A Prediction-Based Multi-Objective VM Consolidation Approach for Cloud Data Centers

Virtual machine(VM)consolidation aims to run VMs on the least number of physical machines(PMs).The optimal consolidation significantly reduces energy consumption(EC),quality of service(QoS)in applications,and resource utilization.This paper proposes a prediction-basedmulti-objective VMconsolidation approach to search for the best mapping between VMs and PMs with good timeliness and practical value.We use a hybrid model based on Auto-Regressive Integrated Moving Average(ARIMA)and Support Vector Regression(SVR)(HPAS)as a prediction model and consolidate VMs to PMs based on prediction results by HPAS,aiming at minimizing the total EC,performance degradation(PD),migration cost(MC)and resource wastage(RW)simultaneously.Experimental results usingMicrosoft Azure trace show the proposed approach has better prediction accuracy and overcomes the multi-objective consolidation approach without prediction(i.e.,Non-dominated sorting genetic algorithm 2,Nsga2)and the renowned Overload Host Detection(OHD)approaches without prediction,such as Linear Regression(LR),Median Absolute Deviation(MAD)and Inter-Quartile Range(IQR).

Virtual Machine Consolidation with Multi-Step Prediction and Affinity-Aware Technique for Energy-Efficient Cloud Data Centers

Virtual machine(VM)consolidation is an effective way to improve resource utilization and reduce energy consumption in cloud data centers.Most existing studies have considered VM consolidation as a bin-packing problem,but the current schemes commonly ignore the long-term relationship between VMs and hosts.In addition,there is a lack of long-term consideration for resource optimization in the VM consolidation,which results in unnecessary VM migration and increased energy consumption.To address these limitations,a VM consolidation method based on multi-step prediction and affinity-aware technique for energy-efficient cloud data centers(MPaAF-VMC)is proposed.The proposed method uses an improved linear regression prediction algorithm to predict the next-moment resource utilization of hosts and VMs,and obtains the stage demand of resources in the future period through multi-step prediction,which is realized by iterative prediction.Then,based on the multi-step prediction,an affinity model between the VM and host is designed using the first-order correlation coefficient and Euclidean distance.During the VM consolidation,the affinity value is used to select the migration VM and placement host.The proposed method is compared with the existing consolidation algorithms on the PlanetLab and Google cluster real workload data using the CloudSim simulation platform.Experimental results show that the proposed method can achieve significant improvement in reducing energy consumption,VM migration costs,and service level agreement(SLA)violations.

Load-Aware VM Migration Using Hypergraph Based CDB-LSTM

Live Virtual Machine(VM)migration is one of the foremost techniques for progressing Cloud Data Centers’(CDC)proficiency as it leads to better resource usage.The workload of CDC is often dynamic in nature,it is better to envisage the upcoming workload for early detection of overload status,underload status and to trigger the migration at an appropriate point wherein enough number of resources are available.Though various statistical and machine learning approaches are widely applied for resource usage prediction,they often failed to handle the increase of non-linear CDC data.To overcome this issue,a novel Hypergrah based Convolutional Deep Bi-Directional-Long Short Term Memory(CDB-LSTM)model is proposed.The CDB-LSTM adopts Helly property of Hypergraph and Savitzky–Golay(SG)filter to select informative samples and exclude noisy inference&outliers.The proposed approach optimizes resource usage prediction and reduces the number of migrations with minimal computa-tional complexity during live VM migration.Further,the proposed prediction approach implements the correlation co-efficient measure to select the appropriate destination server for VM migration.A Hypergraph based CDB-LSTM was vali-dated using Google cluster dataset and compared with state-of-the-art approaches in terms of various evaluation metrics.

A Review of Dynamic Resource Management in Cloud Computing Environments

In a cloud environment, Virtual Machines (VMs) consolidation andresource provisioning are used to address the issues of workload fluctuations.VM consolidation aims to move the VMs from one host to another in order toreduce the number of active hosts and save power. Whereas resource provisioningattempts to provide additional resource capacity to the VMs as needed in order tomeet Quality of Service (QoS) requirements. However, these techniques have aset of limitations in terms of the additional costs related to migration and scalingtime, and energy overhead that need further consideration. Therefore, this paperpresents a comprehensive literature review on the subject of dynamic resourcemanagement (i.e., VMs consolidation and resource provisioning) in cloud computing environments, along with an overall discussion of the closely relatedworks. The outcomes of this research can be used to enhance the developmentof predictive resource management techniques, by considering the awareness ofperformance variation, energy consumption and cost to efficiently manage thecloud resources.

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.