Howard Marks

Network Computing Blogger


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Where the Cloud Touches Down: Simplifying Data Center Infrastructure Management

Thursday, July 25, 2013
10:00 AM PT/1:00 PM ET

In most data centers, DCIM rests on a shaky foundation of manual record keeping and scattered documentation. OpManager replaces data center documentation with a single repository for data, QRCodes for asset tracking, accurate 3D mapping of asset locations, and a configuration management database (CMDB). In this webcast, sponsored by ManageEngine, you will see how a real-world datacenter mapping stored in racktables gets imported into OpManager, which then provides a 3D visualization of where assets actually are. You'll also see how the QR Code generator helps you make the link between real assets and the monitoring world, and how the layered CMDB provides a single point of view for all your configuration data.

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Thursday, August 8, 2013
11:00 AM PT / 2:00 PM ET

This webinar will help attendees understand the overall concept of SDN and its benefits, describe the different conceptual approaches to SDN, and examine the various technologies, both proprietary and open source, that are emerging. It will also help users decide whether SDN makes sense in their environment, and outline the first steps IT can take for testing SDN technologies.

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Most Of Our Benchmarks Are Broken

For years, we in the storage industry have relied on a fairly small set of benchmarks to measure the relative performance of storage systems under different conditions. As storage systems have included new technologies-- including data reduction, flash memory as cache or automated tiering--our existing portfolio of synthetic benchmarks are starting to report results that aren't directly comparable to the performance that this new generation of storage systems will deliver in the real world.

The most commonly used storage benchmark is IOmeter, originally developed by Intel and, since 2001, an open source project on SourceForge. IOmeter can perform random and sequential I/O operations of various sizes, reporting number of IOPs, throughput and latency of the system under test. IOmeter has the virtues of being free and easy to use. As a result, we’ve developed IOmeter access patterns that mix various size I/O requests and random vs. sequential access patterns to mimic file, Web and database servers.

After years of hearing application vendors tell us that the impact of storage system cache should be minimal, we adjusted our test suite to measure actual disk performance, minimizing the impact of the storage system’s RAM cache. Since RAM caches, even today, are just a few gigabytes, simply running the benchmark across a data set, or volume, at least several times the size of the cache would ensure we weren’t seeing a fast cache as a fast storage system.

Once we start testing storage systems that use flash as a cache or automated storage tier, the system will no longer provide consistent performance across the test data set. Instead, when running real applications, some portions of the data, like indexes, will be "hot" and served from flash, where other portions of the data set, like transaction logs or sales order line item records, will be accessed only once or twice. These cooler data items will be served from disk.

The problem is that when IOmeter does random IO, its IO requests are spread evenly across the volume being tested. Unlike real applications, IOmeter doesn’t create hot spots. As a result, IOmeter results won’t show as significant a performance boost from the addition of flash as real-world applications.


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