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Ethernet Fabrics Offer Way Forward for Seismic Applications


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Exploring new frontiers, driving higher efficiencies, and shortening time-to-production. This is the lifeblood of the oil and gas industry. Businesses in this industry are constantly finding new and more efficient ways to interpret and model data and ascertain risk through cutting-edge technologies to maintain a competitive advantage.

With ever increasing simulation model sizes, locating, extracting, and producing energy has become an extremely data-intensive undertaking. Examples of this burgeoning data demand include massive parallel computation and simulation models from geophysicists; 3D visualization and interpretation of geographic data from reservoir simulation engineers, and mining historic seismic data of current reservoirs with up-to-date algorithms. IT managers are in a continuous race to stay ahead of these massive compute and data flow tasks.


Figure 1: Visualizing oil reservoir from Engineering Simulation and Scientific Software's Cyclops software


Deploying more servers, network storage, and workstations in the datacenter can temporarily relieve the demand for increased compute and storage capacity. However, new problems quickly arise as IT managers are confronted with the challenge of interconnecting massive amounts of devices that constantly produce and exchange huge data sets. Without a network that can maintain performance as compute and storage capacity grows, sustaining a competitive edge becomes more difficult as the time-to-result lengthens.

Traditional Datacenter Networks Become Bottlenecks

Ethernet technology has served the industry well over the past few decades. But given the growing demands on oil and gas datacenters -- particularly in terms of compute, storage, and bandwidth requirements -- traditional datacenter networks are becoming inhibitors in the race to stay ahead.


Figure 2: Traditional network diagram


These key inhibitors include:

  • Spanning Tree Protocol (STP)

    STP leads to an underutilized network infrastructure. It was designed to prevent looping traffic in a traditional multi-tier network. STP ensures all network forwarding paths are in a primary-backup model. The end result is a very high rate of inefficiency with up to 50 percent of the available network bandwidth sitting idle in a hot-standby mode.

Figure 3: Spanning tree network diagram

    New oil and gas industry computing models require hundreds, if not thousands, of compute servers with heavy server-to-server as well as server-to-storage traffic patterns. STP not only leads to a high level of inefficiency, but its heavyweight (e.g., slow) control protocol discourages network architects from designing large networks for fear of paralyzing the network when the STP protocol is invoked. Moreover, network architects are conditioned to design around the problem by segmenting their datacenter network in a collection of smaller "pods" or clusters that lead to inefficiencies in performance and higher network equipment costs.

  • Best Effort Delivery Mechanism Leads to Data Loss

    Ethernet relies on "best-effort" data transmission. Traditional network devices are assigned buffers to deal with temporary network bursts. Once the buffer is exhausted, the network switch simply drops further incoming packets and relies on higher level networking protocols to recover and retransmit. Ethernet's approach, while sufficient for normal day-to-day IT applications and traffic patterns, is grossly inadequate for large-scale computing models in the oil and gas industry. Compute servers and storage infrastructures drive increased model sizes and data sets at high speed. Geophysicists and network architects have found that these heavy traffic patterns result in constant data loss that requires additional computing and storage cycles just to recover from the network data loss.
  • Store-and-forward Switching Leads to Unpredictable Jitter

    Traditional Ethernet switching infrastructures are built on "store-and-forward" architectures. Incoming traffic is stored, analyzed, and forwarded to the destination. Each and every forwarding stage of the network repeats this forwarding process as data traffic propagates through the network. Due to the variable size of network data sets, a traditional Ethernet network always has performance variability or "jitter" (the variance in latency among all data flows). While simple and fast enough for traditional IT applications, such as PC desktop connectivity, jitter actually causes unpredictable performance degradation in the oil and gas datacenter.

Typical applications in oil and gas -- such as simulation, location assessment, risk management, visualization and others -- all require data input from a network storage source which feeds into a Message Passing Interface (MPI)-based parallel application environment. These MPI applications are designed to constantly process data sets with variable sizes in real-time, and in a parallel and pipe-lined computing environment. Due to the unpredictable nature of traditional Ethernet jitter, these parallel oil and gas applications are forced to spend significant idle cycles waiting for the completion of the slowest process before moving to the next stage of their pipe-lined computing process. The end result is longer time-to-result.

All in all, it is clear that the network interconnect is lengthening the oil and gas industry's time-to-result. A new architecture and a new delivery mechanism are needed. Both must be ubiquitous, standards-based, and low-cost while driving a higher level of performance. In this way, oil and gas datacenters can facilitate faster time-to-result.

Ethernet Fabrics Break the Bottleneck

While traditional datacenter networks are becoming inhibitors for oil and gas companies in their race to stay ahead, Ethernet fabrics present a new way for oil and gas companies to speed time-to-result and maintain a competitive edge.

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