Top PDF Performance Profiling of Virtual Machines

Performance Profiling of Virtual Machines

Performance Profiling of Virtual Machines

process. Although KVM supports multiple hardware architectures, we choose the x86 with virtualization extensions to illustrate our implementation, because it has the most mature code. The virtualization extensions augment x86 with two new oper- ation modes: host mode and guest mode. KVM runs in host mode, and its guests run in guest mode. Host mode is compatible with conventional x86, while guest mode is very similar to it but de- privileged in certain ways. Guest mode supports all four privilege levels and allows direct execution of the guest code. A virtual machine control structure (VMCS) is introduced to control vari- ous behaviors of a virtual machine. Two transitions are also de- fined: a transition from host mode to guest mode called a VM- entry, and a transition from guest mode to host mode called a VM-exit. Regarding performance profiling, if a performance counter overflows when the CPU is in guest mode, the currently running guest is forced to exit, i.e., the CPU switches from guest mode to host mode. The VM-exit information filed in the VMCS indicates that the current VM-exit is caused by a non-maskable interrupt (NMI). By checking this field, KVM is able to decide whether a counter overflow is contributed by a guest. This ap- proach assumes all NMIs are caused by counter overflows in a profiling session. To be more precise, KVM could also check the content of all performance counters to make sure that NMIs are really caused by counter overflows.
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Characterizing the Performance of Parallel Applications on Multi-Socket Virtual Machines

Characterizing the Performance of Parallel Applications on Multi-Socket Virtual Machines

Virtualization technologies are ubiquitously deployed in data centers and offer the benefit of resource consolida- tion [17], performance and fault isolation, flexible migra- tion [20] and easy creation [6] of specialized environments. They have been extensively used to run web server, E- commerce and data mining workloads. With the recent ad- vent of the cloud computing paradigm, these workloads have been supplemented with High Performance Computing (HPC) applications: the Amazon Elastic Compute Cloud (EC2) al- ready provides virtualized clusters targeting the automotive, pharmaceutical, financial and life sciences domains. The US Department of Energy is evaluating virtualization and cloud computing technologies in the Magellan [16] project. In com- mercial workloads, the tasks are often independent and serve short lived requests; server tasks are started at virtual machine boot time and are alive until shutdown. In contrast, HPC workloads have tasks tightly coupled by data movement and tend to persistently use a significant fraction of the system memory; applications are often run in batch jobs with multiple independent runs submitted simultaneously.
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PERFORMANCE ANALYSIS OF TRANSPORT PROTOCOL DURING LIVE MIGRATION OF VIRTUAL MACHINES

PERFORMANCE ANALYSIS OF TRANSPORT PROTOCOL DURING LIVE MIGRATION OF VIRTUAL MACHINES

Abstract—The Physical servers used in IT are under-utilized. The better utilization of these servers can be achieved using virtualization technology. Virtualization techniques create multiple partitions which are isolated with each other called virtual machines. Each virtual machine (guest) runs their own operating system. The resource allocated for these VMs may fail to execute an application because of resource conflict or un availability of resources. This motivates towards live migration of virtual machines. The live migration copies the running VM from source host to destination host seamlessly using TCP as transport protocol.This paper evaluates performance of TCP in live migration of KVM based virtual machines. The flexibility in UDP which drives the concentration can also be used for this migration.
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Virtual-GEMS: An Infrastructure To Simulate Virtual Machines

Virtual-GEMS: An Infrastructure To Simulate Virtual Machines

Recently, virtualization has become a hot topic in com- puter architecture research. The cost reduction and man- agement simplification brought by server consolidation are good reasons why virtualization has become so popular. But there is a lack of tools for researchers to seek new propos- als of architectures that improve the performance of virtu- alized systems. To fill this niche we have developed Virtual- GEMS, a multiprocessor simulator that allows us to simu- late the behavior of a virtualized system and research new architectures suitable for virtualization. For testing Virtual- GEMS, we describe and evaluate some configurations for the shared L2 cache of a 16-core CMP running 4 virtual machines.
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Virtual Machines and Their Architecture

Virtual Machines and Their Architecture

To reduce performance losses, dynamic binary translators sometimes perform code optimizations during translation. This capability leads naturally to VMs wherein the instruction sets that the host and guest use are the same, with optimization of a program binary as the VM’s sole purpose. Same- ISA dynamic binary optimizers use profile infor- mation collected during the interpretation or translation phase to optimize the binary on-the-fly. An example of such an optimizer is the Dynamo system, originally developed as a research project at Hewlett-Packard. 2

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VIRTUAL MACHINES AND NETWORKS INSTALLATION, PERFORMANCE, STUDY, ADVANTAGES AND VIRTUALIZATION OPTIONS

VIRTUAL MACHINES AND NETWORKS INSTALLATION, PERFORMANCE, STUDY, ADVANTAGES AND VIRTUALIZATION OPTIONS

VMware GSX server [1] can be the best candidate for an academic setting as it is enterprise- level virtual infrastructure software for x86-based machines/servers. VMware GSX Server allows virtual machines to be remotely managed, automatically provisioned, and standardized on a secure, uniform platform. Required operating systems and related applications can reside in multiple virtual machines on a single host physical hardware. VMware GSX server provides broad hardware support by inheriting device support from the host operating system. The product's robust architecture and ability to integrate into Microsoft Windows and Linux host environments make it quick and easy to deploy and manage. VMware GSX server runs as an application on a host operating system; manages and remotely controls multiple servers running in a virtual environment. The advantages of this option are the ability for customization and provisioning of virtual images/configurations, a user interface for easy management of multiple virtual machines sessions, secure access with OpenSSL, secure remote management, automated monitoring and control, web interface for instructors and students to authenticate and access their virtual machines, integration with campus Active Directory and support for popular Linux distributions. The VMware GSX server has high initial cost for the software and hardware and there would be also an on-going maintenance cost. But the benefits and outcomes would be far greater than the traditional option of buying dedicated hardware and software to run only operating system per machine.
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Performance Comparison and Tuning of Virtual Machines For Sequence Alignment Software

Performance Comparison and Tuning of Virtual Machines For Sequence Alignment Software

5.1.1. BWA. The results of running BWA on Machine 1 and all four execution environments (physical server, Kernel Virtual Machine, para-virtualised Xen and Linux Containers) are shown in Fig. 5.1.a. (Note that only one virtual machine was running at a time.) In this test, a paired end alignment with BWA was run 30 times on the chr1 dataset, and a mean of the run time was obtained. In all cases, the application (BWA) was run single-threaded. Since many sequencing pipelines involve large amounts of data, it is not uncommon for users to run alignment and variant calling applications single-threaded and parallelise across datasets (e.g., to parallelise across chromosomes when mapping an entire genome). Here, we see that LXC’s performance closely matches that of the physical server, but Xen and KVM have significant overhead. We reiterate that these results are consistent across runs on both of the servers with Machine 1’s hardware configuration. However, when considering the performance on Machine 2 (cf. Fig. 5.1.b), one can see a stark difference from Machine 1. In particular, Xen exhibits a much larger overhead for Machine 1. This difference will be investigated in Sect. 5.2.2.
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Large Installation Administration. Comparing open source deduplication performance for virtual machines

Large Installation Administration. Comparing open source deduplication performance for virtual machines

An example of efficient resource utilization is virtualization [2]. Many bare-metal systems that are providing only a single service, e.g. a DNS or Mail server, often do not fully utilize the available resources. By using virtualization, a bare-metal server can be used to run multiple virtual operating systems using virtualization technology. From a user point of view, the OS appears isolated and stand- alone. Effectively, one system can be used to create multiple, virtual, systems increasing the utilization of the resources of the bare-metal server. However, each virtualized system still requires the storage of its operating system files, which is often stored on a central storage repository accessible over the network. In scenarios where the same OS is virtualized more than once, the same files are likely to be stored more than once as well. According to Keren Jin and Ethan L. Miller [3], making use of data deduplication for storing virtual disks helps considerably in consolidating storage. However, practical data deduplication implementations and virtual guest performance have not been researched. Data deduplication is a method to eliminate duplicate copies of the same data blocks, which can be used to reduce the required amount of space in storage systems like described in the examples above.
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Migration of Virtual Machines for Better Performance in Cloud Computing Environment

Migration of Virtual Machines for Better Performance in Cloud Computing Environment

Virtualization is a large technology that means it provides an abstract environment whether this is virtual hardware or operating system for the run of applications. This term is often synonymous with hardware virtualization, which plays major role in efficiently delivering Infrastructure as service solutions for cloud computing. In fact, virtualization technologies have a long trail in the history of computer science and have come into many flavors by providing virtual environments at operating system level, programming language level and application level. Moreover, virtualization technologies not only provide a virtual environment for executing applications, but also for storage, memory, and networking.
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HPC-VMs: Virtual Machines in High Performance Computing Systems

HPC-VMs: Virtual Machines in High Performance Computing Systems

The other source of performance penalties for virtual machines has to do with executing privileged instructions like managing memory and I/O operations. Most hardware platforms (including x86 and x86-64) and most operating systems have multiple levels of privilege, often called privilege rings. The x86 and x86-64 architecture has four privilege rings numbered 0 through 3, as depicted in Figure 3. Most x86 and x86-64 operating system including Windows and Linux only use rings 0 and 3 for supervisory and user privileges, respectfully. In order for a ring 3 user application to execute privileged operations like requesting I/O or memory management services, the application must make a system call into the operating system kernel, where carefully vetted ring 0 supervisor level kernel code executes the privileged request on behalf of the application.
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Live Migration of Virtual Machines

Live Migration of Virtual Machines

7.2 Wide Area Network Redirection Our layer 2 redirection scheme works efficiently and with remarkably low outage on modern gigabit networks. How- ever, when migrating outside the local subnet this mech- anism will not suffice. Instead, either the OS will have to obtain a new IP address which is within the destination sub- net, or some kind of indirection layer, on top of IP, must ex- ist. Since this problem is already familiar to laptop users, a number of different solutions have been suggested. One of the more prominent approaches is that of Mobile IP [19] where a node on the home network (the home agent) for- wards packets destined for the client (mobile node) to a care-of address on the foreign network. As with all residual dependencies this can lead to both performance problems and additional failure modes.
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Performance and Energy-Based Cost Prediction of Virtual Machines Auto-Scaling in Clouds

Performance and Energy-Based Cost Prediction of Virtual Machines Auto-Scaling in Clouds

Other work in the literature has proposed an auto-scaling approach to improve the performance in Clouds. For instance, a new performance metric called the Auto-scaling Demand Index (ADI) is introduced in [6]. The approach evaluates several auto-scaling strategies, including (reactive, conservative and predictive) using log traces from Google datacentres and used the utilisation level as a performance indicator. However, this approach is dealing with VM utilisation only, without reference to the auto-scaling costs, including (e.g. energy cost) or the SLA violation. Besides, no details are provided on where/how the experiments were conducted. An efficient auto-scaling approach to dynamically scale cloud instances based on task's deadline constraints and cost is presented in [7]. This approach is implemented on Microsoft Azure platform using both simulated and real applications. However, the energy efficiency of the candidate PM that will host the scaled instance is not considered when designing such mechanism.
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Virtual Putty: Reshaping the Physical Footprint of Virtual Machines

Virtual Putty: Reshaping the Physical Footprint of Virtual Machines

While existing algorithms coupled with the princi- ples described above provide guidance for reducing the physical footprint along individual resource dimensions, reshaping the VM along one resource dimension (e.g., memory) can have adverse consequences along other re- source dimensions (e.g., CPU), as demonstrated in Fig- ure 4. Furthermore, the optimal placement is depen- dent on higher-level system policies, which specify re- quirements in terms of potentially conflicting goals such as power-savings, performance and reliability. For ex- ample, a public cloud provider may desire to mini- mize hosted VMs’ consumption of power, bandwidth and memory, even if it comes at the expense of increased CPU time and disk space (which the user is paying for). In contrast, a private cloud (owned by the cloud users themselves), featuring an abundance of hardware and fast interconnects, might seek to minimize power con- sumption and execution time, regardless of bandwidth, memory and disk utilization.
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FPGA-Based System Virtual Machines

FPGA-Based System Virtual Machines

value is the mean value of R(host, of f ) over all results with a block size smaller or equal than 16 kByte. This value is used as a reference for read and write performance measurement. For the interpretation of the results, it necessary to mention, that both machines use a 16 kB direct mapped cache with write-through strategy. The cache size can be easily figured out in the read performance chart. The write through strategy is the reason for the constant lines in the write performance chart. The general conclusion for the cache/memory access performance measurement is: Both, the host and the guest system, perform identical. Reduced performance for both systems results from the shared memory controller. The amount of performance reduction is the same for both.
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Securing your virtual machines

Securing your virtual machines

CA's  Service  Operations  Insight  can  visualize  and  analyze  an  entire  infrastructure,   including  both  cloud  and  physical  applications  and  transactions  together,  in  the   context  of  the  business  services  they  support.  It  correlates  and  analyzes  information   from  infrastructure,  application  performance  and  other  IT  management  tools  in  real   time.  This  information  is  used  to  map  and  display  IT  assets  that  deliver  specific   business  services,  calculate  service  quality,  and  identify  which  IT  assets  impact   service  quality  and  put  it  at  risk.  The  idea  is  to  model  an  end-­‐to-­‐end  and  real-­‐time   view  of  services  across  the  enterprise.  It  can  automate  actions  that  re-­‐allocate  data   center  and  cloud  resources  to  quickly  fix  service  problems.  "Impact  analysis  from  CA   Service  Operations  Insight  allows  for  quick  problem  identification,  notification  and   automated  help  desk  ticketing,  so  service  quality  problems  can  be  quickly  resolved   and  other  risks  to  business  services  can  be  mitigated,”  says  Dan  Colleli,  a  monitoring   technician  with  Raymond  James  and  a  CA  Insight  customer.  All  this  insight  doesn't   come  cheap:  a  site  license  for  CA  Service  Operations  Insight  starts  at  $175,000  for   up  to  five  data  sources.  
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Hosting Virtual Machines on Distributed Datacenters

Hosting Virtual Machines on Distributed Datacenters

Almost of cloud services nowadays are built at top geo- graphically distributed infrastructure for better reliability and performance. These cloud providers need an efficient method to control and direct user workload to suitable dat- acenter, depending on many factors such as: network traf- fic cost, operation cost budget, energy consumption, etc. In the virtual machine placement problem, current works mainly focus on the efficiency of packing virtual machines into servers and ignore the distributed scenario of datacen- ters. In this paper, we consider the problem of placing vir- tual machines to host applications on a shared resource pool based on distributed cloud platforms. We formulate the problem of hosting virtual machines on distributed datacen- ters as an optimization problem, and propose a distributed framework DHC that can dynamically direct workload be- tween datacenters to maximize total utility of entire data- centers. We also conduct many case studies to validate our method, and evaluate its effectiveness and practicality, us- ing real-workload traffic traces. The simulation results show that our algorithm can dynamically optimize the total util- ity, depending on various workload of users.
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Service level agreements and virtual machines

Service level agreements and virtual machines

This contract enables the provider of the services to be more efficient in the ways their servers run particular services. The agreed upon SLA provides guidelines and metrics that need to be adhered to in order for the contract to remain valid. Creating and abiding by these rules improves the providers awareness of their machines performance, as well as any problems with their implementations. In order to know when the servers are or are not meeting the agreement, monitoring needs to be established to track the status of the resources. This monitoring can be manual, where a systems administrator assesses the metrics by hand, or it could be performed automatically by software which has been configured to evaluate the individual metrics defined by the SLA. Proper monitoring allows the system administrators the time to find, and correct, the resources not meeting the standards contained in the SLA before a potential server crash, or system loss. Monitoring is part of an SLA to maintain appropriate system function, however, an additional benefit occurs when due to proper monitoring, a provider can refute a breach of contract claim by a user.
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Contextualization: dynamic configuration of virtual machines

Contextualization: dynamic configuration of virtual machines

The above scenario has been used to illustrate the fea- sibility of the suggested approach through a series of tests. The tests were performed on host machines with quad core Intel i7-920 CPU @ 2.66GHz, 24GB DDR3 @ 1333MHz, 1GBit NIC and two 1500GB 7200RPM HDDs in RAID1. The following software inclusive of version numbers were used: Debian 6 (Kernel 2.6.32-5- xen-amd64), XEN 4.0.1, Libvirt 1.0.5 and GlusterFS 3.3.2. Three paravirtualized VMs were created, two acting as Gluster servers serving a “brick” each as a replicated vol- ume and a third as a Gluster Client where the tests were performed on the exported file system stored within the volume. In these tests, traffic shaping techniques (man- aged via the Linux Kernel Traffic Control command tc) have been used to simulate network delays and laten- cies between different hosts. The tests show how network delay and latencies affect the I/O performance of the Glus- ter FS distributed file system. The remote host in the test scenario is limited to 25Mbit of bandwidth, with an aver- age 10ms latency. The latency has a 2 % variance following a normal distribution and 25 % correlation with the previ- ous traffic to simulate network fluctuations. The local host is connected with a 500Mbit connection and a measured average latency of 1.02ms (0.5 % variance). These settings correspond to typical network conditions of WAN and LAN networks, respectively. The I/O testing is performed by running a series of tests using the Bonnie++ I/O testing framework [38]. Results from the tests are shown in Figs. 6 and 7.
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A Case for High Performance Computing with Virtual Machines

A Case for High Performance Computing with Virtual Machines

6.1 Experimental Setup We conducted our performance evaluations on an eight- node InfiniBand cluster. Each node in the cluster is equipped with dual Intel Xeon 3.0GHz CPUs, 2 GB of memory and a Mellanox MT23108 PCI-X InfiniBand HCA. The systems are connected with an InfiniScale InfiniBand switch. The same cluster is used to obtain performance results for both the VM-based environment and the native, non-virtualized environment. In VM-based environments, we use Xen 3.0. The Xen domain0 hosts RedHat AS4 with kernel 2.6.12 with 256MB memory. All user domains are running with a single virtual CPU and 896 MB memory, which allows two Do- mUs per physical machine. Each guest OS in DomUs uses the 2.6.12 kernel with all unnecessary services removed. The OS is derived from ttylinux [36], with minimum changes in order to host MPI applications as mentioned in Section 5.3. In the native environment, we also use RedHat AS4 but with SMP mode.
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Virtual machines for virtual worlds

Virtual machines for virtual worlds

Abstract: Multi User Virtual Worlds provide a simulated immersive 3D environment that is similar to the real world. Popular examples include Second Life and OpenSim. The multi-user nature of these simulations means that there are significant computational demands on the processes that render the different avatar-centric views of the world for each participant, which change with every movement or interaction each participant makes. Maintaining quality of experience can be difficult when the density of avatars within the same area suddenly grows beyond a relatively small number. As such virtual worlds have a dynamic resource-on-demand need that could conceivably be met by Cloud technologies. In this paper we make a start to assessing the feasibility of using the Cloud for virtual worlds by measuring the performance of virtual worlds in virtual machines of the type used for Clouds. A suitable benchmark is researched and formulated and the construction of a test-bed for carrying out load experiments is described. The system is then used to evaluate the performance of virtual worlds running in virtual machines. The results are presented and analysed before presenting the design of a system that we have built for managing virtual worlds in the cloud.
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