Telecom Engineering Center

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1 Department of Telecommunications Telecom Engineering Center Khurshid Lal Bhavan, Janpath, New Delhi Study Paper on Cloud computing Virtualisation & Resource Management R. Saji Kumar Director, J.M.Suri DDG, I Division, Telecom Engineering Center, Department of Telecommunications, New Delhi. Abstract In cloud computing architecture where centralised resources like CPU, Memory, Disk space, Input/Output functions etc are shared among multiple users, virtualisation and efficient resource management are key to its success. Virtualization is the process of decoupling the hardware from the operating system on a physical machine. Resource Management is this process of managing the Physical Resources like CPU, Memory, Network etc across various Virtual Machines (VM) based on policies. The policies of cloud management critical for the cloud resource management, generic and functional requirements are elaborated. The various methods adopted for CPU, Memory and IO virtualisation and resource management are discussed in detail in this paper. This paper also gives a sketch of the benefits and limitations of virtualisation. Key words Cloud computing, Virtualisation, Cloud Resource Management, Host Machine, Virtual Machine, Virtual Machine Monitor, Hypervisor, CPU scheduling, CPU load balancing, Memory virtualisation, IO virtualisation. 1. Introduction Cloud Computing is a model for enabling service users to have ubiquitous, convenient and on- demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services), that can be rapidly provisioned and released with minimal management effort or service-provider interaction. The Cloud will provide IT similar to public utilities providing electricity, gas, and water. There is no need to have to own the hardware & the staff. There will be multiple public cloud providers. The generalised architecture of cloud computing is given in figure-1 below.

2 Figure-1: Cloud computing Architecture Infrastructure as a service refers to the sharing of hardware resources for executing services, typically using virtualization technology. With this so-called Infrastructure as a Service (IaaS) approach, potentially multiple users use existing resources. The resources can easily be scaled up when demand increases, and are typically charged for on a per-pay-use basis. In the Platform as a Service (PaaS) approach, the offering also includes a software execution environment, such as an application server. In the Software as a Service approach (SaaS), complete applications are hosted on the Internet. Virtualization is the process of decoupling the hardware from the operating system on a physical machine. Virtualization can be thought of essentially as a computer within a computer, implemented in software. This is true all the way down to the emulation of certain types of devices, such as sound cards, CPUs, memory, and physical storage. The Figure-2 below describes how the physical resources like servers, storage and network are separated from the operating systems and the applications using the virtual infrastructure. Figure-2: Hardware / Software Separation in a Virtualised Environment

3 Cloud Computing takes Virtualization to the Next Step. Virtual Machines & services can be rented as needed from a cloud service provider. However for the management of such a process requires that the resources be managed in an efficient way. Resource Management is this process of managing the Physical Resources like CPU, Memory, Network etc across various Virtual Machines (VM) based on policies. The figure-3 below gives a frame work of the cloud infrastructure with resource orchestration. Figure-3: Framework of cloud infrastructure with Resource orchestration. 2. Virtualisation Virtualization is the process of decoupling the hardware from the operating system on a physical machine. An instance of an operating system running in a virtualised environment is known as a virtual machine. Figure-4 below gives an architecture of the virtualisation with multiple virtual machines sharing the same hardware resources.

4 Figure-4: Virtualisation Architecture Virtualisation technologies ogies allow multiple virtual machines, with heterogeneous operating systems to run side by side and in isolation on the same physical machine. By emulating a complete hardware system, from processor to network card, each virtual machine can share a common set of hardware unaware that this hardware may also be being used by another virtual machine at the same time. The operating system running in the virtual machine sees a consistent, normalized set of hardware regardless of the actual physical hardware components. Virtualisation is adopted in cloud architecture because of the following reasons. a. Hardware independence: The guest VM sees the same hardware regardless of the host hardware b. Isolation VM s operating system is isolated from the host operating system c. Encapsulation Entire Entire VM encapsulated into a single file d. Simplified administration because of Hardware independence & portability, Increased hardware utilization, Server consolidation, Decreased provisioning times and Improved security e. The other reasons include reduced capital expenditure, reduced operating expenditure, reduced risks of data outage and reduced energy consumption. 2.1 Terminologies used in Virtualisation: Following are some of the terminologies used in virtualisation. Host Machine: A host machine is the physical machine running the virtualization software. It contains the physical resources, such as memory, hard disk space, and CPU, and other resources, such as network access, that the virtual machines utilize.

5 Virtual Machine: The virtual machine is the virtualized representation of a physical machine that is run and maintained by the virtualization software. Each virtual machine, implemented as a single file or a small collection of files in a single folder on the host system, behaves as if it is running on an individual, physical, non-virtualized PC. Virtualization Software: Virtualization software is a generic term denoting software that allows a user to run virtual machines on a host machine. Virtual Disk: Virtual Disk is the virtual machine s physical representation on the disk of the host machine. A virtual disk comprises either a single file or a collection of related files. It appears to the virtual machine as a physical hard disk. Shared Folders: Shared folders enable the virtual machine to access data on the host. Most virtual machine implementations support the use of shared folders. Virtual Machine Monitor (VMM): A virtual machine monitor is the software solution that implements virtualization to run in conjunction with the host operating system. The virtual machine monitor virtualizes certain hardware resources, such as the CPU, memory, and physical disk, and creates emulated devices for virtual machines running on the host machine. Hypervisor: In contrast to the virtual machine monitor, a hypervisor runs directly on the physical hardware. The hypervisor runs directly on the hardware without any intervening help from the host operating system to provide access to hardware resources. The hypervisor is directly responsible for hosting and managing virtual machines running on the host machine. 3. Resource Management Resource Management is the process of managing the Physical Resources like CPU, Memory, Network etc across various Virtual Machines (VM) based on policies. The figure-4 below gives positioning of resource management in the management and distributed services in virtualisation architecture.

6 Figure-4: Management & Distributed Services in Virtualisation Architecture The Goal of resource management is three fold. a) Performance isolation: This prevents VMs from monopolizing resources and Guarantees predictable service rates b) Efficient utilization: This is achieved through exploiting under committed resources and over commit with graceful degradation c) Support flexible policies: Absolute service-level agreements are met and relative importance of VMs is controlled efficiently. Resources management requires complex policies and decisions for multi-objective optimization in cloud computing. A cloud is a complex system with a very large number of shared resources subject to unpredictable requests and affected by external events it cannot control. Cloud resource management is extremely challenging because of the complexity of the system which makes it impossible to have accurate global state information and because of the unpredictable interactions with the environment. 3.1 Policies for Cloud management: Polices for cloud management can be grouped into the following five classes. 1) Admission control: The explicit goal of an admission control policy is to prevent the system from accepting workload in violation of high-level system policies; for example, a system may not accept additional workload which would prevent it from completing work already in progress or contracted. Limiting the workload requires some knowledge of the global state of the system; in a dynamic system such knowledge, when available, is at best obsolete. 2) Capacity allocation: Capacity allocation means to allocate resources for individual instances; an instance is an activation of a service. Locating resources subject to

7 multiple global optimization constraints requires a search of a very large search space when the state of individual systems changes rapidly. 3) Load balancing: The common meaning of the term load balancing is that of evenly distributing the load to a set of servers. 4) Energy optimization: In cloud computing a critical goal is minimizing the cost of providing the service and, in particular, minimizing the energy consumption. This leads to a different meaning of the term load balancing; instead of having the load evenly distributed amongst all servers, we wish to concentrate it and use the smallest number of servers while switching the others to a standby mode, a state where a server uses very little energy. 5) Quality of service (QoS) guarantees: A service level agreement (SLA) often specifies the rewards as well as penalties associated with specific performance metrics. 3.2 General Requirements of Resource Management (i) Resource Management provides the user with a unified interface for using all the heterogeneous resources without caring about their real type. (ii) It supports computing, storage, and network resources management. (iii) Resource management supports both virtual and physical resources. (iv) It shields the user from the changing nature (dynamicity) of the performance of cloud resources. (v) It evaluates the performance of each resource to fulfil the QoS of each user request. (vi) It supports a unified resource management interface between different types of hypervisors and the cloud resource management so as to integrate different types of heterogeneous resources. 3.3 Functional Requirements of Resource Management (i) It provides a unified interface for heterogeneous resource, whether virtualized or physical, to upper-layers for management and utilization. (ii) It provides elastic, dynamic, on-demand and automation management for the down-layers, based on user-defined policies by providing resource access control interfaces to the upper-layers. (iii) It provides the capability to describe groups of computing, storage and network resources for easy allocation and deployment to satisfy application/service resource demand of the upper-layers by use of templates and their management. (iv) It provides unified management of the physical devices, including configuration information and topology of assets. (v) The requirements can be classified as below a. Resource encapsulation b. Resource orchestration and provisioning c. Assets management d. Template management e. Cloud service monitoring f. User resource environment management Resource encapsulation (i) The heterogeneous resources can be accessible through a unified interface, which shall be able to create, locate, provision, recover and delete the resources.

8 (ii) (iii) All the physical and virtual resources are managed in a unified manner through resource encapsulation. The attribute of each resource, including resource deployment, status, capacity, execution, exception, error and interrupt shall be measurable and searchable Resource orchestration and provisioning (i) It provides a unified interface to upper-layers for management and execution. (ii) All resources are flexible, on-demand and automation orchestrated, deployed, and provisioned, based on the pre-defined policies that include high availability, load balance, resource migration, energy efficiency and storage deployment. (iii) It provides on-demand and automation management for the lower-layers based on pre-defined policies, and access control interfaces to the upperlayers. (iv) It is possible to dynamically allocate the resources by real-time monitoring of applications and SLAs. (v) It is also possible for the services to be analyzed and to be translated into resource requirements and to trigger appropriate actions Assets Management (i) (ii) (iii) Asset management provides unified management of the physical devices, including asset information management. Assessment attributes (Hardware: racks, servers, storage devices, network equipment, and VMs; Software: hypervisors, operating systems, middleware, databases, applications, licenses, and so on) and topology of physical devices are managed in a unified manner. It automatically updates the assessment attribute when the physical devices are changed Template management (i) It provides life cycle management of each resource, including creation, publication, activation, revocation, deletion etc. (ii) It provides management of life cycles of templates, including creation, publication, activation, revocation, deletion, template provision, etc Cloud service Monitoring (i) All physical and virtual resources are monitored (like physical servers, virtual monitors/hypervisors, virtual machines, physical and virtual disks, physical and virtual network and applications). (ii) The architecture of resource monitor are multi-layered, including service instance monitoring, physical resources monitoring, resource pool monitoring, user connection monitoring, software monitoring, etc. (iii) System is able to detect the exceptions or errors of computing, storage, network equipment and the resources pool without affecting the monitoring of existing users.

9 3.3.6 Health monitoring (i) It is possible to monitor the health of both the physical and virtual infrastructure like physical server hardware status, hypervisor status, virtual machine status, physical and virtual network switches and routers, and storage systems. (ii) It is implemented as a service model, which can be regarded as a map which displays all of the technology components, including transactions, applications, web servers, network switches, virtualized components, and third-party cloud services. (iii) The service model provides run-time monitoring of the constantly changing service infrastructure. (iv) An integrated operation bridge consolidates event and performance data from both physical and virtual sources to reduce duplicate monitoring. (v) Automatic remediation capabilities reduce mean time to repair (MTTR) Performance monitoring (i) It looks at the performance of the CPU, memory, storage and network from the VM guest OS as well as from the hypervisor. (ii) The metrics are monitored both in virtualized and non-virtualized environments Capacity monitoring The key metrics monitored for capacity planning are: (i) Server utilization: Peak/average server resource utilization - memory /CPU/resource, server bottlenecks and correlation with a number of users/vms. (ii) Memory usage: Memory utilization on each server, capacity bottlenecks and relationship with number of users/vms and with different cloud services. (iii) Network usage: Peak/average network utilization, capacity/bandwidth bottlenecks and relationship with a number of users/vms and with different cloud services. (iv) Storage utilization: Overall storage capacity metrics, VM/virtual disk utilization, I/O performance metrics, snapshot monitoring and correlation with a number of users/vms and with different cloud services Security and compliance monitoring Security and compliance monitoring provides metrics for the following key functions - (i) (ii) (iii) (iv) VM sprawl: Metrics to monitor the VM activities as they get cloned, copied, move of network, move to different storage media etc. Configuration metrics: Virtual server configuration monitoring, VM configuration monitoring for software licensing policy enforcement. VI Events that help enforce/detect violations of IT policy, individual security and organization security policy etc. Access control: Access control monitoring and reports for role-based access control enforcement. Compliance monitoring: Metrics to validate/audit IT for confirming to various regulatory requirements.

10 Monitoring and metering for charging and billing (i) In a virtualized environment, where the infrastructure is centralized, it measures resource usage by different business units, groups, and users. This information can be used to distribute/amortize and, in some cases, recover the cost correctly across the organization through a proper chargeback mechanism. (ii) To compute the correct chargeback information in a dynamic virtualized environment, it monitors virtual/physical resource usage, service usage and allocations. (iii) It normalizes the measurement statistics across the cloud infrastructure. (iv) It is also possible to obtain the following metrics - a. Standard metrics: All chargeable resource metrics like CPU usage, memory usage, storage usage (volume and time), and network usage (bandwidth and network traffic). b. Key Virtual Infrastructure (VI) events: VI events for virtual resource life cycle events like start date and end date of VM creation and allocation. c. Configuration monitoring: VM configuration in terms of assigned resources and reservations and also applications installed to an account for software licensing costs. d. VM usage metrics: VM uptime, number of VMs etc Application and service monitoring (i) The application and service monitoring is important in the cloud computing environment for the evaluation of SLA/QoS. (ii) The system monitors the basic health of application servers, with the help of application-specific response time and throughput metrics. (iii) All kinds of virtualization software (server virtualization, storage virtualization, network virtualization, etc.,) provide suitable API for collection of metrics. (iv) The systems have suitable analytics software for analysing the metrics and presenting the results through a suitable GUI User resource environment management (i) User resource environment includes the resources allocated to a user, the state of the resources (such as running, stopped for a virtual machine), and the topologies among the resources. (ii) Cloud infrastructure provides the secure isolation between different user resource environments to prevent the management activities in one user resource environment from impacting other user resource environments. (iii) The cloud provider gives a user appropriate control of his user resource environment. 4. CPU Resource Management and Virtualisation The underlying physical resources are used and the virtualization layer runs instructions only as needed to make virtual machines operate as if they were running directly on a physical machine.

11 4.1 CPU Virtualization (i) One physical CPU is virtualised into multiple virtualized CPU (vcpu) for multiple virtual machine instances using time sharing technologies, so that one instance may obtain at least one vcpu. (ii) The system administrator assigns vcpus for virtual machines. (iii) It is possible to guarantee or limit the performance of a virtualized CPU in a virtual machine instance. (iv) The virtual machine (hypervisor) implements the CPU function which determines the mapping between the vcpus and physical CPUs managed by the hypervisor. (v) It is possible to increase and decrease the number of CPU resources assigned to a Virtual machine at a later stage Software based Virtualisation: The guest application code runs directly on the processor, while the guest privileged code is translated and the translated code executes on the processor. The translated code is slightly larger and usually executes more slowly than the native version. As a result, guest programs, which have a small privileged code component, run with speeds very close to native. Programs with a significant privileged code component, such as system calls, traps, or page table updates can run slower in the virtualized environment Hardware based Virtualisation: The guest code, whether application code or privileged code, runs in the guest mode. There is no need to translate the code. As a result, system calls or trap-intensive workloads run very close to native speed Multi-core Processors: A dual-core processor usually can provide almost double the performance of a singlecore processor, by allowing two virtual CPUs to execute at the same time. Cores within the same processor are typically configured with a shared last-level cache used by all cores, potentially reducing the need to access slower main memory. A shared memory bus that connects a physical processor to main memory can limit performance of its logical processors if the virtual machines running on them are running memory-intensive workloads which compete for the same memory bus resources. Each logical processor of each processor core can be used independently by the CPU scheduler to execute virtual machines Hyper threading: Hyper threading technology allows a single physical processor core to behave like two logical processors. The processor can run two independent applications at the same time. While hyper threading does not double the performance of a system, it can increase performance by better utilizing idle resources leading to greater throughput for certain important workload types. Hyper threading performance improvements are highly application-dependent, and some applications might see performance degradation with hyper threading because many processor resources (such as the cache) are shared between logical processors.

12 4.2 CPU Scheduling Maximum CPU utilization is obtained with multiprogramming i.e. Several processes are kept in memory at one time and every time a running process has to wait, another process can take over use of the CPU. Scheduling is a critical component of the cloud resource management. Scheduling is responsible for resource sharing/multiplexing at several levels; a server can be shared among several virtual machines, each virtual machine could support several applications, and each application may consist of multiple threads. CPU scheduling supports the virtualization of a processor, the individual threads acting as virtual processors; a communication link can be multiplexed among a number of virtual channels, each one of them dedicated to a single flow. A scheduling algorithm should be efficient, fair, and starvation-free. Two distinct dimensions of resource management must be addressed by a scheduling policy: (a) the amount/quantity of resources allocated; and (b) the timing when access to resources is granted. Criteria for selecting a CPU scheduling algorithms is based on the CPU utilization percent of time that the CPU is busy executing a process, Throughput number of processes that are completed per time unit, Response time amount of time it takes from when a request was submitted until the first response occurs (but not the time it takes to output the entire response), Waiting time the amount of time before a process starts after first entering the ready queue (or the sum of the amount of time a process has spent waiting in the ready queue) and Turnaround time amount of time to execute a particular process from the time of submission through the time of completion. The scheduling mechanism shall also provide accurate ratebased controls, support multi-core, multi-threaded CPUs and support grouping mechanism. 4.3 Scheduling Algorithms Round-robin, first-come-first-serve (FCFS), shortest-job-first (SJF), and priority algorithms are among the most common scheduling algorithms for best effort applications First Come First Serve: The process that requests the CPU first is allocated to the CPU first Shortest Job First: When the CPU becomes available, it is assigned to the process that has the smallest next CPU burst Priority based scheduling: It associates each process with a priority and makes a scheduling choice or preemption decision based on the priorities. For example, a process with the highest priority among ready processes would be chosen, and then the process may preempt the currently running process if it is higher in the priority Proportional-Share Based Algorithm It associates each process with a share of CPU resource. The entitled resource may not be fully consumed. When making scheduling decisions, the ratio of the consumed CPU resource to the entitlement is used as the priority of the process. If there is a process

13 that has consumed less than its entitlement, the process is considered high priority and will likely be chosen to run next i.e. schedule VM with smallest virtual time Round Robin Algorithm: In the round robin algorithm, each process gets a small unit of CPU time (a time quantum), usually milliseconds. After this time has elapsed, the process is preempted and added to the end of the ready queue Multi-level queue scheduling: Multi-level queue scheduling is used when processes can be classified into groups. A multi-level queue scheduling algorithm partitions the ready queue into several separate queues. The processes are permanently assigned to one queue, generally based on some property of the process such as memory size, process priority, or process type. Each queue has its own scheduling algorithm Asymmetric Multi processor scheduling: One processor handles all scheduling decisions, I/O processing, and other system activities. The other processors execute only user code. Because only one processor accesses the system data structures, the need for data sharing is reduced Symmetric Multi processor scheduling or Symmetric Multi threading [SMT]: In SMT, each processor schedules itself. Symmetric multiprocessing systems allow several threads to run concurrently by providing multiple physical processors. SMT is a feature provided in the hardware, not the software. 4.4 CPU Load balancing or Inter-processor load balancing: On multi-processor systems, balancing CPU load across processors or load-balancing is critical to the performance. Load-balancing is achieved by having a process migrate from a busy processor to an idle processor. Generally, the process migration improves the responsiveness of a system and its overall CPU utilization. The features of interprocessor load balancing include per-processor dispatch and run queues, scanning remote queues periodically for fairness, pull whenever a physical CPU becomes idle, push whenever a virtual CPU wakes up etc. Multi-processor VM support gives an illusion of dedicated multi-processor, near-synchronous co-scheduling of VCPUs and supports hot-addition of VCPUs Load balancing in NUMA Systems: In a NUMA (Non-Uniform Memory Access) system, there are multiple NUMA nodes that consist of a set of processors and the memory. The access to memory in the same node is local while the access to the other node is remote. The remote access takes longer cycles because it involves a multi-hop operation. Due to this asymmetric access latency, keeping the memory access local or maximizing the memory-locality improves performance. On the other hand, CPU load-balancing across NUMA nodes is also crucial to performance.

14 4.4.2 Shared cache management Shared cache management allows multi-core processors to share a common cache memory. This is a hardware feature offering explicit cost-benefit tradeoffs for migrations and uses hardware cache QoS techniques Load balancing on Hyper-threading Architecture: Hyper threading enables concurrently executing instructions from two hardware contexts in one processor. Although it may achieve higher performance from threadlevel parallelism, the improvement is limited as the total computational resource is still capped by a single physical processor. Also, the benefit is heavily workload dependent. A whole idle processor, that has both hardware threads idle, provides more CPU resource than only one idle hardware thread with a busy sibling thread. 5. Memory virtualization It divides the physical memory, allocate memory for virtual machine instances when starting up, and release memory from virtual machines when shutting down. Every running instance of OS sees a continuous memory space and is isolated from the memory space of other instances. The hypervisors are capable of memory address conversion from the guest instance physical memory address to the machine physical address. The operating system of a running instance maps the application virtual memory to guest instance physical memory. It can increase the memory allocation to a guest OS at a later stage. The virtualization software has commit feature for the virtual machines. 5.1 Re-claiming unused memory The memory manager of the hypervisor detects whether the virtual memory is actually used by the guest OS or not. If not, the hypervisor shall be able to assign the unused part of the memory to another guest OS, so that the memory can be shared among the guest OS. Hence this feature is required for memory over-commitment. This is achieved through traditional method of adding transparent swap layer or using an implicit co-operation. Balooning is a method where Guest OS manages memory implicit cooperation by paging in / out of the virtual disk. In Page Sharing, multiple VMs running same OS de-duplicate redundant copies of code, data etc. 5.2 NUMA [Non Uniform Memory Access] scheduling Periodic rebalancing of the memory usage computes VM entitlements & memory locality, assign home node for each VM and migrate VMs and pages across nodes VM migration is to move all VCPUs and threads associated with VM and migrate to balance load and improve locality Page migration allocates new pages from home node and carries migration & replication

15 6. I/O device Resource Management and Virtualization Each virtual machine is capable to equip its own virtual I/O devices abstracted from the I/O devices of the physical machine and implements the mapping of virtual and physical devices. The VM s do not have any constraint on the number of virtual I/O devices. The data transferred or stored by the physical I/O devices is never shared amongst the virtual machine Operating Systems. The virtualization software allows redirection of virtual machine serial ports over a standard ard network link thereby enabling solutions such as third-party virtual serial port concentrators for virtual machine serial console management or monitoring. Figure-5: IO Virtualisation 7. Virtual machine Duplication and Migration This property allows duplicating the main virtual machine such that the duplicate virtual machine will have the same operating system and installed applications as that of the main virtual machine. It is also possible to move an operating system and its applications between a virtual machine and a physical machine or between virtual machines on different physical machines, with the operating system temporarily stopped. It supports migration of virtual machines online to other physical machines running on the same network and utilizing the same central storage. Virtualization software allows for taking snapshots of the virtual machines to be able to revert to an older state if required. 8. Distributed systems Typically in cloud architecture, there will be multiple server hardware working logically as a single server. These servers may be physically located in a single location or may be hosted at multiple locations. Virtualisation in such a scenario is more complex. The techniques involve choosing an initial host when VM powers on, migrate running VMs across physical hosts and dynamic load balancing. Cluster-wide resource management requires Uniform controls, same as available on single host, flexible hierarchical policies & delegation, configurable automation levels, aggressiveness and configurable VM affinity/anti-affinity rules

16 Distributed Power Management Powers off unneeded hosts, power on when needed again Distributed IO Management as multiple hosts access the same storage array, NIC, HBA etc. Host-level I/O scheduling for arbitrate access to local NICs and HBAs, disk I/O bandwidth management and network traffic shaping. 9. Benefits of Virtualisation Reducing hardware and software needs, improving performance and scalability, and reducing downtime are key factors in managing costs today. Virtual machines provide the means to achieve the following goals. Virtual machines allow more efficient use of resources by consolidating multiple operating environments on underutilized servers onto a smaller number of virtualized servers. Virtual machines make the manageability of systems easier. For example, you do not need to shut down servers to add more memory or upgrade a CPU. The complexity of overall administration is reduced because each virtual machine s software environment is independent from the underlying physical server environment. The environment of a virtual machine is completely isolated from the host machine and the environments of other virtual machines so you can build out highly-secure environments that are tailored to your specifications. For example, you can configure a different security setting for each virtual machine. Also, any attempt by a user to interfere with the system would be foiled because one virtual environment cannot access another unless the virtualization stack allows this. Otherwise, it restricts access entirely. You can migrate old operating systems for which it is difficult to obtain appropriate underlying hardware for a physical machine. Along these same lines, you can run old software that has not been, or cannot be, ported to newer platforms. You can run multiple, different operating systems from different vendors simultaneously on a single piece of hardware. Because virtual machines are encapsulated into files you can easily save and copy a virtual machine. You can quickly move fully configured systems from one physical server to another. Virtualization allows you to deliver a pre -configured environment for internal or external deployment scenarios. Virtual machines allow for powerful debugging and performance monitoring. Operating systems can be debugged without losing productivity and without having to set up a more complicated debugging environment. The virtual machine provides a compatible abstraction so that software written for it will run on it. For example, hardware level virtual machine will run all the software, operating systems, and applications written for the hardware. Similarly, an operating system level virtual machine will run applications for that particular

17 operating system, and a high -level virtual machine will run programs written in the high-level language. Because virtual machines can isolate what they run, they can provide fault and error containment. You can insert faults proactively into software to study its subsequent behavior. You can save the state, examine it, modify it, reload it, and so on. In addition to this type of isolation, the virtualization layer can execute performance isolation so that resources consumed by one virtual machine do not necessarily affect the performance of other virtual machines. 10. Conclusion Data centre and desktop computing successfully use virtualization for better utilization of computing capacity, to balance computing load, manage complexity and parallelism and improve security by isolation. However Virtualization may not work well for Resource-intensive applications where VMs may have RAM/CPU/SMP limitations or situations where custom hardware devices are required. Some hardware architectures or features are impossible to be virtualized as certain registers or states are not exposed. Mobile and embedded computing currently lag behind virtualisation since most hypervisors only support the x86 platform, require large memories, have poor real-time support and are inefficient with microkernel Oss. Moreover suitable open source-code hypervisors are not available Glossary of Terms CPU: FCFS: HBA: IaaS: MTTR: NIC: NUMA: OS: PaaS: PC: QoS: RAM: SaaS: SJF: SLA: SMP: SMT: vcpu: VM: VMM: Central processing unit in any computing device like personnel computers, servers etc First Come First Serve Host Bus Adapter for Fibre channel interface to Storage Array Networks [SAN] Infrastructure as a Service Mean Time to Repair Network Interface Card which generally has the Ethernet ports equipped Non-Uniform Memory Access Operating System like windows xp, Unix, Linux etc Platform as a Service Personnel Computers Quality of service Random Access Memory Software as a Service Shortest Job First Service Level Agreements Symmetric Multi Processing Symmetric Multi Threading Virtual CPU Virtual machine Virtual machine Monitor

18 References 1. ITU-T document on Cloud TR 2. GR No. TEC/GR/IT/CLI-001/01/OCT-12 on Cloud Infrastructure 3. vsphere Resource Management Guide 4. NIST Cloud computing reference architecture 5. White paper on virtualisation by Virtual management Technologies 6. PCI Data Security Standards Virtualisation guidelines 7. NIST Guide to Security for Full Virtualisation Technologies