The “digital universe” seems to be mirroring our own physical universe: it just keeps expanding at faster and faster rates. Consider this jaw-dropping statistic from market researchers at IDC: the total volume of digital information that is created and replicated globally reached 281 BILLION gigabytes of digital information in 2007. That’s 45 gigabytes for every person on Earth.

A big part of this growth results from the rise in consumer-generated content. While the initial stages of the Web included a relatively small number of sites with relatively fixed content, we now have thousands of choices. And many Web destinations are driven by their ability to aggregate an enormous number of consumer-generated files such as photos and videos.
But there is more to the story. Even though the amount of data being created and stored continues to rise at a significant clip, the number of files, or file count, is rising at a much faster rate. Recent data from IDC details how the compound annual growth rate for terabytes of capacity is 59 percent while the compound growth rate for file count is 88 percent during the same period. This results in new challenges and demands for Web-scale infrastructure.

No longer is finding enough capacity sufficient to scale Web-scale infrastructure for hundreds of thousands to millions of users. The thornier problem is now managing the “file count explosion” resulting from soaring numbers of individually-contributed user-generated content items and optimizing the performance of increasingly-complex Web-scale applications across Web servers such as Apache and Lighttpd, indexing engines such as Lucene, and databases such as MySQL.

On the file count side of the ledger, it’s a classic “needle in the haystack” problem. As more and more files are generated, it takes longer and longer to handle user queries for specific files. Why? The indexes used to find files are growing bigger as well. Keep in mind that in most of these applications, even though the content requested varies widely across each individual user, the ways to reach that content such as searching by name, subject, or “recency” requires the use of a common index for all users. As a result, it takes more time to comb through these enormous indexes to find the files a user wants. And this creates serious performance bottlenecks when the historical practice of keeping that indexing information on mechanical disks starts to bog down.

The root of this index overload is metadata, or the information about files, rather than the file content itself. Specifically, when file systems need to search through lots of files and numerous directory levels, the overhead of those metadata operations can overwhelm the storage system. The impact of this can be seen in a typical file or object retrieval operation where the common request to “walk the directory tree” leads to an excessive number of additional NFS operations instead of simply being able to identify the file location and getting it.

Solving this bottleneck requires a reevaluation of how to deploy memory in the datacenter. Forward-looking datacenter architects and managers are now recognizing the advantages of scalable, network-based caching as a means of solving this growing “file explosion” problem. By making a scalable pool of memory available in the form of a centralized network caching service, datacenters can deliver accelerated file services that offload slower mechanical disk drives, storage subsystems and existing file systems from the heavy lifting of handling this mushrooming metadata and index growth. Some advantages of network caching include:

1. Peak performance

Deploying memory as a centralized cache enhances disk drives performance and enables immediate I/O performance gains of 10-50X, significantly improving application response times.

2. Consolidated and centralized resources

Implementing a consolidated, shared network caching resource dramatically improves overall system efficiency, reduces overall costs, and intelligently rebalances workloads in response to existing application infrastructure.

3. Single-point management

A centralized model of single-point management allows for true expansion without additional management overhead. Adding resources to the caching appliances does not require extra administrative tasks as a single appliance can scale under one management interface.

4. Use of existing infrastructure

Centralized caching solutions enhance existing infrastructure by supporting current storage systems and Web/application servers.

5. Multi-use deployment

As a shared resource, scalable caching appliances can optimize the performance of multiple applications within a single appliance, greatly increasing the overall efficiency and effectiveness in the datacenter.

To address Web-scale application performance issues, new accelerated file and object services are emerging to remedy the scaling challenges. These new services make applications available to more concurrent users with greatly improved file access performance to meet the compute and storage intensive demands of Web-scale datacenters.

One example of these services includes the ability to handle and cache metadata selectively. The conventional model of storing all information on disk-based systems can lead to lengthy response times, limiting the number of application transactions that can take place within a given time frame. Metadata operations such as looking up file names and locations can involve numerous small, random requests that — when multiplied across thousands of simultaneous users — often overwhelm a typical disk storage system. When the file count grows, and the number of nested directories increases, it is not impossible to have ratios up to 20:1 of metadata commands to file retrieval commands. This overhead limits the scale of the overall infrastructure.

By keeping frequently requested data in memory as opposed to on mechanical disks, system I/O performance increases dramatically, enabling improved application response. The primary benefit of the centralized caching deployment is the ability to optimize requests across hundreds or thousands of Web servers. This in turn enables Web applications to easily scale the number of concurrent users without suffering from excessive delays or the need to grossly overprovision infrastructure.

Conclusions

Application workloads are changing from individual users accessing desktop applications to hundreds or thousands of concurrent users accessing common Web applications. Whether for social networking, file sharing, or commercial applications, the use of consolidated Web-scale infrastructure is on the rise, demanding new thinking for datacenter systems.

Fortunately, not everything has to be re-architected. As the demands for rapid file access increase, placing heavier and heavier loads on original Web infrastructure, Web-scale applications can make use of centralized caching resources to scale appropriately with existing infrastructure. This ensures that rapid response times are met for an optimized end user experience.

About the Author

Gary Orenstein is vice president of marketing and business development for Gear6 and has been active in the IP storage networking industry since its inception. He was an initial governing board member of the Storage Networking Industry Association IP Storage Forum where he helped develop, promote, and deliver educational information furthering market growth.

Prior to Gear6, Gary served as vice president of marketing at Compellent Technologies, a network storage company delivering affordable, modular SANs. Before Compellent, he was a founding team member at Nishan Systems, a leader in the IP Storage market, and spearheaded many of Nishan’s milestone industry events such as The Promontory Project – the first transcontinental IP storage network, and the first wire-speed iSCSI demonstration. Prior to that, he spent several years building international distribution and joint ventures for US companies, including a distributorship for Sun Microsystems in Asia.

Gary is also the author of IP Storage Networking: Straight to the Core — a book that outlines the business value of enterprise storage technology. He holds an MBA from the Wharton School at the University of Pennsylvania, and a BA from Dartmouth College.