QLogic 10000 Series Mt. Rainier Technology Accelerates the Enterprise User Manual


White Paper

QLogic Mt. Rainier Technology

Accelerates the Enterprise

Shared, Scalable, and Transparent Caching for Network Storage

Key Findings

A large and growing gap exists between the information access performance demands of critical applications running on highperformance servers, and the performance capacity of subsystems that are based on traditional, mechanical storage. QLogic® findings indicate that:

•• Flash-based caching offers the promise of substantial application performance improvements.

•• The placement of Flash-based caches within the storage network profoundly impacts whether, and to what degree, those promises can be realized. The current market offerings force architects to make difficult trade-offs.

•• The QLogic Mt. Rainier technology delivers a unique, shared, server-based caching solution that delivers scalability and performance, guarantees cache coherence, and improves the economics of enterprise-wide caching adoption.

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Executive Summary

With Mt. Rainier technology, QLogic delivers a set of unique solutions optimized to address the growing performance gap between what the processor can compute and what the storage I/O subsystem can deliver. With a very simple deployment model, this approach seamlessly combines enterprise server I/O connectivity with shared, server-based I/O caching. Mt. Rainier delivers dramatic and— perhaps

most importantly—smoothly scalable application performance improvements to the widest range of enterprise applications. In combination with the QLogic Cache Optimization Protocol™ (QCOP), these performance benefits are transparently extended to today’s most demanding active-active clustered environments. This white paper provides a high-level introduction to the QLogic Mt. Rainier technology.


Increased server performance, higher virtual machine density, advances in network bandwidth, and more demanding business application workloads create a critical I/O performance imbalance between servers, networks, and storage subsystems. Storage I/O is the primary performance bottleneck for most virtualized and data-intensive applications. While processor and memory performance have grown in step with Moore’s Law (getting faster and smaller), storage performance has lagged far behind, as shown in Figure 1. This performance gap is further widened by the rapid growth in data volumes that most organizations are experiencing today. For example, IDC predicts that the amount of data volume in the digital universe will increase by a factor of 44 over the next 10 years.

Figure 1. Growing Disparity Between CPU and Disk-Based Storage Performance

Following industry best practices, storage has been consolidated, centralized, and located on storage networks (for example, Fibre Channel, Fibre Channel over Ethernet [FCoE], and iSCSI) to enhance efficiency, compliance policies, and data protection. However, network storage design introduces many new points where latency can be introduced. Latency increases response times, reduces application access to information, and decreases overall performance. Simply put, any port in a network that is over-subscribed can become a point of congestion, as shown in Figure 2.

Figure 2. Sources of Latency on Storage Networks

As application workloads and virtual machine densities increase, so does the pressure on these potential hotspots and the time required to access critical application information. Slower storage response times result in lower application performance, lost productivity, more frequent service disruptions, reduced customer satisfaction, and, ultimately, a loss of competitive advantage.

Over the past decade, IT organizations and suppliers have employed several approaches to address congested storage networks and avoid the risks and costly consequences of reduced access to information and the resulting underperforming applications.

The Traditional Approach: Refresh Storage Infrastructure

The traditional approach to meeting increased demands on enterprise storage infrastructure is to periodically replace or “refresh” the storage arrays with newer products. These infrastructure upgrades tend to focus on higher-performance storage array controllers and disk drives that spread data wider across a larger number of storage channels, increase the number of array frontand back-end storage ports available, and increase network bandwidth. Implementing a well-designed infrastructure refresh delivers


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improved system performance, but also introduces significant costs and risks. For example, installing new arrays requires migrating existing data to those new arrays, and this migration generally requires a minimum of one or two outages per attached server. Furthermore, as the sheer volume of the data involved in these migrations grows, migration jobs take longer, and cost, complexity, and risk all increase. With the continuing expectation of geometrically increasing performance demands, the improvements delivered by these “big bang” infrastructure upgrades are temporary, by their nature. The dynamic growth of application workloads at the edge of the comparatively static storage networks and arrays eventually outstrips any feasible configuration at the core of those networks. This inherent guarantee of obsolescence results in excessive spending to optimize storage performance at the expense of efficient capacity and it drives infrastructure refresh cycles to typically occur every three to five years.

A New Option: Deploy Flash Memory

In the last few years, Flash memory has emerged as a valuable tool for increasing storage performance. Flash memory outperforms rotating magnetic media by orders of magnitude when processing random I/O workloads. As a new and rapidly expanding semiconductor technology, QLogic expects Flash memory, unlike mechanical and disk drives, to track a Moore’s Law-style curve for performance and capacity advances.

To accelerate early adoption, Flash memory has been primarily packaged as solid-state disk (SSD) drives that simplify and accelerate adoption. Although originally packaged to be plug-compatible with traditional, rotating, magnetic media disk drives, SSDs are now available in additional form factors, most notably server-based PCI Express® boards.

SSD Caching Versus Tier 0 Data Storage

The defining characteristic of SSDs is that—independent of physical form factor—they are accessed as if they are traditional disk drives. The compatible behavior of SSDs enabled their rapid adoption as an alternative to 10K to 15K RPM disk drives. In high-end, mission-critical applications, the much higher performance of SSDs—coupled with much lower power and cooling requirements—largely offset their initially high prices. As SSD prices have decreased and capacities have increased, SSDs deployed as primary storage have seen accelerated adoption for a relatively small set of performance-critical applications.

Array-Based SSD Caching

Initial deployments of SSD caching were delivered by installing SSDs, along with the required software and firmware functionality, within shared storage arrays. Due to the plug-compatibility of early SSDs, these initial implementations did not require extensive modifications to existing array hardware or software and, in many cases, were available as upgrades to existing equipment.


Applying SSD caching to improve performance inside storage arrays offers several advantages that closely parallel the fundamental advantages of centralized network-attached storage arrays. Advantages include: efficient sharing of valuable resources, maintenance of existing data protection regimes, and providing a single point of change while maintaining existing network topology.


Adding SSD caching to storage arrays requires upgrading and, in some cases, replacing existing arrays (including data migration effort and risk). Even if all of the disk drives are upgraded to SSDs, the expected performance benefit is not fully realizable due to contention-induced latency at over-subscribed network and array ports (see Figure 2). The performance benefits of SSD caching in storage arrays may be shortlived and performance may not scale smoothly. The initial per-server performance improvements will decrease over time as the overall demands on the arrays and storage networks increase with growing workloads, and with server and virtual server attach rates.

Caching Appliances

Caching appliances—relatively new additions to storage networking— are network-attached devices that are inserted into the data path between servers and primary storage arrays.


Like array-based caching, caching appliances efficiently share relatively expensive and limited resources, but do not require upgrades to existing arrays. Because these devices are independent of the primary storage arrays, they can be distributed to multiple locations within a storage network to optimize performance for specific servers or classes of servers.


Also in common with arrays, caching appliances are vulnerable to congestion in the storage network and at busy appliance ports. The appliance approach offers better scalability than array-based caching because it is conceptually simple to add incremental appliances into a storage network. However, each additional appliance represents a large capital outlay, network topology changes, and outages.

In contrast to array-based caching, caching appliances are new elements in the enterprise IT environment and require changes to policies, procedures, run books, and staff training.

Lastly, bus and memory bandwidth limitations of the industry-standard components at the heart of caching appliances restrict their ability to smoothly scale performance. Because these appliances sit in-band on


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