Live Migration of Virtual Machines Using a Mamdani Fuzzy Inference System

Efforts were exerted to enhance the live virtual machines(VMs)migration,including performance improvements of the live migration of services to the cloud.The VMs empower the cloud users to store relevant data and resources.However,the utilization of servers has increased significantly because of the virtualization of computer systems,leading to a rise in power consumption and storage requirements by data centers,and thereby the running costs.Data center migration technologies are used to reduce risk,minimize downtime,and streamline and accelerate the data center move process.Indeed,several parameters,such as non-network overheads and downtime adjustment,may impact the live migration time and server downtime to a large extent.By virtualizing the network resources,the infrastructure as a service(IaaS)can be used dynamically to allocate the bandwidth to services and monitor the network flow routing.Due to the large amount of filthy retransmission,existing live migration systems still suffer from extensive downtime and significant performance degradation in crossdata-center situations.This study aims to minimize the energy consumption by restricting the VMs migration and switching off the guests depending on a threshold,thereby boosting the residual network bandwidth in the data center with a minimal breach of the service level agreement(SLA).In this research,we analyzed and evaluated the findings observed through simulating different parameters,like availability,downtime,and outage of VMs in data center processes.This new paradigm is composed of two forms of detection strategies in the live migration approach from the source host to the destination source machine.

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.

安全增强型虚拟化计算环境下的虚拟机动态迁移机制(英文)

As the number of Virtual Machines(VMs) consolidated on single physical server increases with the rapid advance of server hardware,virtual network turns complex and frangible.Modern Network Security Engines(NSE) are introduced to eradicate the intrusions occurring in the virtual network.In this paper,we point out the inadequacy of the present live migration implementation,which hinders itself from providing transparent VM relocation between hypervisors equipped with Network Security Engines(NSE-H).This occurs because the current implementation ignores VM-related Security Context(SC) required by NSEs embedded in NSE-H.We present the CoM,a comprehensive live migration framework,for NSE-H-based virtualization computing environment.We built a prototype system on Xen hypervisors to evaluate our framework,and conduct experiments under various realistic application environments.The results demonstrate that our solution successfully fixes the inadequacy of the present live migration implementation,and the performance overhead is negligible.

A Hybrid Approach for Performance and Energy-Based Cost Prediction in Clouds

With the striking rise in penetration of Cloud Computing,energy consumption is considered as one of the key cost factors that need to be managed within cloud providers’infrastructures.Subsequently,recent approaches and strategies based on reactive and proactive methods have been developed for managing cloud computing resources,where the energy consumption and the operational costs are minimized.However,to make better cost decisions in these strategies,the performance and energy awareness should be supported at both Physical Machine(PM)and Virtual Machine(VM)levels.Therefore,in this paper,a novel hybrid approach is proposed,which jointly considered the prediction of performance variation,energy consumption and cost of heterogeneous VMs.This approach aims to integrate auto-scaling with live migration as well as maintain the expected level of service performance,in which the power consumption and resource usage are utilized for estimating the VMs’total cost.Specifically,the service performance variation is handled by detecting the underloaded and overloaded PMs;thereby,the decision(s)is made in a cost-effective manner.Detailed testbed evaluation demonstrates that the proposed approach not only predicts the VMs workload and consumption of power but also estimates the overall cost of live migration and auto-scaling during service operation,with a high prediction accuracy on the basis of historical workload patterns.

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.