Virtual Machine Management Research Articles

Energy-aware framework with Markov chain-based parallel simulated annealing algorithm for dynamic management of virtual machines in cloud data centers

Significant savings in the energy consumption, without sacrificing service level agreement (SLA), are an excellent economic incentive for cloud providers. By applying efficient virtual Machine placement and consolidation algorithms, they are able to achieve these goals. In this paper, we propose a comprehensive technique for optimum energy consumption and SLA violation reduction. In the proposed approach, the issues of allocation and management of virtual machines are divided into smaller parts. In each part, new algorithms are proposed or existing algorithms have been improved. The proposed method performs all steps in distributed mode and acts in centralized mode only in the placement of virtual machines that require a global vision. For this purpose, the population-based or parallel simulated annealing (SA) algorithm is used in the Markov chain model for virtual machines placement policy. Simulation of algorithms in different scenarios in the CloudSim confirms better performance of the proposed comprehensive algorithm.

PAVM: a framework for policy‐aware virtual machine management

SummaryThe problem of efficient placement of virtual machines (VMs) in cloud computing infrastructure is well studied in the literature. VM placement decision involves selecting a physical machine in the data center to host a specific VM. This decision could play a pivotal role in yielding high efficiency for both the cloud and its users. Also, reallocation of VMs could be performed through migrations to achieve goals like higher server consolidation or power saving. VM placement and reallocation decisions may consider affinities such as memory sharing, CPU processing, disk sharing, and network bandwidth requirements between VMs defined in multiple dimensions. Considering the NP‐hard complexity associated with computing an optimal solution for this VM placement decision problem, existing research employs heuristic‐based techniques to compute an efficient solution. However, most of these approaches are restricted to only a single attribute at a time. That is, a given technique of using heuristics to compute VM placement considers only a single attribute, while completely ignoring the impact of other dimensions of placing VMs. While this approach may improve the efficiency with respect to the affinity attribute in consideration, it may yield degraded performance with respect to other affinities. In addition, the criteria for determining VM‐placement efficiency may vary for different applications. Hence, the overall goal of achieving VM placement efficiency becomes difficult and challenging. We are motivated by this challenging problem of efficient VM placement and propose policy‐aware virtual machine management (PAVM), a generic framework that can be used for efficient VM management in a cloud computing platform based on the service provider‐defined policies to achieve the desired system‐wide goals. This involves efficient means to profile different VM affinities and to use profiled information effectively by intelligent and efficient VM migrations at run time considering multiple attributes at a time. By conducting extensive evaluation through simulation and real experiments that involve VM affinities on the basis of network and memory, we confirmed that the PAVM architecture is capable of improving the efficiency of a cloud system. We elaborate the architecture of a PAVM system, describe its implementation, and present details of our experiments. Copyright © 2016 John Wiley & Sons, Ltd.

WAYFINDER: parallel virtual machine reallocation through A* search

Modern virtual machine (VM) management software enables consolidation of VMs for power savings or load-balancing for performance. While existing literature provides various methods for computing a better load-balanced, or consolidated goal state, it fails to adequately suggest the best path from the system’s current state to the desired goal allocation. This paper discusses an approach to efficient path finding in VM placement problems for cloud computing environments of moderate scale with results indicating the solution is reasonable for managing hundreds of VMs. We present an overview of known approaches to dynamic VM placement and discuss their shortcomings with respect to dynamic reallocation. We then describe a novel design and implementation of a heuristic search algorithm to determine optimal sequential migration plans to transition from a given VM-to-host allocation to an arbitrary desired allocation state. We then elaborate nuances of A* application to this domain along with our simulation-based validation approach. Finally, this work demonstrates a novel and highly effective technique for exploiting migration parallelism in order to rapidly achieving VM reallocation convergence suitable for continual workload rebalancing in cloud data centers.

Energy conservation in cloud data centers by minimizing virtual machines migration through artificial neural network

Cloud computing is one of the most attractive cost effective technologies for provisioning information technology (IT) resources to common IT consumers. These resources are provided as service through internet in pay per usage manner, which are mainly classified into application, platform and infrastructure. Cloud provides its services through data centers that possess high configuration servers. The conservation of data centers energy give benefits to both cloud providers and consumers in terms of service time and cost. One of the fundamental services of cloud is infrastructure as a service that provides virtual machines (VMs) as a computing resource to consumers. The VMs are created in data center servers as the machine instances, which could work as a dedicated computer system for consumers. As cloud provides the feature of elasticity, the consumers can change their resource demand during service. This characteristics leads VMs migration is unavoidable in cloud environment. The increased down time of VMs in migration affects the efficiency of cloud service. The minimization of VMs migration reduces the processing time that ultimately saves the energy of data centers. The proposed methodology in this work utilizes genetically weight optimized artificial neural network to predict the near future availability of data center servers. Based on the future availability of resources the VMs management activities are performed. The implementation results demonstrated that the proposed methodology significantly reduces the processing time of data centers and the response time of customer applications by minimizing VMs migration.

SDN-Based Virtual Machine Management for Cloud Data Centers

Software-defined networking (SDN) is an emerging paradigm to logically centralize the network control plane and automate the configuration of individual network elements. At the same time, in cloud data centers (DCs), although network and server resources are collocated and managed by a single administrative entity, disjoint control mechanisms are used for their respective management. In this paper, we propose a unified server-network resource management for such converged information and communication technology (ICT) environments. We present a SDN-based orchestration framework for live virtual machine (VM) management that exploits temporal network information to migrate VMs and minimize the network-wide communication cost of the resulting traffic dynamics. A prototype implementation is presented, and a cloud DC testbed is used to evaluate the impact of diverse orchestration algorithms. Our live VM management has been shown to reduce the network-wide communication cost, especially for the high-cost and congestion-prone core and aggregation layers of the DC. Our results show an increase in network-wide throughput by over six times, as well as over 70% communication cost reduction by migrating less than 50% of the VMs.

Virtual machine migration and management for vehicular clouds

Vehicular Cloud Computing is a growing research field which consolidates the benefit of cloud computing into vehicular ad hoc networks. However, few studies address vehicles as potential Virtual Machine hosts. Due to the rapidly changing environment of a vehicular cloud, a host can easily change or leave coverage. As such, Virtual Machine Management and Migration schemes are necessary to ensure cloud subscribers have a satisfactory level of access to the resources. This paper proposes several Vehicular Virtual Machine Migration (VVMM) schemes: VVMM-U (Uniform), VVMM-LW (Least Workload), VVMM-MA (Mobility Aware) and MDWLAM (Mobility and Destination Workload Aware Migration). Their performance is evaluated with respect to a set of metrics through simulations with varying levels of vehicular traffic congestion, Virtual Machine sizes and levels of load restriction. The most advanced scheme (MDWLAM), takes into account, the workload and mobility of the original host as well as those of the potential destinations. By doing so a valid destination will both have time to receive the workload and migrate the new load when necessary. The behavior of various algorithms is compared and the MDWLAM has been shown to demonstrate best performance, exhibiting migration drop rates that are negligibly small.

Research on an Approach of Cloud Workloads Deployment to Public Cloud Based on Open Source Standard

Cloud computing is a kind of formula mode that is based on internet, and in this way, the sharing software and hardware resource and information can be provided for the computer and the other terminal devices on demand. However, due to technical differences among main service vendors, there is a compatibility issue on appropriate format that is based on the virtual machine manager of different cloud platforms. Therefore, it brings some hinder to flexible deployment of cloud workloads to some extent. While with the great development of open source standers, such issues can be solved in another train of thought. In this paper, it proposes a constructive solution to deploy cloud workloads based on open source standers. In the whole structure, the key point is that a cloud middleware is introduced and the application of MapReduce algorithm. The cloud middleware contains four kinds of modules, namely, OVF converter, classifier, interface matched and security check. And the improved MapReduce algorithm is used to the classification phase of the cloud workloads. In the program, cloud workloads are sent to the cloud middleware, and in the process of classified → packaged →matching interface → security check → deployment, which can make cloud workloads deployed easily and flexibly. And this paper will elaborate the progress of each section through a theory case. The biggest construction of this program is manifested in four aspects that are function modular, operation independence, security and scalability. This program must be further improvement, based on the addition of functional roles and the development of open source standards, and has a strong theoretical and practical significance.

Adaptive VM Management with Two Phase Power Consumption Cost Models in Cloud Datacenter

As cloud computing models have evolved from clusters to large-scale data centers, reducing the energy consumption, which is a large part of the overall operating expense of data centers, has received much attention lately. From a cluster-level viewpoint, the most popular method for an energy efficient cloud is Dynamic Right Sizing (DRS), which turns off idle servers that do not have any virtual resources running. To maximize the energy efficiency with DRS, one of the primary adaptive resource management strategies is a Virtual Machine (VM) migration which consolidates VM instances into as few servers as possible. In this paper, we propose a Two Phase based Adaptive Resource Management (TP-ARM) scheme that migrates VM instances from under-utilized servers that are supposed to be turned off to sustainable ones based on their monitored resource utilizations in real time. In addition, we designed a Self-Adjusting Workload Prediction (SAWP) method to improve the forecasting accuracy of resource utilization even under irregular demand patterns. From the experimental results using real cloud servers, we show that our proposed schemes provide the superior performance of energy consumption, resource utilization and job completion time over existing resource allocation schemes.

Decomposed multi-objective bin-packing for virtual machine consolidation

In this paper, we describe a novel solution to the problem of virtual machine (VM) consolidation, otherwise known as VM-Packing, as applicable to Infrastructure-as-a-Service cloud data centers. Our solution relies on the observation that virtual machines are not infinitely variable in resource consumption. Generally, cloud compute providers offer them in fixed resource allocations. Effectively this makes all VMs of that allocation type (or instance type) generally interchangeable for the purposes of consolidation from a cloud compute provider viewpoint. The main contribution of this work is to demonstrate the advantages to our approach of deconstructing the VM consolidation problem into a two-step process of multidimensional bin packing. The first step is to determine the optimal, but abstract, solution composed of finite groups of equivalent VMs that should reside on each host. The second step selects concrete VMs from the managed compute pool to satisfy the optimal abstract solution while enforcing anti-colocation and preferential colocation of the virtual machines through VM contracts. We demonstrate our high-performance, deterministic packing solution generation, with over 7,500 VMs packed in under 2 min. We demonstrating comparable runtimes to other VM management solutions published in the literature allowing for favorable extrapolations of the prior work in the field in order to deal with larger VM management problem sizes our solution scales to.

Oriented-SLA and Energy-Efficient Virtual Machine Management Strategy of Cloud Data Centers

Cloud computing has revolutionized the information and communication technology industry by enabling on-demand provisioning of elastic computing resources on a pay-asyou-go basis. However, due to the large-scale popularization and application, cloud data centers consume enormous amounts of electrical energy, which results in high operating costs and carbon dioxide emissions. In this paper, twenty-two algorithms are simulated for testing the trade-off between the applications performance and energy consumption during virtual machine management of cloud data centers in CloudSim simulation toolkit. The goal of these algorithms is to reduce energy consumption under ensuring the performace of cloud applications. A large number of simulation experimental results prove the performance of these twenty-two algorithms respectively.

A Study on Security Vulnerability on Cloud Platforms

Cloud computing provides an in-built platform to the users with easy and on-demand access to different system level services for creating virtual machines (VM) with efficient utilization of hardware, computing and network resources. This allows users to remotely execute large number of applications across various domains such as, health-care, utility services, e-governance, etc. There are different cloud operating platforms for creation and management of virtual machines through various system or application services. For example, openstack, oracle virtualbox, Microsoft HyperV are widely used operating platform. Virtual machine migration (VMM) is one of such important system services that used for smooth the progress of system maintenance, load management, fault tolerance, secure and safe service offerings. Virtual Machine live migration service transfers an active VM from one physical machine to another across different data centres. It involves a sequence of operations in iteration for migrating execution context of a VM to the destination machine. These operations are dependent on schedule, availability of resources and overall timing constraints. On the other hand, the migration process involves transferring the control data over a communication channel with a shared storage. Due to the above mentioned complexities of VM migration process, any security failures during the migration may cause transfer of incorrect VM instance to a data centre and which in turn may lead to exposure of the data centre to the attackers. This may be manifested as catastrophic system failure, degradation of system performance, load balancing and in long run may result collapse of the data centre. In this paper we studied various security vulnerability in different cloud platforms. These vulnerability will help the security developers to design a secured cloud platform.

Virtual Machine Co-migration for Out-of-band Remote Management

In Infrastructure-as-a-Service (IaaS) clouds, users manage the systems in virtual machines (VMs) called user VMs through remote management systems (RMSes). To allow users to manage their VMs during failures inside the VMs, IaaS usually provides out-of-band remote management. This management is performed indirectly via an RMS server in a privileged VM called the management VM. However, it is discontinued when a user VM is migrated. This is because an RMS server in the management VM at the source host is terminated on VM migration. Even worse, pending data is lost between an RMS client and a user VM. In this paper, we propose D-MORE for continuing out-of-band remote management across VM migration. D-MORE provides a privileged and migratable VM called DomR and performs out-of-band remote management of a user VM via DomR. During VM migration, it synchronously co-migrates DomR and its target VM and transparently maintains the connections between an RMS client, DomR, and its target VM. We have implemented D-MORE in Xen and confirmed that a remote user could manage his VM via DomR after the VM has been migrated. Our experiments showed that input data was not lost during VM migration and the overhead of D-MORE was acceptable.

LHCb experience with running jobs in virtual machines

The LHCb experiment has been running production jobs in virtual machines since 2013 as part of its DIRAC-based infrastructure. We describe the architecture of these virtual machines and the steps taken to replicate the WLCG worker node environment expected by user and production jobs. This relies on the uCernVM system for providing root images for virtual machines. We use the CernVM-FS distributed filesystem to supply the root partition files, the LHCb software stack, and the bootstrapping scripts necessary to configure the virtual machines for us. Using this approach, we have been able to minimise the amount of contextualisation which must be provided by the virtual machine managers. We explain the process by which the virtual machine is able to receive payload jobs submitted to DIRAC by users and production managers, and how this differs from payloads executed within conventional DIRAC pilot jobs on batch queue based sites. We describe our operational experiences in running production on VM based sites managed using Vcycle/OpenStack, Vac, and HTCondor Vacuum. Finally we show how our use of these resources is monitored using Ganglia and DIRAC.

The Evolution of Cloud Computing in ATLAS

The ATLAS experiment at the LHC has successfully incorporated cloud computing technology and cloud resources into its primarily grid-based model of distributed computing. Cloud R&D activities continue to mature and transition into stable production systems, while ongoing evolutionary changes are still needed to adapt and refine the approaches used, in response to changes in prevailing cloud technology. In addition, completely new developments are needed to handle emerging requirements. This paper describes the overall evolution of cloud computing in ATLAS. The current status of the virtual machine (VM) management systems used for harnessing Infrastructure as a Service resources are discussed. Monitoring and accounting systems tailored for clouds are needed to complete the integration of cloud resources within ATLAS' distributed computing framework. We are developing and deploying new solutions to address the challenge of operation in a geographically distributed multi-cloud scenario, including a system for managing VM images across multiple clouds, a system for dynamic location-based discovery of caching proxy servers, and the usage of a data federation to unify the worldwide grid of storage elements into a single namespace and access point. The usage of the experiment's high level trigger farm for Monte Carlo production, in a specialized cloud environment, is presented. Finally, we evaluate and compare the performance of commercial clouds using several benchmarks.

Anomaly detection and identification scheme for VM live migration in cloud infrastructure

Virtual machines (VM) offer simple and practical mechanisms to address many of the manageability problems of leveraging heterogeneous computing resources. VM live migration is an important feature of virtualization in cloud computing: it allows administrators to transparently tune the performance of the computing infrastructure. However, VM live migration may open the door to security threats. Classic anomaly detection schemes such as Local Outlier Factors (LOF) fail in detecting anomalies in the process of VM live migration. To tackle such critical security issues, we propose an adaptive scheme that mines data from the cloud infrastructure in order to detect abnormal statistics when VMs are migrated to new hosts. In our scheme, we extend classic Local Outlier Factors (LOF) approach by defining novel dimension reasoning (DR) rules as DR-LOF to figure out the possible sources of anomalies. We also incorporate Symbolic Aggregate ApproXimation (SAX) to enable timing information exploration that LOF ignores. In addition, we implement our scheme with an adaptive procedure to reduce chances of performance instability. Compared with LOF that fails in detecting anomalies in the process of VM live migration, our scheme is able not only to detect anomalies but also to identify their possible sources, giving cloud computing operators important clues to pinpoint and clear the anomalies. Our scheme further outperforms other classic clustering tools in WEKA (Waikato Environment for Knowledge Analysis) with higher detection rates and lower false alarm rate. Our scheme would serve as a novel anomaly detection tool to improve security framework in VM management for cloud computing.

Know Thy Neighbor: Crypto Library Detection in Cloud

Abstract Software updates and security patches have become a standard method to fix known and recently discovered security vulnerabilities in deployed software. In server applications, outdated cryptographic libraries allow adversaries to exploit weaknesses and launch attacks with significant security results. The proposed technique exploits leakages at the hardware level to first, determine if a specific cryptographic library is running inside (or not) a co-located virtual machine (VM) and second to discover the IP of the co-located target. To this end, we use a Flush+Reload cache side-channel technique to measure the time it takes to call (load) a cryptographic library function. Shorter loading times are indicative of the library already residing in memory and shared by the VM manager through deduplication. We demonstrate the viability of the proposed technique by detecting and distinguishing various cryptographic libraries, including MatrixSSL, PolarSSL, GnuTLS, OpenSSL and CyaSSL along with the IP of the VM running these libraries. In addition, we show how to differentiate between various versions of libraries to better select an attack target as well as the applicable exploit. Our experiments show a complete attack setup scenario with single-trial success rates of up to 90% under light load and up to 50% under heavy load for libraries running in KVM.

Accessing critical care big data: a step by step approach.

Since the publication of an introductory article in the Journal of Thoracic Disease and the Chinese version in Journal of Clinical and Pathological Research (1,2), I received many letters inquiring about detailed steps in accessing the multiparameter intelligent monitoring in intensive care-2 (MIMIC-2) database. When I told them there were very good instructions on the website that can guide you accessing the whole database in a step-by-step approach, most of them were still confusing on some technical details. Probably, most of these readers are critical care clinicians who are interested in researches by using this database but they are lack of some basic knowledge of computer science. Therefore, I write this step-by-step guidance on how to access the MIMIC-2 clinical database, assuming that the readers have no knowledge on virtual machine and database management. In the manuscript, I used many figures to illustrate the detailed steps in establishing virtual machine. I hope that interested investigators can access this database freely without cumbering by technical difficulties.

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This paper presents further development of Sevigator hypervisor-based security system. Original design of Sevigator confines users’ applications in a separate virtual machine that has no network interfaces. For trusted applications Sevigator intercepts network-related system calls and routes them to the dedicated virtual machine that services those calls. This design allows Sevigator protect networking from malicious applications including high-level intruders residing in the kernel. Modern microkernel-based hypervisors opened the door to redesign of Sevigator. Those hypervisors are small operating systems by nature, where management of virtual machines as well as most of hardware operations are isolated in processes with low priority level. Compromising such a process does not result in compromising the whole hypervisor. In this paper we present an experimental design of Sevigator based on NOVA hypervisor where system calls of trusted applications are serviced by a dedicated process in the hypervisor rather than a separate VM. The experiment shows about 25% performance gain due to reduced number of context switches.

Analysis of Infrastructure Monitoring Requirements for OpenStack Nova

Abstract Nova, the compute component of open source cloud platform OpenStack, is responsible for provisioning and management of virtual machines. For the provision of services, compute requires large amount of physical and logical infrastructure. There is a need for monitoring the infrastructure in order to gauge resource utilization, proper functioning and performance of the system. Though several tools exist, a well-defined list of parameters and processes to be monitored within Nova is lacking. In this paper, we identify and present a list of attributes required for monitoring the infrastructure and processes associated with OpenStack Nova. Different categories within infrastructure monitoring are identified, and monitoring parameters associated with them are listed. The proposed list has been applied to three existing software commonly used for monitoring OpenStack Nova. The list facilitates system administrators during selection of existing monitoring software for Nova and will be of help to the developers in selecting monitoring functionality for new monitoring software.

Centralization of Network Using Openflow Protocol

Traditional network is used have distributed nature which face complexities while modifying, monitoring, securing the network. The centralization of network is proving very effective in terms for management, implementation of new applications, monitoring the network. The programmable network using programmable devices helps to implement centralization of network successfully. To reduce the latency in the network the control parameters of control plane are used for making the forwarding decision by the centralized controller. Software Defined Network (SDN) is emerging technology having the centralized policy to design cloud network topology. The abstraction of control plane from data plane is isolating the control parameters from the flow of packets for making an efficient forwarding decision by programmable device. This device is known to be controller; it also reduces the complexities to process the data overhead. The OpenFlow protocol is used for abstraction and transferring the control parameters over secure channel. The protocol enables the enterprise to design well managed and secure cloud. In this paper, the experimental test bed is built which provides the centralized System Center Virtual Machine Manager (SCVMM) controller in the private cloud. The cloud provides its virtual Platform as a Service (PaaS) to the client by creating multiple Virtual Machine instances on single machine. The Virtual Private Network is created by using the Network Virtualization Generic Routing (NVGRE) tunnelling Protocol for the client to reach up to the hosted platform.