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Predictable Performance & Granular Control By @KellyMurphyGS | @CloudExpo [#Cloud]

In a highly virtualized environment, delivering predictable performance to VMs from a shared elastic resource is critical

Design Goal 2: Predictable Performance and Granular Control per VM

Traditional storage was designed to present a LUN to a server. This worked well until that server became a hypervisor running 10, 20 or 30 virtual servers, all pointing to the same LUN. Adding to the complexity, all hypervisors need to point to the same LUN to enable VM mobility or high availability. Architecturally, this creates a many-to-one relationship that makes optimization impossible. Each VM has a different I/O pattern. When they all mix together, the aggregate I/O becomes highly random, creating performance issues with no way to isolate problems or control I/O per VM.

Isolate and Optimize I/O per VM
The core component of the Gridstore architecture is the vController that operates in the host kernel and appears as a local SCSI device. From this privileged position, it isolates I/O from each of the VMs into independent swim lanes or channels from the hypervisor across the network and onto storage nodes in the Grid. This re-creates the1:1 relationship between VM and storage. Once I/O is isolated, each channel can be independently optimized according to what the VM is trying to achieve. Optimizations occur dynamically optimizing the I/O pattern to the demands of the VM.

System Center
All-Flash Hyper-Converged Infrastructure
Predictable Performance per VM
With the I/O isolated into channels per VM, the vController can now overlay QoS policies or set specific IOPS minimum/maximum per VM to give predictable performance per VM. Upon provisioning VMs through Virtual Machine Manager (VMM) and classifying them as a platinum service, Gridstore will deliver a platinum storage service. Furthermore, administrators can dial in a specific amount of IOPS to a given VM to ensure that it allocates a minimum amount of I/O to enable it to run optimally. Equally, lower-grade classes can be created or limits on IOPS can be dialed in to ensure that no VM consumes more than it requires. This is beneficial for service providers who want to offer different levels of service to customers, since it ensures they can deliver what they promise, resulting in predictable environments that run as desired for all workloads.

End-to-End QoS Architecture
Due to the unpredictable performance in virtual environments, QoS has become a hot feature. However not all QoS is the same. Three models for QoS are:

  1. Hypervisor based
  2. Array based
  3. End-to-end

A. Hypervisor- (Host-) Based QoS
Hypervisors create QoS in the hypervisor by throttling I/O. This is effective to ensure that one or more VMs do not consume all the IOPS, the noisy neighbor problem. This model, however, only operates in a single hypervisor host. It does not provide a global view (i.e., storage resources being consumed by other hosts, like a database on a physical server). Furthermore, a hypervisor-based model is not in control of the actual storage resource, making it impossible to allocate more resources to a particular VM or physical host to meet a minimum IOPS reserve.

B. Array-Based QoS
This is the opposite of the hypervisor model. In this configuration a storage array presents multiple LUNs to the hypervisor. Each LUN has a QoS priority attached to it, and I/O is more or less "allocated" to these LUNs based on the prioritization. Problems with this model include:

  • Being LUN based, it is not granular to the VM level
  • The I/O blender is still blending, which means I/O that passes through a LUN is blended together from multiple VMs, resulting in no per-VM granularity. With this, hundreds of VMs on that LUN compete for resources. When a VM on the highest priority LUN runs wild and consumes all resources, every other VM is impacted. According to the QoS policy, it did its job. It served the highest-priority LUN and starved the other LUNs. However, it does not know that all this I/O is coming from a single VM at the expense of others. It does not know that they are on the same highest-priority LUN
  • An array-based QoS model does not have any ability to control the flow from the hypervisor end. Rather, its domain of control is within the array.

C. End-to-End Per-VM I/O Control
In a highly virtualized environment, delivering predictable performance to VMs from a shared elastic resource is critical. Gridstore's vController technology isolates I/O from each VM at the source and enables end-to- end per-VM I/O control through the entire storage stack. By controlling both the resource consumption (VM hosts) and the resource allocation (storage resources from Grid nodes), Gridstore can precisely monitor demand as well as control the allocation of resources across the hypervisor hosts and storage resources. End-to-end VM I/O control enables the ability to dial in specific I/O per VM or class of VMs, thus ensuring SLAs are met. The result: complete control of resource allocation and utilization.

The post Design Goal 2: Predictable Performance and Granular Control per VM appeared first on Gridstore.

More Stories By Kelly Murphy

As a serial entrepreneur with a track record of bringing disruptive technologies to market, Kelly Murphy brings 15 years CEO experience with disruptive venture backed software companies. In 1998, almost a decade before the cloud became popular, Murphy founded Marrakech, the first software company that offered on-demand procurement and supply chain systems to over 30,000 trading partners including some of the world’s largest retailers, consumer food producers, packaging companies and utilities. After selling Marrakech in 2007, he turned his sights onto what was his largest obstacle in growing his previous business — storage.

In 2009, Murphy founded Gridstore — a pioneer of software-defined storage that is set to disrupt the traditional storage industry. Currently, he serves on Gridstore’s Board of Directors and is also the Chief Technology Officer. Originally from Canada, Murphy obtained his BS in Computer Science from Michigan Technological University, played Division I hockey and was the seventh pick of the New York Islanders in the 1984 entry draft.

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