with light load, host with proper load, host with middle load and host with heavy load. By defining TESA, VMs on lightly loaded host or VMs on heavily loaded host are migrated to another host with proper load; VMs on properly loaded host or VMs on middling loaded host are kept constant. Then, based on the TESA, five kinds of VM selection policies(minimization of migrations policy based on TESA(MIMT), maximization of migrations policy based on TESA(MAMT), highest potential growth policy based on TESA(HPGT), lowest potential growth policy based on TESA(LPGT) and random choice policy based on TESA(RCT)) are presented, and MIMT is chosen as the representative policy through experimental comparison. Finally, five research directions are put forward on future energy management. The results of simulation indicate that, as compared with single threshold(ST) algorithm and minimization of migrations(MM) algorithm, MIMT significantly improves the energy efficiency in data centers.

Hybrid Prairie Dog and Beluga Whale Optimization Algorithm for Multi-Objective Load Balanced-Task Scheduling in Cloud Computing Environments

The cloud computing technology is utilized for achieving resource utilization of remotebased virtual computer to facilitate the consumers with rapid and accurate massive data services.It utilizes on-demand resource provisioning,but the necessitated constraints of rapid turnaround time,minimal execution cost,high rate of resource utilization and limited makespan transforms the Load Balancing(LB)process-based Task Scheduling(TS)problem into an NP-hard optimization issue.In this paper,Hybrid Prairie Dog and Beluga Whale Optimization Algorithm(HPDBWOA)is propounded for precise mapping of tasks to virtual machines with the due objective of addressing the dynamic nature of cloud environment.This capability of HPDBWOA helps in decreasing the SLA violations and Makespan with optimal resource management.It is modelled as a scheduling strategy which utilizes the merits of PDOA and BWOA for attaining reactive decisions making with respect to the process of assigning the tasks to virtual resources by considering their priorities into account.It addresses the problem of pre-convergence with wellbalanced exploration and exploitation to attain necessitated Quality of Service(QoS)for minimizing the waiting time incurred during TS process.It further balanced exploration and exploitation rates for reducing the makespan during the task allocation with complete awareness of VM state.The results of the proposed HPDBWOA confirmed minimized energy utilization of 32.18% and reduced cost of 28.94% better than approaches used for investigation.The statistical investigation of the proposed HPDBWOA conducted using ANOVA confirmed its efficacy over the benchmarked systems in terms of throughput,system,and response time.

I-Neat:An Intelligent Framework for Adaptive Virtual Machine Consolidation

With the increasing use of cloud computing,high energy consumption has become one of the major challenges in cloud data centers.Virtual Machine(VM)consolidation has been proven to be an efficient way to optimize energy consumption in data centers,and many research works have proposed to optimize VM consolidation.However,the performance of different algorithms is related with the characteristics of the workload and system status;some algorithms are suitable for Central Processing Unit(CPU)-intensive workload and some for web application workload.Therefore,an adaptive VM consolidation framework is necessary to fully explore the potential of these algorithms.Neat is an open-source dynamic VM consolidation framework,which is well integrated into OpenStack.However,it cannot conduct dynamic algorithm scheduling,and VM consolidation algorithms in Neat are few and basic,which results in low performance for energy saving and Service-Level Agreement(SLA)avoidance.In this paper,an Intelligent Neat framework(I-Neat)is proposed,which adds an intelligent scheduler using reinforcement learning and a framework manager to improve the usability of the system.The scheduler can select appropriate algorithms for the local manager from an algorithm library with many load detection algorithms.The algorithm library is designed based on a template,and in addition to the algorithms of Neat,I-Neat adds six new algorithms to the algorithm library.Furthermore,the framework manager helps users add self-defined algorithms to I-Neat without modifying the source code.Our experimental results indicate that the intelligent scheduler and these novel algorithms can effectively reduce energy consumption with SLA assurance.

Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center

Load balancing is vital for the efficient and long-term operation of cloud data centers.With virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing and consolidation.However,it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring instability.Therefore,we provide a new approach,called Prepartition,for load balancing.It partitions a VM request into a few sub-requests sequentially with start time,end time and capacity demands,and treats each sub-request as a regular VM request.In this way,it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal,which supports the resource allocation in a fine-grained manner.Simulations with real-world trace and synthetic data show that our proposed approach with offline version(PrepartitionOff)scheduling has 10%–20%better performance than the existing load balancing baselines under several metrics,including average utilization,imbalance degree,makespan and Capacity_makespan.We also extend Prepartition to online load balancing.Evaluation results show that our proposed approach also outperforms state-of-the-art online algorithms.

Chaotic Simulator for Bilevel Optimization of Virtual Machine Placements in Cloud Computing

The drastic increase in engineering system complexity has spurred the development of highly efficient optimization techniques.Many real-world optimization problems have been identified as bilevel/multilevel as well as multiobjective.The primary aim of this work is to present a framework to tackle the bilevel virtual machine(VM)placement problem in cloud systems.This is done using the coupled map lattice(CML)approach in conjunction with the Stackelberg game theory and weighted-sum frameworks.The VM placement problem was modified from the original multiobjective(MO)problem to an MO bilevel formulation to make it more realistic albeit more complicated.Additionally comparative analysis on the performance of the CML approach was carried out against the particle swarm optimization method.A new bilevel metric called the cascaded hypervolume indicator is introduced and applied to measure the dominance of the solutions produced by both methods.Detailed analysis on the computational results is presented.

Tasks Scheduling in Cloud Environment Using PSO-BATS with MLRHE

Cloud computing plays a significant role in Information Technology(IT)industry to deliver scalable resources as a service.One of the most important factor to increase the performance of the cloud server is maximizing the resource utilization in task scheduling.The main advantage of this scheduling is to max-imize the performance and minimize the time loss.Various researchers examined numerous scheduling methods to achieve Quality of Service(QoS)and to reduce execution time.However,it had disadvantages in terms of low throughput and high response time.Hence,this study aimed to schedule the task efficiently and to eliminate the faults in scheduling the tasks to the Virtual Machines(VMs).For this purpose,the research proposed novel Particle Swarm Optimization-Bandwidth Aware divisible Task(PSO-BATS)scheduling with Multi-Layered Regression Host Employment(MLRHE)to sort out the issues of task scheduling and ease the scheduling operation by load balancing.The proposed efficient sche-duling provides benefits to both cloud users and servers.The performance evalua-tion is undertaken with respect to cost,Performance Improvement Rate(PIR)and makespan which revealed the efficiency of the proposed method.Additionally,comparative analysis is undertaken which confirmed the performance of the intro-duced system than conventional system for scheduling tasks with highflexibility.