XCP 1.0

"Xen Cloud Platform (XCP) is an open source enterprise-ready server virtualization and cloud computing platform." The recent XCP 1.0 release includes support for the OpenStack cloud orchestration software; together XCP and OpenStack provide a complete, open source, solution for building large scale cloud data centers.

One of the benefits of an open source virtualization/cloud stack is the opportunity to customize the commodity components to deliver differentiated services. This article examines the opportunity to dramatically reduce the cost of delivering cloud services using the open source, open standard instrumentation available as part of the Xen Cloud Platform.

The following diagram shown the architectural components of the Xen Cloud Platform and shows how sFlow instrumentation fits within the XCP architecture.

The Control Interface (XE/XAPI) configures and controls resources within the Resource pool (e.g. provisioning, starting, stopping and migrating virtual machine and storage resources). Enabling sFlow in XCP (provided by Open vSwitch and Host sFlow) adds highly scalable performance monitoring and usage metering of the cloud infrastructure; including visibility into physical and virtual network, server and storage activity.

Real-time monitoring allows resources to be optimally provisioned to match demand. Adjusting capacity to demand reduces costs by eliminating over provisioning and applying resources where they are needed to avoid service failures.

In addition, detailed usage data allows for differentiated billing strategies (for example charging different rates for local and non-local network traffic). Aligning billing policies with the cost of delivering services allows services to be delivered at a variety of price points and encourages customers to use resources efficiently.

The steps needed to enable sFlow monitoring in Xen Cloud Platform are described in the article, XCP 1.0 beta. Most switch vendors also support the sFlow standard (instructions for enabling sFlow on most vendor's switches are documented in articles on this blog). The combination of sFlow from switches and XCP provides the data center wide, end-to-end visibility needed for complete control.

For additional information, the sFlow presentation provides a strategic view of the role that sFlow monitoring plays in managing converged, virtualized and cloud environments.

Presentation

The way in which convergence, virtualization and cloud computing are transforming the data center; and the vital role that the sFlow standard plays in managing performance and reducing costs has been a theme on this blog over the last year.

This 12 minute video draws the threads together in a single presentation and provides a high level overview of the topics. For more information, browse the data center articles on this blog or visit sFlow.org.

Visibility in the cloud

One of the challenges in moving a virtual machine from a private data center to a public cloud like Amazon Elastic Compute Cloud (EC2) or Rackspace Cloud is maintaining visibility into performance.

The article, Cloud-scale performance monitoring, describes how the sFlow standard delivers the visibility needed to manage the cloud infrastructure. In the case of a private cloud, where the physical infrastructure and virtual machines are dedicated to a single organization, the visibility provided by the infrastructure can be shared with internal customers and used to manage the services deployed in the cloud.

However, in a public cloud the infrastructure is owned and operated by the cloud service provider and customers are typically given very little visibility into the shared infrastructure hosting their virtual machines.

For example, the diagram at the top of this article shows three virtual machines, VM1, VM2 and VM3, hosted on two physical servers, Server 1 and Server 2. If these virtual machines were hosted in a private cloud all the elements of the physical and virtual infrastructure shown in the diagram can be instrumented with sFlow providing visibility to the management team.

However, move the three virtual machines to a public cloud and only the virtual machines are visible. A Management Boundary separates service provider resources from the customer resources and it is no longer possible to know which virtual machines are hosted on which physical servers or to see network and system performance using sFlow from switches and servers.

The diagram above shows the elements from the example that are visible in a public cloud deployment. The example is representative of a typical small scale deployment: the Vyatta virtual appliance (VM3) provides routing and firewall capabilities, VM1 is configured as a web server and VM2 as a database server. One of the benefits of moving to the public cloud is the ability to scale up the number of servers to meet demand. The article, How Zynga Survived FarmVille, describes using a public cloud provider to handle rapidly changing workloads. The architecture mentioned in the article is a widely adopted, scale-out, implementation of the elements shown in the diagram - see Memcached for additional details, large scale deployments of this architecture may involve thousands of servers.

In order to provide visibility in a public cloud deployment, each virtual machine must be responsible for monitoring its own performance. The Vyatta virtual appliance already includes support for sFlow. Installing Host sFlow agents on the virtual machines extends visibility to include network and system performance throughout the virtual machine cluster - see Cluster performance.

A key benefit of deploying services in the public cloud is the ability to dynamically add and remove capacity. In this environment, sFlow monitoring helps control costs by providing the data needed to closely match capacity to demand. In addition, many organizations operate hybrid clouds with some workloads running in a private cloud and others running in the public cloud. sFlow simplifies management by delivering integrated visibility across all the physical and virtual elements in the private and public cloud, providing the measurements needed to manage costs by striking the optimal balance between public and private cloud capacity.

Power

Electricity Counter

The paper, No "Power" Struggles: Coordinated Multi-level Power Management for the Data Center, examines different power control strategies and suggests that power savings of 20% - 60% are possible using feedback control to optimize and consolidate virtual machine locations and workloads. The paper concludes, "Our results indicate that effective coordination is likely to be more important in future environments with richer diversity in workloads and increased emphasis on power reduction."

The sFlow energy metering extension, currently being developed as part of the sFlow standard, builds on the proven scalability of sFlow's counter polling mechanism to provide real-time metering of all switches, PoE ports, servers, blades and processors in the data center.

Incorporating power metering in sFlow integrates power and temperature information with workload and performance statistics from network devices and servers to deliver the real-time measurements needed for coordinated control and optimization of power usage throughout the data center.

Control costs

The visibility and control provided by sFlow can significantly reduce data center costs, both in terms of the capital cost of equipment and the operational costs of managing, powering and cooling the data center.

Poor visibility results in significant costs in two ways:

1. Over provisioning, because demand is poorly understood system architects act defensively, overestimating requirements and adding excessive safety margins as insurance. The excess capacity built into the system is expensive to purchase and maintain, however, since there is no visibility these costs are hidden.
2. Poor Service, because demand is poorly understood, operational changes occur in reaction to costly service failures, resulting in rushed, poorly targeted addition of capacity that further increase wasteful over provisioning.

The network wide visibility provided by sFlow fundamentally changes the equation. Detailed visibility into demand allows resources to be targeted where they are needed, minimizing over provisioning and avoiding service failures.

The diagram graphically demonstrates the difference between these two approaches. In the case of limited visibility, the increase in demand is not detected until it is too late. Over-reacting to the performance failure leads to excessive over-provisioning. With the visibility and control provided by sFlow, the increase in demand is detected early and additional capacity added. As demand for the service decreases, the additional resources are released so that they can be applied elsewhere.

The benefit of data center convergence and virtualization is that resources are pooled and can be allocated as needed. However, without network visibility it is impossible to fully realize the cost savings and improved performance that convergence promises.