Virtualization Management Traditional IT architectures are generally based in silos, with dedicated computing resources to specific applications and excess or resourcing to accommodate peak demand of the workload and potential future growth. Virtualization systems have been replacing these silos with a layer of shared IT resources, multiplexed between the demands of dynamic workloads. This consolidation of resources and workloads has resulted in dramatic increases in productivity, improving the efficiency of the IT infrastructure and better application performance while reducing IT costs. However, virtualization leads to new challenges in operations management, beyond the reach of traditional paradigms. In particular: The behavior of the use of resources by virtual machines (VMs) and their performance can be dramatically different from physical servers. In contrast to physical servers, in a VM, resources fluctuate dynamically and may experience interference from other virtual machines that share the same physical host. Virtualization increases the utilization of physical resources, possibly leading to applications beyond the limits of operations and creating problems of quality of service (QoS). Virtualization eliminates borders, making each layer of the IT infrastructure more sensitive to interference from other. Such interference can lead to decreased performance, availability and efficiency at every layer of the IT infrastructure.
Therefore, virtualization technologies required performance management resources designed to handle these complex factors. These technologies must replace manual partitioning management with unified, proactive, scalable and automated resources management.
Virtualization Management Traditionally, IT infrastructures have been shaped by server s farms, whose resources are dedicated to a specific applications. Take for example two applications: APP1 and APP2 (Figure 1). These applications will provided by physical servers equipped with necessary resources to process each application efficiently. In the best cases, these servers share storage devices organized in a SAN (Storage Area Network) in addition to a communications network LAN. The average workload of these servers is usually a small percentage of their peak traffic. In this sense, during normal periods of load, server capacity greatly exceeds the application needs, producing highly efficient performance but low resource utilization.
The management of these infrastructures focuses on monitor each server individually. Each server has its own configuration and their operating parameters are evaluated individually. Thus, the operational management of these environments focuses on the management of different server configurations, with resources and performance relegated to incremental capacity to handle workloads during peak periods. Using Virtualization Virtualization eliminates the limits on the handling of resources between applications. The "Hypervisors", packaged in virtual machines (VMs) the physical resources and share them between the workloads of multiple applications as shown in Figure 2. This architecture of shared resources can dramatically improve utilization of resources as well as enable a more flexible growth of resources and workloads.
Virtualization transforms the foundations of traditional operations management: Removes limits and provides consistency for configuration management. This simplifies the configuration management using common "templates". Aun cuando las máquinas virtuales son indistinguibles semánticamente de los servidores físicos, sus comportamientos suelen ser distintos. Even when virtual machines are semantically indistinguishable from physical servers, their behaviors are often different. The performance of a physical server is independent of other servers. The VMs that share a physical server can interfere with other VMs hosted on the same physical server. This creates a complex challenge of operations management by moving the focus of configuration management into the management and administration of shared resources and their performance. Consolidating flows workload increases average resource utilization. If workloads are uncorrelated, its peaks can be dispersed and adjusted by excess of capacity required for individual workloads. If, however, the underlying workloads are correlated, their peaks can be composed, resulting in bottlenecks, performance degradation and
failures. The virtualization management must provide protection against such problematic and dynamic scenarios. The physical infrastructures are limited to ensuring application performance through the oversized capabilities. Instead, VMs require automated technology of asset management to provide the best performance for applications. Virtualization eliminates the boundaries between the applications, allowing cross-interference of the respective elements and problems propagation. The traditional partition management requires a complex coordination between infrastructure, application, storage and network administrators to resolve such problems. Virtualization management technologies required to unify the monitoring, analysis and control elements to avoid these complexities. The virtual infrastructures offer the potential to automatically reduce the need for intensive labor managing. Monitoring, analysis and control functions should be unified and automated to allow a small number of administrators to manage large-scale infrastructure. Managing balance between performance and resource utilization A simple way to handle the challenges mentioned above is to consider the balance between performance and resource utilization. Traditional performance analysis uses delay curves in the use of resources, as shown in Figure 3. The use of a resource (or service), represented by the horizontal axis, is often defined as the ratio between the arrival rate of workload and its rate of service. It represents the amount of new demand for services that comes during a time unit of service, also known as workload processed by the resource. The vertical axis represents the service performance,
measured in terms of the delay produced by the average size of the queue waiting for service. As resource utilization increases, so does the delay. When utilization is low, the delay consists entirely of processing time through the service. As utilization increases beyond a certain risk threshold, the buffers are filled, resulting in congestion, bottlenecks (sustained congestion), overflow of traffic, and failure. If utilization continues to increase, these conditions will exacerbated congestion.
Without virtualization, each physical server must adapt to fluctuations in utilization between average and peak traffic. If this gap is large, the resource will be used on most of the time. Resources and performance management is mainly reduced to a oversizing of server resources with the assurance that the peaks are well managed. Virtualization consolidates multiple streams of workload to improve the average utilization. The average utilization is the sum of the individual flow utilization and consequently its moves to the right. If workloads are uncorrelated, peak workloads aggregates could be attended at similar levels of individual flows. As a result, the average utilization can grow while retaining the peak utilization by improving resource efficiency. However, occasional correlations or peak workloads can push the resource utilization beyond the risk thresholds. This could result in congestion, bottlenecks, losses and failures. Therefore, performance management can no longer be managed through excessive resource endowment and must proceed with dynamic real-time decisions. Its leads to new challenges substantially. To illustrate these challenges, it is useful to compare the bottlenecks management in traditional systems to virtualized systems. A bottleneck is, by definition, a resource experiencing a sustained or intermittent congestion. Bottlenecks are generally produced during peak hour traffic. Consider first
the architecture of Figure 1. Application administrators of APP 1 application can anticipate potential bottlenecks during peak time between 3 and 5 pm. They detect these bottlenecks by monitoring threshold events. For example, bottlenecks in the storage path lengths can manifest in excessive tail. Solving this problem is simple: avoid bottlenecks by provisioning additional capacity to absorb the peaks (ie, implementing a host bus adapter (HBA) higher bandwidth). While this excess capacity is lost during off-peak hours. That is, eliminates bottlenecks in the most expensive way. Bottlenecks in virtualized systems can be much more complex to detect and isolate. At the same time, virtualization allows more flexible solving strategies. More generally, several key factors influence the complexity of virtualization management: Interference: workloads that share a resource may disturb other workload. Ambiguity: the operational data of a resource may reflect the aggregate behavior of multiple streams of workload sharing that resource, making it difficult to interpret the data and isolate the effects of individual flows. Fragmentation: The configuration management is fragmented along the borders of the servers and the elements. However, performance problems can spread among multiple elements and systems that require complex coordination between virtualization administrators, applications and storage. Overutilization: leads to a greater likelihood of performance problems, bottlenecks and failures.
Hypervisor Complexities: the Hypervisor mechanisms can lead to management problems. For example, some virtual machines may require a symmetric multiprocessor (SMP). The hypervisor provides semantic SMP by scheduling concurrent virtual CPUs. This can lead to problems of co-programming. Similar problems arise performance through memory management mechanisms of hypervisor. No scalability: In Partitioned, manual management requires hours of administrator in proportion to the number of elements and the speed of their change events. Virtualization increases the scaling factors, stimulates an increased rate of deploying virtual machines, compared with the deployment of physical hosts; and supports flexible dynamic changes that increase the rate of change events. Therefore, partitioning manual management is inherently not scalable to systems virtualization. These factors of complexity virtualization management reflect needs of new technologies of performance management that can transcend the limits of traditional management of the central IT infrastructure.
Virtualization eliminates borders, making each layer of the IT infrastructure more sensitive to interference from other. Such interference can lead to decreased performance, availability and efficiency at every layer of the IT infrastructure