Before deploying any flash memory system, IT architects need a means to proactively identify if functionality ceilings will be broken and the best way to evaluate the technology selections for best meeting program workload requirements of their very own networked storage.
Flash storage is one of the most promising new technology to affect data centres in years. Very similar to virtualization, flash storage will likely be deployed in virtually every data centre during the next decade–its performance, footprint, power and reliability advantages are just too compelling.
However, every data center must be uniquely architected to fulfill the specific software, user access and response time requirements and no one storage vendor can create a single product that’s ideal for each program workload.
Although storage systems that incorporate flash guarantee to ease all storage performance issues, determining which applications justify the need for flash and just how much flash to set up are fundamental questions.
If flash is not provisioned properly and tested against the actual applications that run in your infrastructure, the cost of your flash storage may price 3X-10X the price per GB of conventional rotation media (HDDs).
Before deploying any flash memory strategy, IT architects need a way to proactively identify if functionality ceilings will be breached and the best way to assess the technology selections for best fulfilling application workload needs of the very own networked storage.
Workload Analytics is a procedure whereby intelligence is gathered concerning the distinctive characteristics of application workloads in a specific environment. By recording all of the attributes of real time generation workloads, exceptionally precise workload models could be created which enable storage and application infrastructure managers to stress test storage product o?erings utilizing THEIR speci?c workloads.
The first concept is to extract statistics on production workloads in the storage environment to establish that an I/O baseline and simulate I/O growth tendencies.
Truly understanding the performance characteristics of flash memory is extremely different than traditional tough drive-based storage. Flash sellers claim they’re very fast–some vendors are claiming more than a million IOPS, however the configurations and assumptions to attain such results fluctuate greatly and can be quite misleading. Regrettably, enabling such features can have a dramatic impact on performance. This makes workload modeling of such traffic much more complicated.
Workload models must accurately capture these features and have the ability to mimic data compression and inline deduplication. Accurate workload modeling for flash will need to emulate your workload, control the applicability of the content, command the compressibility of the data content, and generate countless IOPS.
Deciding when and if flash or hybrid storage methods are perfect for your data center is a complex task these days. Determined by vendor-provided benchmarks will usually be irrelevant since they can’t determine how flash memory will reap your precise applications.
Workload modeling, together with load generating appliances, is your very cost-effective means to produce intelligent flash storage decisions and also to align installation decisions with your specific performance requirements.
There is a brand new breed of storage performance validation tools readily available on the market today. Tools like Load DynamiX allow you to create realistic workload pro?les of production application environments and create workload analytics that offer insight into how workloads interact with the infrastructure.
To help you examine that’s the right mixture of SSD and HDD for the environment, these innovative new storage validation tools can help you create configuration/investment scenarios–now and in to the future.