Moving your data and applications to the cloud isn’t the easiest of tasks, if you want to do it right. There’s a multitude of decisions to make. Some you’ll get wrong, which might make you reconsider your cloud operating model or cloud provider. Which brings the next question: are you locked-in at your cloud provider? Can you move your data between clouds?
One start-up that attempts to make the move to the cloud and moving between clouds easier, is Elastifile. An Israeli company, founded in 2013 with its first version of the product out in Q4-2016, it created the Elastifile Cross-Cloud Data Fabric. Their objective: bring cloud-like efficiency to the on-premises cloud, and facilitate a easy lift-and-shift into the hybrid cloud.
Continue reading “Moving to and between clouds made simple with Elastifile Cloud File System”
Consistency and predictability matter. You expect Google to answer your search query within a second. If it takes two seconds, that is slow but ok. Much longer and you will probably hit refresh because ‘it’s broken and maybe that will fix it’.
There are many examples that could substitute the scenario above. Starting a Netflix movie, refreshing your Facebook timeline, or powering on an Azure VM. Or in your business: retrieving an MRI scan or patient data, compiling a 3D model, or listing all POs from last month.
Ensuring your service can meet this demand of predictability and consistency requires a multifaceted approach, both in hardware and procedures. You can have a modern hypervisor environment with fast hardware, but if you allow a substantially lower spec system in the cluster, performance will not be consistent. What happens when a virtual machine moves to the lower spec system and suddenly takes longer to finish a query?
Similarly, in storage, tiering across different disk types helps improve TCO. However, what happens when data trickles down to the slowest tier? Achieving that lower TCO comes with the tradeoff of less latency predictability.
These challenges are not new. If they impact user experience too much, you can usually work around them. For example, ensure your data is moved to a faster tier in time. If you have a lot of budget, maybe forgo the slowest & cheapest NL-SAS tier and stick to SAS & SSD. But what if the source of the latency inconsistency is something internal to a component, like a drive?
Continue reading “SNIA: Avoiding tail latency by failing IO operations on purpose”
Once upon a time there was a data center filled with racks of physical servers. Thanks to hypervisors such as VMware ESX it was possible to virtualize these systems and run them as virtual machines, using less hardware. This had a lot of advantages in terms of compute efficiency, ease of management and deployment/DR agility.
To enable many of the hypervisor features such as VMotion, HA and DRS, the data of the virtual machine had to be located on a shared storage system. This had an extra benefit: it’s easier to hand out pieces of a big pool of shared storage, than to predict capacity requirements for 100’s of individual servers. Some servers might need a lot of capacity (file servers), some might need just enough for an OS and maybe a web server application. This meant that the move to centralized storage was also beneficial from a capacity allocation perspective.
Continue reading “Intel SPDK and NVMe-oF will accelerate NVMe adoption rates”
I’m excited to announce I’ll be attending Storage Field Day 12! During the event we’ll talk storage technology for three days, starting on March 8th. There’s an impressive line-up of companies and delegates gathering in Silicon Valley and of course we’ll live stream the presentations for the folks back home, who can pitch in over Twitter. Did I mention the line-up of companies already? Oh boy!
Continue reading “Storage Field Day 12: storage drop bears reunited!”