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Application Performance and WAN Speedup

Terry Critchley

Much emphasis is placed on servers and storage when discussing Application Performance, mainly because the application lives on a server and uses storage. However, the network has considerable importance, certainly in the case of WANs where there are ways of speeding up the transmission of data of a network. These techniques usually go under the title WAN Acceleration or WAN Optimization.

It is often thought that the limiting factor in speed of network transmission is the bandwidth of the medium involved but this is not strictly true. There are techniques for getting better performance from WANs, often by biasing data transmission priorities towards more important business processes. Having designed the WAN network and its components, is that it then? Job done?

Not really, since there are tricks you can employ to enhance performance but that is a bonus not a panacea for poor design. There are ways to get apparent extra bandwidth without a disruptive upgrade.

The normal definition is the optimization of available bandwidth in the areas:

■ Don't send what you don't need to send, especially large files or print loads

■ If it must be sent, try to schedule it appropriately so as not to interfere with critical workloads. Use business prioritization as the decision yardstick

■ Use techniques to optimize use of the available bandwidth (discussed below).

1. Caching

This is the storage of data transmitted from a source to a destination at that destination. If the same data is requested at the destination, the optimization software recognizes this and stops any request to the original source for a retransmission.

2. Deduplication

Data deduplication is the replacement of multiple copies or blocks of data (at various levels of granularity) with references to a shared copy in order to save storage space and/or bandwidth (SNIA Definition). Data deduplication can operate at the file, block or bit level.

3. Compression

This is fairly obvious and the data transmission is reduced by an amount dictated by the efficiency of the data compression/decompression algorithms used. The efficiency of a compression technique is measured by the ratio original size of data to the compressed size.

4. FEC (Forward Error Correction)

A “receiver makes it right” transmission technique where extra bits are added to a packet/message for analysis at the receiving end. In general, it means that the receiving end of the transmission is able to detect, and in most cases correct, any erroneous transmissions.

Packets warranting retransmission may:

■ be corrupted due to errors, for example noise

■ lost in link or host failures

■ dropped due to buffer overflow

■ dropped due to aging or sell by date exceeded, for example the TTL (Time To Live) field in IP (Internet Protocol)

5. Traffic shaping

Traffic shaping is the practice of regulating network data transfer to assure a certain level of performance, quality of service (QoS). The practice involves favoring transmission of data from higher priority applications over lesser ones, as designated by the business organization. It is sometimes called packet shaping.

6. Congestion Control

This TCP function is designed to stop the sender shipping more data than the network can handle, as if trying to drink from a fire hose. TCP uses a number of mechanisms based on a parameter called the congestion window.

7. Protocol acceleration

A class of techniques for improving application performance by avoiding or circumventing shortcomings of various protocols. There are several forms of protocol acceleration:

■ TCP Acceleration

■ CIFS (Common Internet File Systems) and NFS (Network File System) Acceleration

■ HTTP (Hypertext Transfer Protocol) Acceleration

■ Microsoft Exchange Acceleration

See reference 7 below, under "WAN Optimization References" which presents good coverage of some of the factors listed above.

8. Transmission protocol

Choose your transmission protocol according to what you are transmitting. Some protocols are better than other at transmitting certain types of data. This will also dictate the expected loss (and hence retransmission) rate

9. Take advice

Take advice from outside if you aren't sure what you are doing.

10. Tricks of the trade

Some of the tricks of the optimization trade are:


Dr. Terry Critchley is the Author of "Making It in IT", "High Performance IT Services" and “High Availability IT Services”.

This blog was created from extracts from Terry Critchley's book: High Performance IT Services [ August 25 2016]


WAN Optimization References

There are a number of useful references on this topic. I found all the following ones useful in various areas as well as being quite easy to follow. I have therefore decided to list them all and let the network experts among you choose your own favorite.

1. WAN Optimization Part 1: TCP Limitations

2. WAN Optimization Part 2: Put Performance Second

3. WAN Optimization Part 3: Overcoming Bandwidth Limitations

4. The 2014 Application & Service Delivery Handbook. Part1: Introduction and Challenges
[Search on “Application and Service Delivery Handbook” to find versions from 2011, 2012 and 2013. Add the search term “webtorials” if the hit list is too large.]

5. The Definitive Guide to Cloud Acceleration

6. An Introduction to IP Header Compression

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Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

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AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Application Performance and WAN Speedup

Terry Critchley

Much emphasis is placed on servers and storage when discussing Application Performance, mainly because the application lives on a server and uses storage. However, the network has considerable importance, certainly in the case of WANs where there are ways of speeding up the transmission of data of a network. These techniques usually go under the title WAN Acceleration or WAN Optimization.

It is often thought that the limiting factor in speed of network transmission is the bandwidth of the medium involved but this is not strictly true. There are techniques for getting better performance from WANs, often by biasing data transmission priorities towards more important business processes. Having designed the WAN network and its components, is that it then? Job done?

Not really, since there are tricks you can employ to enhance performance but that is a bonus not a panacea for poor design. There are ways to get apparent extra bandwidth without a disruptive upgrade.

The normal definition is the optimization of available bandwidth in the areas:

■ Don't send what you don't need to send, especially large files or print loads

■ If it must be sent, try to schedule it appropriately so as not to interfere with critical workloads. Use business prioritization as the decision yardstick

■ Use techniques to optimize use of the available bandwidth (discussed below).

1. Caching

This is the storage of data transmitted from a source to a destination at that destination. If the same data is requested at the destination, the optimization software recognizes this and stops any request to the original source for a retransmission.

2. Deduplication

Data deduplication is the replacement of multiple copies or blocks of data (at various levels of granularity) with references to a shared copy in order to save storage space and/or bandwidth (SNIA Definition). Data deduplication can operate at the file, block or bit level.

3. Compression

This is fairly obvious and the data transmission is reduced by an amount dictated by the efficiency of the data compression/decompression algorithms used. The efficiency of a compression technique is measured by the ratio original size of data to the compressed size.

4. FEC (Forward Error Correction)

A “receiver makes it right” transmission technique where extra bits are added to a packet/message for analysis at the receiving end. In general, it means that the receiving end of the transmission is able to detect, and in most cases correct, any erroneous transmissions.

Packets warranting retransmission may:

■ be corrupted due to errors, for example noise

■ lost in link or host failures

■ dropped due to buffer overflow

■ dropped due to aging or sell by date exceeded, for example the TTL (Time To Live) field in IP (Internet Protocol)

5. Traffic shaping

Traffic shaping is the practice of regulating network data transfer to assure a certain level of performance, quality of service (QoS). The practice involves favoring transmission of data from higher priority applications over lesser ones, as designated by the business organization. It is sometimes called packet shaping.

6. Congestion Control

This TCP function is designed to stop the sender shipping more data than the network can handle, as if trying to drink from a fire hose. TCP uses a number of mechanisms based on a parameter called the congestion window.

7. Protocol acceleration

A class of techniques for improving application performance by avoiding or circumventing shortcomings of various protocols. There are several forms of protocol acceleration:

■ TCP Acceleration

■ CIFS (Common Internet File Systems) and NFS (Network File System) Acceleration

■ HTTP (Hypertext Transfer Protocol) Acceleration

■ Microsoft Exchange Acceleration

See reference 7 below, under "WAN Optimization References" which presents good coverage of some of the factors listed above.

8. Transmission protocol

Choose your transmission protocol according to what you are transmitting. Some protocols are better than other at transmitting certain types of data. This will also dictate the expected loss (and hence retransmission) rate

9. Take advice

Take advice from outside if you aren't sure what you are doing.

10. Tricks of the trade

Some of the tricks of the optimization trade are:


Dr. Terry Critchley is the Author of "Making It in IT", "High Performance IT Services" and “High Availability IT Services”.

This blog was created from extracts from Terry Critchley's book: High Performance IT Services [ August 25 2016]


WAN Optimization References

There are a number of useful references on this topic. I found all the following ones useful in various areas as well as being quite easy to follow. I have therefore decided to list them all and let the network experts among you choose your own favorite.

1. WAN Optimization Part 1: TCP Limitations

2. WAN Optimization Part 2: Put Performance Second

3. WAN Optimization Part 3: Overcoming Bandwidth Limitations

4. The 2014 Application & Service Delivery Handbook. Part1: Introduction and Challenges
[Search on “Application and Service Delivery Handbook” to find versions from 2011, 2012 and 2013. Add the search term “webtorials” if the hit list is too large.]

5. The Definitive Guide to Cloud Acceleration

6. An Introduction to IP Header Compression

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...