Methods and systems for converged networking and storage

Abstract

A device includes a converged input / output controller that includes a physical target storage media controller, a physical network interface controller and a gateway between the storage media controller and the network interface controller, wherein gateway provides a direct connection for storage traffic and network traffic between the storage media controller and the network interface controller.

Description

CROSS REFERENCE TO RELATED APPLICATIONS[0001]This application claims priority to the following provisional applications, each of which is incorporated herein by reference in its entirety: U.S. patent application 61 / 950,036, filed Mar. 8, 2014 and entitled “Method and Apparatus for Application Driven Storage Access”; and U.S. patent application 62 / 017,257, filed Jun. 26, 2014 and entitled “Apparatus for Virtualized Cluster IO”.FIELD OF THE INVENTION[0002]This application relates to the fields of networking and data storage, and more particularly to the field of converged networking and data storage devices.BACKGROUND OF THE INVENTION[0003]The proliferation of scale-out applications has led to very significant challenges for enterprises that use such applications. Enterprises typically choose between solutions like virtual machines (involving software components like hypervisors and premium hardware components) and so-called “bare metal” solutions (typically involving use of an operat...

AI Technical Summary

Benefits of technology

The converged solution described in this patent text will improve performance, increase reliability, and provide a multi-tenant environment for computer resources. It uses NVMe over Ethernet to tunnel the traffic of a high-speed memory protocol, which allows for faster processing and better performance. This solution also allows for more scalability and efficient use of hardware, resulting in a smaller data center footprint and fewer systems to manage. Overall, the patent text explains how the use of NVMe over Ethernet can help create a better, more efficient, and scalable computing and storage environment.

Problems solved by technology

The proliferation of scale-out applications has led to very significant challenges for enterprises that use such applications.

At large scale, virtual machine solutions typically have poor input-output (IO) performance, inadequate memory, inconsistent performance, and high infrastructure cost.

Bare metal solutions typically have static resource allocation (making changes in resources difficult and resulting in inefficient use of the hardware), challenges in planning capacity, inconsistent performance, and operational complexity.

In both cases, inconsistent performance characterizes the existing solutions.

Referring to FIG. 2, one major problem with hypervisors is with the complexity of IO operations.

Such activities involve very large numbers of IO operations, which result in poor performance in hypervisor solutions.

Users of those applications also frequently re-shard (change the shards on which data is deployed), resulting in big problems for bare metal solutions that have static storage resource allocations, as migration of data from one location to another also involves many copying and transport operations, with large numbers of IO operations.

As the amount of data used in scaleout applications grows rapidly, and the connectedness among disparate systems increases (such as in cloud deployments involving many machines), these problems grow exponentially.

This not only degrades performance (creating latency and throughput issues) of the operation, but also adversely affects other applications that run on the CPU.

In environments where a large amount of traffic is moving, congestion can occur in the network 122, causing degradation in performance as well as problems with availability (e.g., dropped packets, lost connections, and unpredictable latencies).

However, these are reactive methods; that is, they detect formation of congestion points and push back on the source to reduce congestion, potentially resulting in delays and performance impacts.

As described, limitations of this approach include degradation in raw performance, unpredictable performance, impact on other tenants or operations, availability and reliability, and inefficient use of resources.

There is no programmability in a typical legacy SAN architecture.

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