Performance Analysis and Mitigation of Virtual Machine Server by using Naive Bayes Classification

Cloud providers generally run one or more Virtual Machine (VM) instances on the same physical machine. Though it increases data center utilization, it exposes VM to a co-location attack. VM placement and migration are the two strategies adopted for mitigating co-locations. Current methods for VM placement or VM migration consider only security as decision criteria and do not consider other factors like Quality-of-Service degradation, data center utilization, etc. This work proposes a placement and migration strategy for mitigation of co-location attacks with joint consideration of multi objectives like QoS, data center utilization, energy consumption, and security risks. A security-driven multi-criteria optimization -based VM placement policy is proposed. A joint consideration of multiobjective performance optimization along with co-location security risk minimization is done to design a novel VM placement policy based on user categorization. The policy can reduce the likelihood of co-location target VM with attacker VM without much degradation to the performance of VM and data center utilization. The solution mitigates co-location risks without much compromise to the performance of VM and data center resource utilization. The co-residence risk is mitigated by the categorization of users into three levels i.e. unlabeled, risky, and safe, and physical machines into two groups as safe and unsafe. The PMs available in data center is grouped into three different VM placement policies, they are undecided pool, safe pool and unsafe pool.

Cloud computing models use virtual machine (VM) clusters for protecting resources from failure with backup capability. Cloud user tasks are scheduled by selecting suitable resources for executing the task in the VM cluster. Existing VM clustering processes suffer from issues like preconfiguration, downtime, complex backup process, and disaster management. VM infrastructure provides the high availability resources with dynamic and on-demand configuration. The proposed methodology supports VM clustering process to place and allocate VM based on the requesting task size with bandwidth level to enhance the efficiency and availability. The proposed clustering process is classified as preclustering and postclustering based on the migration. Task and bandwidth classification process classifies tasks with adequate bandwidth for execution in a VM cluster. The mapping of bandwidth to VM is done based on the availability of the VM in the cluster. The VM clustering process uses different performance parameters like lifetime of VM, utilization of VM, bucket size, and task execution time. The main objective of the proposed VM clustering is that it maps the task with suitable VM with bandwidth for achieving high availability and reliability. It reduces task execution and allocated time when compared to existing algorithms.

This paper proposes an adaptive virtual machine (VM) assignment scheme for multi-tenant data center networks. In multi-tenant data centers, tenants submit their resource requirements and the data centers provide VMs which are assigned to physical servers. These VMs communicate with each other to execute distributed processing. The amount of traffic communicated by VMs is very large and this is one of big issues in data center networks. In order to reduce the amount of traffic injected into data center networks, we need an appropriate VM assignment strategy which considers traffic between VMs. Furthermore, VMs are dynamically assigned to and removed from physical servers according to resource requirements of tenants, and thus we should consider such dynamic behavior. The proposed scheme provides an VM assignment strategy which reduces the amount of traffic injected into data center networks, taking into account dynamic behavior of the data center networks. Through simulation experiments, we demonstrate that the proposed scheme reduces the network load efficiently.

CoMap: An efficient virtual network re-mapping strategy based on coalitional matching theory

Virtual Machines (VMs) and network virtualization technologies have been widely used for cloud computing. Active VMs can move to other execution environments, an action called "live migration." VMs must have the same IP address before and after migration. Therefore, live migration must be in the same segment. For this reason, network virtualization technologies are used to make a wide area L2 network between data centers (DC). Such L2 networks have a redundant route problem resulting from the distance between a particular VM and the default gateway. To solve this problem, each DC has a default gateway and VMs use the nearest default gateway. However, if live migration is used, the VMs' nearest default gateway changes. In this paper, we explain these problems, and propose solutions to change to the proper default gateway.

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 (MPaAFVMC) 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.

SummaryVirtual desktop infrastructure (VDI) solutions seek to provide a satisfactory user experience at the client side when accessing remote desktop applications, even from mobile devices with limited capabilities. This paper presents a new approach, improving on previous work by the authors, in which a combination of Virtual Network Computing (VNC) and streaming protocols allowed efficient remote web access to virtualized applications within a cloud architecture. The new approach simplifies virtual machine templates, from which virtual machine instances are deployed, by centralizing software modules, greatly simplifying their management. Our new contribution consists of an integrated solution with specific WebM video encoding modules in charge of application visual output processing, an Hypertext Transfer Protocol (HTTP) streaming server, and a VNC server. The solution can be installed in the hypervisor of the host machines instead of replicating the servers and modules throughout the guest (virtual) machines that run the virtualized applications. Consequently, their implementations are unique and independent of the operating system of the virtual machines. In short, it is not necessary to provide different implementations for different operating systems, which reduces the complexity of virtual machine templates and greatly simplies platform management. To demonstrate our solution, we have modified the Quick Emulator (QEMU)‐Kernel‐based Virtual Machine (KVM) hypervisor source code accordingly. We also present qualitative and quantitative analyses that demonstrate that the new approach is advantageous in terms of software management and quality of experience, compared with our previous work and other well‐known thin clients, contributing to the enhancement of VDI systems. Copyright © 2015 John Wiley & Sons, Ltd.

Virtualization has become a core technique to support a data center, which usually consists of hardware, virtual machine(VM), and operating system(OS) etc. With virtualization, users can make good use of resources provided in a data center with resource protection and efficient hardware utilization. However, as data amount has increased very rapidly, many issues such as low power and resource sharing have become very critical. To resolved these issues, this paper presents a novel simulation environment based on a tiny OS, Hard Disk Drive (HDD), and Solid State Drive(SSD) to help users and designers understand a data center via simulation and analysis. Compared with the previous work, our tool can simulate and analyze homogeneous and heterogeneous systems with a multi-layer approach. Although each part of a data center has been studied by researchers, the analysis for data centers with multiple layers or heterogeneous devices is still an important issue. If programmers cannot analyze a data center, they are not easy to handle resources efficiently or understand cross-layer influence. They may encounter unpredictable challenges arising from the integration of various components. To address these challenges, programmer and system designer will be need thorough understanding of multi-layer heterogenous system. We propose a new simulation tool called Data Center Simulator(DCSim). We have used small-scale os and storage simulation to achieve multi-layer system simulation to help us to understand multi-layer heterogenous system. Therefore, programmer can simulate and analysis the system that we will analysis the parallel program and different storage deices on DCSim.

Hypervisor-enabled virtualization is a promising technology to deploy applications and implement Virtual Machine (VM) migration on cloud data centers. However, one critical aspect in hypervisor-based solution is that to deploy applications each VM is installed with a dedicated guest OS along with the requirement of binaries and library files and substantially large VM's image size. Therefore, it is a significant concern for data center administrators because it causes performance overheads. To solve these issues, a new paradigm of virtualization technology that has attracted considerable attention is containerization. Although, progress has been made to address container-based virtualization, there has been less attention to explore the issue of performance evaluation of container and VM in a migration environment. The main contribution of this research study is to develop an experimental setup to implement and evaluate the performance of LXD/CR container-based migration technique and compared it with the pre-copy VM migration scheme. To conduct this performance evaluation, first we modified Linux kernel and implemented LXD which uses liblxc to launch container, then migration of the running container is facilitated using a checkpoint/restore mechanism of CRIU technique. To ensure the applicability and validity for real-time cloud infrastructure, we executed a wide variety of benchmarks such as web server, CPU-intensive, disk I/O and database. From the results obtained, it is found that compared to pre-copy VM migration scheme, LXD/CR container migration technique reduces the downtime, migration time, amount of data transferred and the number of pages transferred by 75.66%, 65.55%, 76.63%, and 76.78%, respectively. In addition, the migration overhead caused is reduced by 55.89% w.r.t. CPU utilization and 76.52% w.r.t. RAM utilization. This being an important contribution for migrating the running applications efficiently and consequently increase the business value of the cloud infrastructure providers.

Increasing demand for Cloud infrastructure and services leads to the challenges for management and maintenance of large data Center. Data center is fully equipped with huge number of resources. Those resources consumes energy in spite of their partial or full utilization. As a result data center consumes lots of energy, which in turn increases the total cost of operation and carbon footprint in environment. These concern leads to “Green Computing”, i.e. to reduce total operational cost, Carbon Footprint in environment and efficient usage of the computing resources. In data center main processing element is virtual machine (VM), which is an instance of computing and storage resources, handles computational processes. Hence, it is important to reduce energy consumed by VM. As the workload distribution is varying in data Center as per the need, the number of VMs configured in the host are uneven, but host consumes maximum energy every time, irrespective of the workload. This leads to wastage of computational resources. This paper is intended to analyze such issues and specifically prove an algorithm which, significantly reduces energy consumption in data center, while ensuring SLA, when VM is in migration from one host to another in the data center.

CBase: Fast Virtual Machine storage data migration with a new data center structure

Scheduling in workflows plays a crucial role in optimizing task execution and resource utilization in cloud computing systems, particularly when Quality of Service (QoS) requirements, such as deadlines and task priorities, have to be fulfilled. This paper presents a novel, lightweight heuristic scheduling algorithm that incorporates QoS metrics into the heuristic scheduling process using the WorkflowSim simulation framework. The algorithm filters tasks based on the feasibility of deadlines, computes dynamic priority scores based on a blend of urgency and task weight, and employs an earliest-finish-time (EFT) policy for task to VM allocation. To test the proposed approach, simulation experiments were performed with synthetic Directed Acyclic Graph (DAG) workflows in a heterogeneous VM (Virtual Machine) cloud environment. The performance of the algorithm was evaluated relative to traditional baseline approaches, including First-Come-FirstServe (FCFS), Round Robin (RR), and Min-Min, using the metrics makespan, deadline miss rate, workflow success rate, and VM utilization. The results demonstrate the effectiveness of the proposed method in reducing makespan by up to 31.7% and improving workflow success rate to $\mathbf{7 2 \%}$ while achieving $\mathbf{7 9 \%}$ VM utilization, outperforming traditional schedulers. These findings suggest that lightweight heuristics can graciously meet the objectives of QoS without suffering the computational cost of metaheuristics. Enhancements in the future may include energy- and cost-conscious goals, re- scheduling, and predictive models to make it applicable across the various dynamics of a production field.

We study the problem of virtual machine (VM) placement and migration in a data center. In the current approaches, VMs are assigned to physical servers using on-demand provisioning. Such an approach is simple but it often results in a poor performance due to resource fragmentation. Additionally, sub-optimal VM placement usually generates unneeded VM migration and unnecessary cross network traffic. The efficiency of a datacenter therefore significantly depends on how VMs are provisioned and where they are placed. A good placement scheme will not only improve the quality of service but also reduce the operation cost of the data center. In this paper, we study the problem of optimal VM placement and migration to minimize resource usage and power consumption in a data center. We formulate the optimization problem as a joint multiple objective function and solve it by leveraging the framework of convex optimization. Due to the intractable nature of the combinatorial optimization, we then propose Multi-level Join VM Placement and Migration (MJPM) algorithms based on the relaxed convex optimization framework to approximate the optimal solution. The theoretical analysis demonstrates the effectiveness of our proposed algorithms that substantially increases data center efficiency. In addition, our extensive simulation results on different practical topologies show significant performance improvement over the existing approaches.

Many data centers extensively use virtual machines (VMs), which provide the flexibility to move workload among physical servers. VMs can be placed to maximize application performance, power efficiency, or even fault tolerance. However, VMs are typically repositioned without considering network topology, congestion, or traffic routes. In this demo, we show a system, Virtue, which enables the comparison of different algorithms for VM placement and network routing at the scale of an entire data center. Our goal is to understand how placement and routing affect overall application performance by varying the types and mix of workloads, network topologies, and compute resources; these parameters will be available for demo attendees to explore.

We propose two VM migration methods that improve the total sum of program correct by dynamically moving VMs to appropriate edge node. We assume a multi-hierarchy information processing system that the cloud data center and the edge node VM (Virtual Machine) perform processing at the same time, the data center completes all processing, and the VM has the required response time and returns a result that the processing has progressed (called accuracy). The first method is “VM sweeping method” that allocates more CPU time to VMs that perform high accuracy processing. The second methods is “VM sweeping method” that allocates more CPU time to VMs that perform high accuracy processing that improves per unit CPU time (AIR), and “VM number averaging method” that attempts to move VMs so that the assignment status of the CPU time in all edge nodes is equal. The accuracy improvement effect of the proposed VM migration method was evaluated by a simulator. Evaluations changed the AIR from the Early-blooming type where the accuracy greatly increases in the first half to the Late-blooming type where the accuracy greatly increases in the second half, and comparing it with the case where the VM is not moved. It was clarified that a big effect can be expected in the VM sweeping method, and the VM number averaging method in the Earlyblooming type program.