Virtual Machine Management Research Articles

Type-aware virtual machine management for energy efficient cloud data centers

To meet the growing demands on cloud services and applications, a sizeable number of large scale cloud data centers, hosting thousands of heterogeneous servers, is established by cloud service providers. The ever growth in establishing cloud data centers is accompanied by consuming enormous amounts of energy. Thus, proposing efficient management approaches to reduce the energy consumption in cloud data centers becomes a top priority for ensuring the scalability of the cloud computing architecture. In general, the main source of energy overconsumption in today’s data centers is due to the inefficient use of the physical servers’ resources, which results in poor server utilization patterns. So, the key aspect is to utilize the physical resources optimally while serving the cloud user demands. This paper investigates the design and implementation of virtual machine management strategies for energy efficient cloud data centers. Particularly, it considers the processes of virtual machine placement and virtual machine consolidation in enhancing the energy efficiency in cloud infrastructures. While addressing the virtual machine placement problem is important, virtual machine consolidation is even more important to enable continuous reorganization of the already-placed virtual machines on the least number of physical machines. This results in reducing the number of active physical machines by leveraging live virtual machine migration enabled by the virtualization concept. Moreover, since the virtual machine migration operations consume additional energy, the frequency of VM migrations needs to be limited and controlled as well. The paper presents a distributed approach to an energy-efficient dynamic virtual machine consolidation mechanism. This approach determines, based on novel algorithms, which virtual machines to migrate, and when. Then, the placement of the virtual machines selected for migration is achieved based on a generalization of the Knapsack Problem known as the Multiple Choice Knapsack Problem. The placement process suits both static and dynamic virtual machine placement. The results of the performance evaluation demonstrate that the proposed new algorithms are able to enhance the energy efficiency in cloud data centers.

Effects of Reducing VMs Management Times on Elastic Applications

Cloud infrastructures provide computing resources to applications in the form of Virtual Machines (VMs). Many applications deployed in cloud resources have an elastic behavior, that is, they change the number of servers (VMs) dynamically, adapting the application to the workload. Scaling-out and scaling-in operations are managed by an auto-scaler module, which can be reactive (adapting the number of VMs to the current workload) or proactive (adapting to the expected future workload). The cloud infrastructure provides a management interface to create (deploy) and destroy (shutdown) server instances, operations that require some time to complete. In this work we evaluate to what extent the reduction of the time required by VM management operations, namely deployment and shutdown, impacts the performance of applications and the behavior of reactive and proactive auto-scaling policies. After establishing several ideal boundaries on the use of resources, we carry out a set of experiments that show how short management times drastically reduce the use of resources, while allowing the application to operate within the required performance bounds.

An Energy Efficient VM Management Scheme with Power-Law Characteristic in Video Streaming Data Centers

As cloud computing services have gained popularity, users view videos on websites (e.g., YouTube ) to generate high CPU resource utilization and bandwidth for video streaming data centers. However, popular videos result in power-law features to cause imbalanced resource utilization. In addition, hotspot and idle servers generate extra power consumption in data centers. Previous studies considered to satisfy the requirements of users, provide faster access rates and save power consumption. However, fewer studies considered resource utilization with different popularity videos. Therefore, this paper proposes an energy efficient virtual machine (VM) management scheme with power-law features (VMPL). VMPL predicts the resource utilization of the video in the future based on the popularity, ensures enough resources for upcoming videos, and turns off idle servers for power saving. Simulation results validated by mathematical analysis show that VMPL has the best resource utilization and the lowest power consumption compared with Nash and Best-Fit algorithms.

Virtual Machine Management Technology for Operating Packet Switching System in a Virtualization Environment

Virtual Machine Management Technology for Operating Packet Switching System in a Virtualization Environment

An experiment-driven energy consumption model for virtual machine management systems

As energy consumption is becoming critical in Cloud data centers, Cloud providers are adopting energy-efficient virtual machines management systems. These systems essentially rely on “what-if” analysis to determine what the consequence of their actions would be and to choose the best one according to a number of metrics. However, modeling energy consumption of simple operations such as starting a new VM or live-migrating is complicated by the fact that multiple phenomena occur. It is therefore important to identify which factors influence energy consumption before proposing any new model. We claim in this paper that one critical parameter is the host configuration, characterized by the number of VMs it is currently executing. Based on this observation, we present an energy model that provides energy estimation associated with VM management operations, such as VMs placement, VM start up and VM migration. The average relative estimation error is lower than 10% using the transactional web benchmark TPC-W, making it a good candidate for driving the actions of future energy-aware cloud management systems.

Performance benchmarking of hypervisors- A Case Study

Objectives: Benchmark the performance of the different hypervisors using standard benchmarking tools considering the different parameters like CPU, RAM, DISK Read-Write, Network Read-Write. We use different types of hypervisors and compare the performance of hypervisors how they behave differently for above mentioned parameters. Methods/ Statistical Analysis: Experiment setup consists of three hypervisors namely Virtual Box 6.0, VMware workstation as a hosted hypervisors and Xen as a Native or bare metal hypervisor. Open source hypervisor considered for the experiment. VMware Workstation 9.0, Virtual Box 5.0 as a hosted hypervisor and Citrix Xen as the native hypervisor were considered. Stress is used as the standard benchmarking tool and Glances tool helps to collect and monitor the traces. Findings: The virtualization is carried out by the software layer called as the Hypervisor or Virtual Machine Monitor (VMM). Hypervisor is widely used in cloud datacenters. Bench-mark is the measurement of best practice performance. Bench-marking is very essential term for the discovery of the best performance given by the particular system. Benchmarking can provides you the external references and the best practices on which to base your evaluations and to design your system pro-cesses which can be very useful in finding the gaps in the system to achieve the desired performance. The Bare metal Hypervisors have the direct access to the hardware resource rather than accessing resource via operating system as in hosted hypervisors. Performance analysis of desired work shows the keen differences in performance related to CPU, RAM, DISK Read-Write, and Network Read-Write performance. Results concludes that hosted hypervsors have good performance in N/W, Disk read and write because the architecturally bounded to operating system where interact with base OS while Xen as native hypervisor has a good CPU Performance because Virtual machine has direct access to hardware layer through Virtual machine manager. Improvements: We used single benchmark tool to test all parameters, it has potential to use different benchmarking tools specific to parameters like for Network Read-Write, AIO stress can be used. Further concentrates bench mark comparison of hypervisors and containers, where container adoption has increased in cloud data centers. Keywords: Benchmarking, Cloud, Glances, Hypervisors, VMware, Xen

Enhanced intrusion detection and prevention system on cloud environment using hybrid classification and OTS generation

Cloud environment is an assembly of resources for furnishing on-demand services to cloud customers. Here access to cloud environment is via internet services in which data stored on cloud environment are easier to both internal and external intruders. To detect intruders, various intrusion detection systems and authentication systems was proposed in earlier researches which are primarily ineffective. Many existing researchers were concentrated on machine learning approaches for detecting intrusions using fuzzy clustering, artificial neural network, support vector machine, fuzzy with neural network and etc., which are not furnishing predominant results based on detection rate and false negative rates. Our proposed system directed on intrusion detection system and it uses cloudlet controller, trust authority and virtual machine management in cloud environment. We propose two novel algorithms such as (i) packet scrutinization algorithm which examines the packets from the users and (ii) hybrid classification model called “NK-RNN” which is a combination of normalized K-means clustering algorithm with recurrent neural network. For preventing the user from intruders, we propose a one time signature for cloud user in order to access the data on cloud environment. Our proposed classifier effectively detects the intruders which are experimentally proved by comparing with existing classification models. Thus our proposed results are expressed by packet loss ratio, average packet delay, throughput, detection rate, false positive rate and false negative rate.

Multi-objective dynamic management of virtual machines in cloud environments

The management strategy of a data center needs access to sufficient resources in order to handle different requests of applications, while minimizing the consumed energy. With regard to high and varying resource demands, Virtual Machines (VM) management necessitates dynamic strategies. Dynamic management of VMs includes VM placement and VM migration. The management approach presented in this paper aimed to reduce the energy consumption and the violation of service level agreements (SLA) simultaneously in data centers. The simulation results indicate that proposed approach improved the VM management 40% compared to the previous single-goal approaches based on the energy consumption and SLA violation rates.

A Virtual Machine Placement Algorithm with Energy-Efficiency in Cloud Computing

Energy efficient virtual machines (VM) management and distribution on cloud platforms is an important research subject. Mapping VMs into PMs (Physical Machines) requires knowing the capacity of each PM and the resource requirements of the VMs. It should also take into accounts of VM operation overheads, the reliability of PMs, Quality of Service (QoS) in addition to energy efficiency. In this article, the authors propose an energy efficient statistical live VM placement scheme in a heterogeneous server cluster. Their scheme supports VM requests scheduling and live migration to minimize the number of active servers in order to save the overall energy in a virtualized server cluster. Specifically, the proposed VM placement scheme incorporates all VM operation overheads in the dynamic migration process. In addition, it considers other important factors in relation to energy consumption and is ready to be extended with more considerations on user demands. The authors conducted extensive evaluations based on HPC jobs in a simulated environment. The results prove the effectiveness of the proposed scheme.

CCHybrid: CPU co‐scheduling in virtualization environment

SummaryVirtualization is very important to build the emerging cloud infrastructure, and a VM (virtual machine) with many kinds of workloads can run on physical machines in cloud environment. The VMM (virtual machine manager) scheduling algorithm asynchronously schedules each VCPU (virtual CPU) of a VM and ensures the CPU time usage of each VM. This proportional share method is widely used, because it simplifies the implementation of VMM CPU scheduling algorithm and can provide near‐perfect performance for most ordinary workloads. However, when a VM runs with parallel workloads, the above method causes performance degradation because of the negative impact of virtualized systems. Therefore, in this paper, we propose an optimized scheduling system, called CCHybrid, for parallel program in the Xen. It uses weight‐based proportion share strategy to ensure the fairness. In order to resolve the impact of virtualization on synchronization, it uses a novel co‐scheduling strategy, which dynamically adjusts the size of co‐scheduling to remit CPU fragmentation and maintains the original asynchronous scheduling policy for non‐parallel applications. In this way, CCHybrid provides CPU resource allocation services for Xen and can decrease the negative impact of virtualized systems, while ensuring the fairness of VMs and the performance of non‐parallel workload. Experimental results show that in the case of multiple VMs, CCHybrid improves the performance of parallel workload from 15% to 50%, and the impact on non‐parallel workload is less than 5%, in comparison with the credit scheduling algorithm of Xen.

An Efficient Decentralized Energy and Communication Aware Workload Management

In this paper, a research work is carried out to improve workload management by minimize energy consumption of node, localizing traffic in a network and reduce communication delay between virtual machines in cloud environment. Cloud computing consists numerous virtual machine to store huge volume of data. The usage of virtual machines is to provide separate guest operating system which is popular now-a-days. The workload management across different virtual machines in cloud is really a difficult process. Decentralized approaches were proposed in the literature for energy efficient management of virtual machines. But still it has some issues like failed to consider the communication between virtual machines and it leads to traffic in a network. Thus it reduces the performance of work load management.

Novel power reduction framework for enhancing cloud computing by integrated GSNN scheduling method

Popularity of cloud computing is being increased drastically by the use of people all over the world in their comfort zone. For the purpose of withholding the performance of cloud, it is significant to design an efficient scheduling methodology. Researches have designed methods like directed acyclic graph, MinES, MinCS, ant colony optimization, cross-entropy stochastic scheduling, interlacing peak scheduling method, etc for enhancing cloud and user experience. The major problem existed in these methods was higher execution time, overload issues and higher power consumption. To overwhelm the problems, we design a novel framework that is comprised with queue manager (QM), scheduler (SH), virtual machine manager (VMMA), VM allocator (VMA) and VM power manager (VMP). Firstly, in QM the incoming tasks are split into two queues based on task’s deadline, they are represented as urgent queue (UQ) and waiting queue (WQ). Secondly, we perform hybrid scheduling which combines grey system and neural network (GSNN) that considers three significant parameters as task length, CPU intensive and memory intensive. This GSNN scheduling is enabled to withstand effectively even for ‘N’ number of tasks and that leads to minimization of execution time. Then thirdly, each task is allocated to corresponding VM with respect to the capacity and workload of VM. Finally VMP keeps updating the VM information for computing the underutilized hosts then it performs VM migration to put up the idle host to OFF state, for reducing the unwanted power consumption. Simulation results of our entire framework shows improvements when compared with state-of-the-art methods.

Topology and Application Aware Dynamic VM Management in the Cloud

Cloud computing continues to mature and more applications continue to be deployed in public clouds. Client applications deployed in the cloud should automatically scale up and down to match changing workload demands, though they must be careful to ensure that sufficient resources are provisioned to achieve performance objectives. The cloud provider, on the other hand, attempts to reduce costs by reducing power consumption by consolidating load onto fewer, highly utilized machines. In this work, we introduce an algorithm that integrates both application autoscaling and dynamic virtual machine (VM) allocation into a single algorithm in order to achieve the goals of both cloud provider and client. Further, we consider multi-VM applications, such as multi-tiered web-based applications, and extend the integrated algorithm to take the network topology into account when placing or migrating applications. The goal is to reduce VM-to-VM communication latency; our focus is on trying to contain applications within the same racks. We evaluate our work through simulation, showing that the integrated algorithm can achieve better application performance with a significant reduction in virtual machine live migrations, and the topology-aware extension successfully places applications within a single rack.

Load feedback-based resource scheduling and dynamic migration-based data locality for virtual hadoop clusters in openstack-based clouds

With cloud computing technology becoming more mature, it is essential to combine the big data processing tool Hadoop with the Infrastructure as a Service (IaaS) cloud platform. In this study, we first propose a new Dynamic Hadoop Cluster on IaaS (DHCI) architecture, which includes four key modules: monitoring, scheduling, Virtual Machine (VM) management, and VM migration modules. The load of both physical hosts and VMs is collected by the monitoring module and can be used to design resource scheduling and data locality solutions. Second, we present a simple load feedback-based resource scheduling scheme. The resource allocation can be avoided on overburdened physical hosts or the strong scalability of virtual cluster can be achieved by fluctuating the number of VMs. To improve the flexibility, we adopt the separated deployment of the computation and storage VMs in the DHCI architecture, which negatively impacts the data locality. Third, we reuse the method of VM migration and propose a dynamic migration-based data locality scheme using parallel computing entropy. We migrate the computation nodes to different host(s) or rack(s) where the corresponding storage nodes are deployed to satisfy the requirement of data locality. We evaluate our solutions in a realistic scenario based on OpenStack. Substantial experimental results demonstrate the effectiveness of our solutions that contribute to balance the workload and performance improvement, even under heavy-loaded cloud system conditions.

PLAN: Joint Policy- and Network-Aware VM Management for Cloud Data Centers

Policies play an important role in network configuration and therefore in offering secure and high performance services especially over multi-tenant Cloud Data Center (DC) environments. At the same time, elastic resource provisioning through virtualization often disregards policy requirements, assuming that the policy implementation is handled by the underlying network infrastructure. This can result in policy violations, performance degradation and security vulnerabilities. In this paper, we define PLAN, a PoLicy-Aware and Network-aware VM management scheme to jointly consider DC communication cost reduction through Virtual Machine (VM) migration while meeting network policy requirements. We show that the problem is NP-hard and derive an efficient approximate algorithm to reduce communication cost while adhering to policy constraints. Through extensive evaluation, we show that PLAN can reduce topology-wide communication cost by 38 percent over diverse aggregate traffic and configuration policies.

Angleško-slovenski glosar virtualizacijske terminologije: usklajevanje tehničnega, terminološkega in organizacijskega dela

With the growing adoption of using virtual machines over physical hosts as a form of resource consolidation, The English-Slovene Glossary of Virtualization-related Terms encompassing management of virtual machines, cloud orchestration and data storage seemed like the next logical step.The Glossary of Virtualization-related Terms has been translated into Slovene and reviewed by experts in the fields of cloud computing, virtualization technologies and linguists. Close to 6000 terms had been localized for the Slovene market, using the advanced version of Poedit application – the editor for translating apps and websites. PoEdit automatically displays translation equivalents either from its own base (built-in translation memory) or from the base of previously translated words and phrases, which had been created and offered as opensource by other users. Based on these, it makes suggestions and, over time, learns enough to fill in frequently used strings. The translated text was then imported into its original page location – the Graphic User Interface (visible on buttons on the dashboard) of the customized ManageIQ Enterprise Virtualization Manager (EVM) software used by administrators of public and private clouds. Hence the main criterion was brevity and precision in transfering meaning across languages. This is where we encountered most problems – neologisms and existing words that acquire new meaning as a result of rapid development of virtualization technology. To avoid merely adding a suffix while the core of the word remains the same in Slovene (e.g. tenant, tenant-ov) and also to encourage further additions, comments or suggested changes the glossary has been made available on Wikipedia, the online encyclopedia.

NDynamic Framework for Secure VM Migration over Cloud Computing

In the centralized cloud controlled environment, the decision-making and monitoring play crucial role where in the host controller (HC) manages the resources across hosts in data center (DC). HC does virtual machine (VM) and physical hosts management. The VM management includes VM creation, monitoring, and migration. If HC down,the services hosted by various hosts in DC can`t be accessed outside the DC. Decentralized VM management avoids centralized failure by considering one of the hosts from DC as HC that helps in maintaining DC in running state. Each host in DC has many VM`s with the threshold limit beyond which it can`t provide service.To maintain threshold , the host`s in DC does VM migration across various hosts. The data in migration is in the form of plaintext, the intruder can analyze packet movement and can control hosts traffic. The incorporation of security mechanism on hosts in DC helps protecting data in migration. This paper discusses an approach for dynamic HC selection, VM selection and secure VM migration over cloud environment.

Virtual machine management system based on the power saving algorithm in cloud

This work uses the open source codes and PHP web programming to implement a resource management system with power saving method for virtual machines. We propose a system integrated with open source software, such as KVM and Libvirt, to construct a virtual cloud management platform. This system can detect the status of cloud resources via SNMP, calculate the operation efficiency of the overall system, allocate virtual machines through the live migration technology, and turn off extra machines in the cloud to save energy. According to our proposed power saving method, we have constructed a power efficient virtualization management platform in the cloud. Our objective is to provide enterprises or end users with power saving private cloud solutions. In this work we have also built a web page to allow users to easily access and control the cloud virtualization resources, i.e., users can manage virtual machines and monitor the status of resources via the web interface. From analysis of the experimental results of live migration of virtual machines, this work demonstrates that efficient use of hardware resources is realized by the power saving method, and the aim of power saving for cloud computing is achieved.

Datacenter Virtualization with Optimization and Customization

The paper provides a way to provide virtualization administration as a service and also ways to customize, optimize and improvise the performance of latest available virtualization techniques. Till now virtualization provides utilization from 60-70% for hypervisor but there is no technique available to optimize the server and client associated with it. The paper delivers a solution which is oriented for VM administrators and VM providers. The solution once implemented will provide a secure, economical, interoperable, storage platform independent solution for virtualization setup for administrators. The implementation can be done in a few minutes but it will provide a boost to management of virtual machines from the base level. The security techniques discussed in paper can be improvised i.e. VPN and encryption can be implemented in place of port forwarding although it might affect performance.

Energy efficiency of dynamic management of virtual cluster with heterogeneous hardware

Cloud computing is an essential part of today’s computing world. Continuously increasing amount of computation with varying resource requirements is placed in large data centers. The variation among computing tasks, both in their resource requirements and time of processing, makes it possible to optimize the usage of physical hardware by applying cloud technologies. In this work, we develop a prototype system for load-based management of virtual machines in an OpenStack computing cluster. Our prototype is based on an idea of ‘packing’ idle virtual machines into special park servers optimized for this purpose. We evaluate the method by running real high-energy physics analysis software in an OpenStack test cluster and by simulating the same principle using the Cloudsim simulator software. The results show a clear improvement, 9–48 % , in the total energy efficiency when using our method together with resource overbooking and heterogeneous hardware.