Controller Technology: How It Works
As data storage requirements escalate and IT departments begin to reach the limits of virtualization’s ability to
control the necessary hardware, they must examine new approaches to storing and managing data. Controller
technology offers a revolutionary, automated solution to data storage, but not all controller technology is
created equal.
A storage controller manages the flow of information between the server and the data, assigning two paths, in
case one of the paths fails or is overloaded. “Active” and “passive” refer to the relative ability of a controller to
assume an application workload — whether it sits idle until it is called upon to balance a load, or it is actively
available at all times. An asymmetric controller architecture requires the administrator to define a preferred path
and offers faster performance for an I/O following the preferred path, while a symmetric controller does not
require preferred path definitions and offers the same level of performance regardless of the path.
Despite many advances in storage technologies, no host or network balancing algorithm is perfect. For
example, the Microsoft
®
Windows implementation Multi Path IO (MPIO) is based on a round-robin algorithm.
Round-robin is a scheduling algorithm that assigns time slices to each process in equal portions and in order —
handling all processes without priority. However, the round-robin approach does not balance server
performance, and if scheduled jobs vary in size the round-robin algorithm favors large jobs over others.
For the best levels of performance and availability, every layer of technology must be balanced: at the host,
network and storage levels. Host-level balancing generally requires installing and updating host software as
well as determining configuration and compatibility dependencies based on applications, OS type, cluster
configuration and virtualization requirements. Similarly, a balanced network eliminates bottlenecks and reroutes
I/Os in order to keep data moving efficiently.
Imbalances at the host and network levels must be absorbed and mitigated at the storage system level. As a
result of these many dependencies and complexities, it is far from easy to develop a well balanced, highly
available, high-performance system. For example, if a network has one large block of data that is requested as
an I/O request, it will be limited to the bandwidth of a single link, creating a load imbalance. Even if link
aggregation is used on both the server and the storage system, any traffic flow between a single server and the
storage system could not exceed the bandwidth of a single link in the aggregate. Because none of these
balancing technologies are perfect, administrators must rely on the solid foundation of the SAN to compensate.
These limitations drive up the administration cost or at least make it impossible to get efficient use of a SAN
without SAN administrator(s).
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