Getting Started with the Cloud Data Management Interface (CDMI) Conformance Test

For a long time, the architecture and best practices of storage networks have been relatively well-understood. Recently, however, advanced capabilities have been added to storage that could have broader impacts on networks than we think.

The three main storage network transports - Fibre Channel, Ethernet, and InfiniBand – all have mechanisms to handle increased traffic, but they are not all affected or implemented the same way. For instance, placing a protocol such as NVMe over Fabrics can mean very different things when looking at one networking method in comparison to another.

Unfortunately, many network administrators may not understand how different storage solutions place burdens upon their networks. As more storage traffic traverses the network, customers face the risk of congestion leading to higher-than-expected latencies and lower-than expected throughput. Watch this webinar to learn:

•Typical storage traffic patterns
•What is Incast, what is head of line blocking, what is congestion, what is a slow drain, and when do these become problems on a network?
•How Ethernet, Fibre Channel, InfiniBand handle these effects
•The proper role of buffers in handling storage network traffic
•Potential new ways to handle increasing storage traffic loads on the network

Traditionally, much of the IT infrastructure that we’ve built over the years can be divided fairly simply into storage (the place we save our persistent data), network (how we get access to the storage and get at our data) and compute (memory and CPU that crunches on the data). In fact, so successful has this model been that a trip to any cloud services provider allows you to order (and be billed for) exactly these three components.

We build effective systems in a cost-optimal way by using appropriate quantities of expensive and fast memory (DRAM for instance) to cache our cheaper and slower storage. But currently fast memory has no persistence at all; it’s only storage that provides the application the guarantee that storing, modifying or deleting data does exactly that.

Memory and storage differ in other ways. For example, we load from memory to registers on the CPU, perform operations there, and then store the results back to memory by using byte addresses. This load/store technology is different from storage, where we tend to move data back and fore between memory and storage in large blocks, by using an API (application programming interface).

New memory technologies are challenging these assumptions. They look like storage in that they’re persistent, if a lot faster than traditional disks or even Flash based SSDs, but we address them in bytes, as we do memory like DRAM, if more slowly. Persistent memory (PM) lies between storage and memory in latency, bandwidth and cost, while providing memory semantics and storage persistence. In this webcast, SNIA experts will discuss:

•Traditional uses of storage and memory as a cache
•How can we build and use systems based on PM?
•What would a system with storage, persistent memory and DRAM look like?
•Do we need a new programming model to take advantage of PM?
•Interesting use cases for systems equipped with PM
•How we might take better advantage of this new technology

Kubernetes (k8s) is an open-source system for automating the deployment, scaling, and management of containerized applications. Kubernetes promises simplified management of cloud workloads at scale, whether on-premises, hybrid, or in a public cloud infrastructure, allowing effortless movement of workloads from cloud to cloud. By some reckonings, it is being deployed at a rate several times faster than virtualization.

In this presentation, we’ll introduce Kubernetes and present use cases that make clear where and why you would want to use it in your IT environment. We’ll also focus on the enterprise requirements of orchestration and containerization, and specifically on the storage aspects and best practices.

•What is Kubernetes? Why would you want to use it?
•How does Kubernetes help in a multi-cloud/private cloud environment?
•How does Kubernetes orchestrate & manage storage? Can Kubernetes use Docker?
•How do we provide persistence and data protection?
•Example use cases