Large Number Of Virtual Machines Research Articles

Algorithms for automated live migration of virtual machines

We present two strategies to balance the load in a system with multiple virtual machines (VMs) through automated live migration. When the push strategy is used, overloaded hosts try to migrate workload to less loaded nodes. On the other hand, when the pull strategy is employed, the light-loaded hosts take the initiative to offload overloaded nodes. The performance of the proposed strategies was evaluated through simulations. We have discovered that the strategies complement each other, in the sense that each strategy comes out as “best” under different types of workload. For example, the pull strategy is able to quickly re-distribute the load of the system when the load is in the range low-to-medium, while the push strategy is faster when the load is medium-to-high. Our evaluation shows that when adding or removing a large number of virtual machines in the system, the “best” strategy can re-balance the system in 4–15 min.

VMThunder: Fast Provisioning of Large-Scale Virtual Machine Clusters

Infrastructure as a service (IaaS) allows users to rent resources from the Cloud to meet their various computing requirements. The pay-as-you-use model, however, poses a nontrivial technical challenge to the IaaS cloud service providers: how to fast provision a large number of virtual machines (VMs) to meet users' dynamic computing requests? We address this challenge with VMThunder, a new VM provisioning tool, which downloads data blockson demandduring the VM booting process and speeds up VM image streaming by strategically integrating peer-to-peer (P2P) streaming techniques with enhanced optimization schemes such as transfer on demand, cache on read, snapshot on local, and relay on cache. In particular, VMThunder stores the original images in a share storage and in the meantime it adopts a tree-based P2P streaming scheme so that common image blocks are cached and reused across the nodes in the cluster. We implement VMThunder in CentOS Linux and thoroughly test its performance. Comprehensive experimental results show that VMThunder outperforms the state-of-the-art VM provisioning methods, with respect to scalability, latency, and VM runtime I/O performance.

Design of an Efficient Method for Identifying Virtual Machines Compatible with Service Chain in a Virtual Network Environment

With advancements in network technologies, network virtualization has been proposed to efficiently provide a large number of services and flexible management by utilizing limited resources over existing networks as much as possible. Network virtualization has been proposed as a new paradigm for networks, as it simplifies complicated network configurations for convenient maintenance through the maximum utilization of limited network resources. However, problems have arisen due to network virtualization, such as how to assign real network components that are compatible with virtual network components over a large number of virtual machines existing in a network. In particular, the servicechaining concept—where a network flow only passes through needed services—has been newly introduced by combining the recently highlighted Software-Defined Network with a virtualization concept called Network Function Virtualization. As a result, studies on which virtual machines are selected and how to connect them have increased ever more. Accordingly, this paper aims to identify virtual machines that are compatible with service chaining in a virtual network environment where virtual machines are dispersed, and it proposes a method of how to create a path by connecting virtual machines.

Update Checker: Increasing Virtual Machine Security in Cloud Environments

A common approach in Infrastructure-as-a-Service Clouds or virtualized Grid computing is to provide virtual machines to customers to execute their software on remote resources. Giving full superuser permissions to customers eases the installation and use of user software, but it may lead to security issues. The providers usually delegate the task of keeping virtual machines up to date to the customers, while the customers expect the providers to perform this task. Consequently, a large number of virtual machines (either running or dormant) are not patched against the latest software vulnerabilities. The approach presented in this article deals with these problems by helping users as well as providers to keep virtual machines up to date. Prior to the update step, it is crucial to know which software is actually outdated or affected by remote security vulnerabilities. While these tasks seem to be straight forward, developing a solution that handles multiple software repositories from different vendors and identifies the correct packages is a challenging task. The Update Checker presented in this article identifies outdated software packages in virtual machines, regardless if the virtual machine is running or dormant on disk. The proposed Online Penetration Suite performs pre-rollout scans of virtual machines for security vulnerabilities using established techniques and prevents execution of flawed virtual machines.

Cost-oriented proactive fault tolerance approach to high performance computing (HPC) in the cloud

Cloud computing offers new computing paradigms, capacity and flexible solutions to high performance computing (HPC) applications. For example, Hardware as a Service (HaaS) allows users to provide a large number of virtual machines (VMs) for computation-intensive applications using the HaaS model. Due to the large number of VMs and electronic components in HPC system in the cloud, any fault during the execution would result in re-running the applications, which will cost time, money and energy. In this paper we presented a proactive fault tolerance (FT) approach to HPC systems in the cloud to reduce the wall-clock execution time and dollar cost in the presence of faults. We also developed a generic FT algorithm for HPC systems in the cloud. Our algorithm does not rely on a spare node prior to prediction of a failure. We also developed a cost model for executing computation-intensive applications on HPC systems in the cloud. We analysed the dollar cost of provisioning spare nodes and checkpointing FT to assess the value of our approach. Our experimental results obtained from a real cloud execution environment show that the wall-clock execution time and cost of running computation-intensive applications in cloud can be reduced by as much as 30%. The frequency of checkpointing of computation-intensive applications can be reduced up to 50% with our FT approach for HPC in the cloud compared with current FT approaches.

Multicast Congestion Control with Quantized Congestion Notification in Data Center Networks

In data center networks, group communication is currently playing an important role and multicast communications is an effective way to support group communication for large numbers of virtual machines. Layer-2 congestion control named QCN (Quantized Congestion Notification) has been proposed to realize the high reliability required by LAN/SAN integration in data center networking. Our preliminary evaluation in this paper shows that a multicast flow suffers lower throughput than unicast flows when conventional QCN is applied in a naive manner. This is because a sending device receives congestion feedback from multiple locations on a multicast tree and decreases transmission rate accordingly. To counter this throughput degradation of multicast flows, we propose a new Layer 2 congestion control algorithm in multicast environment, Quantized Congestion Notification with Bottleneck Selection (QCN/BS). In QCN/BS, the switch in the worst congestion level is selected and the transmission rate of the sending device is calculated exclusively according to feedback from the selected switch. Simulation results show that when conventional QCN is used, a multicast flow experiences lower and more severely unfair throughput than a unicast flow. The proposed QCN/BS resolves this problem.

Fault-tolerant virtual cluster experiments on federated sites using BonFIRE

The failure of Cloud sites and variability of performance of the virtual machines (VMs) in this environment are two issues that have to be taken into account by software providers. If they want to guarantee the return of the results on time to their customers, their virtual infrastructure must be designed to adapt itself to the new scenario. This is especially critical in compute intensive applications that execute on virtual clusters with a large number of VMs, because they can need hours or days to produce valid results. Changes in the performance could mean longer times to produce results and, probably, higher costs. Site failures usually force to restart from the beginning, losing many computing hours. In this paper we present a fault-tolerant virtual cluster architecture that can tackle with both issues in the context of compute intensive bag-of-tasks applications. It includes an Elasticity Engine that uses the application performance to decide about the enlargement or reduction of the virtual cluster to fulfill the expectations of the final users. The architecture has been tested in three experiments: execution of the application in a multi-site configuration which has shown that it is not suffering from any penalty because of its execution in a distributed environment; an experiment about Specific Deadline Objective where the Elasticity Engine takes decisions about the enlargement of the cluster with new VMs to end the simulation on time; and a fault-tolerance test where one part of a distributed virtual cluster is lost, restoring the application performance on the surviving Cloud site using recovering mechanisms and elasticity rules, without interruption of the service.

Agent based Resource Monitoring System in IaaS Cloud Environment

Abstract In cloud computing environment, Infrastructure as a Service (IaaS) takes the lowest tier in the cloud pyramid where most control and management is needed. IaaS clouds offer IT infrastructure resources for computing, storage and networking to cloud users. In a real cloud data center, there are physical servers with a large number of virtual machines. These virtual machines are hosted with many heterogeneous applications. In order to optimize the utilization of computing resources and also saving energy consumption of cloud data centers, the applications running on the virtual machines will be migrated either to the same server or to another physical or virtual server. Identifying when it is best to migrate an application in a virtual machine has a direct impact on resource optimization. Performance optimization can be best achieved by an efficiently monitoring the utilization of computing resources. So, we need a comprehensive intelligent monitoring agent to analyze the performances of virtual machines. In this paper, we propose an agent based resource monitoring system that depicts the CPU and memory utilization. The monitoring agent collects the virtual machine resource usages and displays in a dashboard. Dashboard displays the key performance metrics such as CPU and memory utilization. The statistical report of dashboard provides information to cloud administrator for resource optimization.

Increasing virtual machine security in cloud environments

A common approach in Infrastructure-as-a-Service Clouds or virtualized Grid computing is to provide virtual machines to customers to execute their software on remote resources. Giving full superuser permissions to customers eases the installation and use of user software, but it may lead to security issues. The providers usually delegate the task of keeping virtual machines up to date to the customers, while the customers expect the providers to perform this task. Consequently, a large number of virtual machines (either running or dormant) are not patched against the latest software vulnerabilities. The approach presented in this article deals with these problems by helping users as well as providers to keep virtual machines up to date. Prior to the update step, it is crucial to know which software is actually outdated or affected by remote security vulnerabilities. While these tasks seem to be straightforward, developing a solution that handles multiple software repositories from different vendors and identifies the correct packages is a challenging task. The Update Checker presented in this article identifies outdated software packages in virtual machines, regardless if the virtual machine is running or dormant on disk. The proposed Online Penetration Suite performs pre-rollout scans of virtual machines for security vulnerabilities using established techniques and prevents execution of flawed virtual machines. The article presents the design, the implementation and an experimental evaluation of the two components.

Energy accounting for shared virtualized environments under DVFS using PMC-based power models

Virtualized infrastructure providers demand new methods to increase the accuracy of the accounting models used to charge their customers. Future data centers will be composed of many-core systems that will host a large number of virtual machines (VMs) each. While resource utilization accounting can be achieved with existing system tools, energy accounting is a complex task when per-VM granularity is the goal. In this paper, we propose a methodology that brings new opportunities to energy accounting by adding an unprecedented degree of accuracy on the per-VM measurements. We present a system — which leverages CPU and memory power models based in performance monitoring counters (PMCs) — to perform energy accounting in virtualized systems. The contribution of this paper is threefold. First, we show that PMC-based power modeling methods are still valid on virtualized environments. Second, we show that the Dynamic Voltage and Frequency Scaling (DVFS) mechanism, which commonly is used by infrastructure providers to avoid power and thermal emergencies, does not affect the accuracy of the models. And third, we introduce a novel methodology for accounting of energy consumption in virtualized systems. Accounting is done on a per-VM basis, even in the case where multiple VMs are deployed on top of the same physical hardware, bypassing the limitations of per-server aggregated power metering. Overall, the results for an Intel ® Core™ 2 Duo show errors in energy estimations <5%. Such an approach brings flexibility to the chargeback models used by service and infrastructure providers. For instance, we are able to detect cases where VMs executed during the same amount of time, present more than 20% differences in energy consumption even only taking into account the consumption of the CPU and the memory.

Dynamic memory mapping delivers additional flexibility to virtual resource management

The Department of Computer Science and Technology, Peking University, Beijing, China, has shown that a novel Dynamic Memory Mapping (DMM) model brings about additional flexibility to virtual resource management, leading to the feature-adjustable design of a virtual machine monitor (VMM). The study is reported in Issue 53 (June, 2010) of SCIENCE CHINA Information Sciences because of its significant research value. Memory is one of the most frequently accessed components in virtual machine (VM) systems. Because a VM’s memory requirement varies according to the running applications, disregarding the dynamic changes can result in suboptimal use of memory resources, which negatively affects the VM’s performance. However, because the infrastructures of current technologies are usually independent of each other, they exhibit poor extensibility, integrity, and maintainability. To improve the flexibility and extensibility of the VMM, we need to implement a dynamic memory management mechanism in the VMM, while preserving the high efficiency of memory accesses from virtual machines. To resolve these problems, this work proposes a DMM model [1]. The DMM model is a low-level memory management mechanism, which allows dynamic change of the mapping between the pseudo-physical memory as seen from VMs and the machine memory, while the virtual machine is running. On one hand, DMM is independent of, yet compatible with, various virtualization architectures, while on the other, it presents a uniform upward interface for supporting high-level memory management policies. As a result, the DMM layer incorporates high-level policies and low-level implementations by making both of them adjustable. In this work, Prof. Wang, Prof. Luo and their group present the principle of the DMM model, and explain the procedures of various memory management policies under this model, such as demand paging, virtual memory and memory sharing. They also implement the DMM model in KVM, an open source VMM. They first designed a memory pool, a set of machine pages provided by the VMM to a particular virtual machine whose size could be expanded or shrunk while operating. To make the model work in a real system, they manipulated a page-level protection mechanism to propagate memory-mapping updates to the shadow page tables, which is the only way for a VM to access its virtualized memory. They also utilized reverse mapping, a data structure that maps a machine page back to all the shadow page table entries that have mapped it, to facilitate the mapping propagation. The DMM model has several advantages over the current memory management mechanism in VMMs. The first is platform independency—the model is defined abstractly, thus is independent of implementations and computer architectures. The second is flexibility—the DMM model provides a uniform interface for integrating advanced memory management policies. Through the general mechanism, they can work together without conflict. Last but not least, the modular and layered design of the DMM reduces the complexity of a VMM’s code base, and therefore improves the security and dependability of the system. A journal reviewer noted:“This paper addresses the inefficiency in the design of current virtual machine monitors. Their approach is novel and systematic, and incurs only minor overheads. The result is of academic significance and practical value”. Another reviewer said, “It enriches and expands the capacity and capability of virtualization. It offers us new methods to deploy and manage large numbers of virtual machines”. A series of papers about virtual machine system optimization written by Prof. Wang, Prof. Luo and their group have been published in SCIENCE CHINA Information Sciences [2], IEEE Cluster [3], ACM SIGOPS Operating System Review [4]. The authors are affiliated to the Institute of Network Computing and Information Systems (NCIS, http://ncis.pku.edu.cn) at Peking University. This institute, led by Prof. LI XiaoMing, conducts research mainly in the fields of high productivity computing, search engine and Web mining, distributed systems, internet and mobile computing, and database technology.