Welcome!

Machine Learning Authors: Pat Romanski, Yeshim Deniz, Liz McMillan, Elizabeth White, Zakia Bouachraoui

Related Topics: Microservices Expo, Java IoT, Linux Containers, Containers Expo Blog, @CloudExpo, @DXWorldExpo, SDN Journal

Microservices Expo: Article

Understanding APM on the Network

TCP Window Size

In Part 6, we dove into the Nagle algorithm - perhaps (or hopefully) something you'll never see. In Part VII, we get back to "pure" network and TCP roots as we examine how the TCP receive window interacts with WAN links.

TCP Window Size
Each node participating in a TCP connection advertises its available buffer space using the TCP window size field. This value identifies the maximum amount of data a sender can transmit without receiving a window update via a TCP acknowledgement; in other words, this is the maximum number of "bytes in flight" - bytes that have been sent, are traversing the network, but remain unacknowledged. Once the sender has reached this limit and exhausted the receive window, the sender must stop and wait for a window update.

The sender transmits a full window then waits for window updates before continuing. As these window updates arrive, the sender advances the window and may transmit more data.

Long Fat Networks
High-speed, high-latency networks, sometimes referred to as Long Fat Networks (LFNs), can carry a lot of data. On these networks, small receive window sizes can limit throughput to a fraction of the available bandwidth. These two factors - bandwidth and latency - combine to influence the potential impact of a given TCP window size. LFNs networks make it possible - common, even - for a sender to transmit very fast (high bandwidth) an entire TCP window's worth of data, having then to wait until the packets reach the distant remote site (high latency) so that acknowledgements can be returned, informing the sender of successful data delivery and available receive buffer space.

The math (and physics) concepts are straightforward. As the network speed increases, data can be clocked out onto the network medium more quickly; the bits are literally closer together. As latency increases, these bits take longer to traverse the network from sender to receiver. As a result, more bits can fit on the wire. As LFNs become more common, exhausting a receiver's TCP window becomes increasingly problematic for some types of applications.

Bandwidth Delay Product
The Bandwidth Delay Product (BDP) is a simple formula used to calculate the maximum amount of data that can exist on the network (referred to as bits or bytes in flight) based on a link's characteristics:

  • Bandwidth (bps) x RTT (seconds) = bits in flight
  • Divide the result by 8 for bytes in flight

If the BDP (in bytes) for a given network link exceeds the value of a session's TCP window, then the TCP session will not be able to use all of the available bandwidth; instead, throughput will be limited by the receive window (assuming no other constraints, of course).

The BDP can also be used to calculate the maximum throughput ("bandwidth") of a TCP connection given a fixed receive window size:

  • Bandwidth = (window size *8)/RTT

In the not-too-distant past, the TCP window had a maximum value of 65535 bytes. While today's TCP implementations now generally include a TCP window scaling option that allows for negotiated window sizes to reach 1GB, many factors limit its practical utility. For example, firewalls, load balancers and server configurations may purposely disable the feature. The reality is that we often still need to pay attention to the TCP window size when considering the performance of applications that transfer large amounts of data, particularly on enterprise LFNs.

As an example, consider a company with offices in New York and San Francisco; they need to replicate a large database each night, and have secured a 20Mbps network connection with 85 milliseconds of round-trip delay. Our BDP calculation tells us that the BDP is 212,500 (20,000,000 x .085 *8); in other words, a single TCP connection would require a 212KB window in order to take advantage of all of the bandwidth. The BDP calculation also tells us that the configured TCP window size of 65535 will permit approximately 6Mbps throughput (65535*8/.085), less than 1/3 of the link's capacity.

A link's BDP and a receiver's TCP window size are two factors that help us to identify the potential throughput of an operation. The remaining factor is the operation itself, specifically the size of individual request or reply flows. Only flows that exceed the receiver's TCP window size will benefit from, or be impacted by, these TCP window size constraints. Two common scenarios help illustrate this. Let's say a user needs to transfer a 1GB file:

  • Using FTP (in stream mode) will cause the entire file to be sent in a single flow; this operation could be severely limited by the receive window.
  • Using SMB (at least older versions of the protocol) will cause the file to be sent in many smaller write commands, as SMB used to limit write messages to under 64KB; this operation would not be able to take advantage of a TCP receive window of greater than 64K. (Instead, the operation would more likely be limited by application turns and link latency; we discuss chattiness in Part 8.)

For more networking tips, click here for the full article.

More Stories By Gary Kaiser

Gary Kaiser is a Subject Matter Expert in Network Performance Analytics at Dynatrace, responsible for DC RUM’s technical marketing programs. He is a co-inventor of multiple performance analysis features, and continues to champion the value of network performance analytics. He is the author of Network Application Performance Analysis (WalrusInk, 2014).

Comments (0)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.


CloudEXPO Stories
With more than 30 Kubernetes solutions in the marketplace, it's tempting to think Kubernetes and the vendor ecosystem has solved the problem of operationalizing containers at scale or of automatically managing the elasticity of the underlying infrastructure that these solutions need to be truly scalable. Far from it. There are at least six major pain points that companies experience when they try to deploy and run Kubernetes in their complex environments. In this presentation, the speaker will detail these pain points and explain how cloud can address them.
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-centric compute for the most data-intensive applications. Hyperconverged systems already in place can be revitalized with vendor-agnostic, PCIe-deployed, disaggregated approach to composable, maximizing the value of previous investments.
When building large, cloud-based applications that operate at a high scale, it's important to maintain a high availability and resilience to failures. In order to do that, you must be tolerant of failures, even in light of failures in other areas of your application. "Fly two mistakes high" is an old adage in the radio control airplane hobby. It means, fly high enough so that if you make a mistake, you can continue flying with room to still make mistakes. In his session at 18th Cloud Expo, Lee Atchison, Principal Cloud Architect and Advocate at New Relic, discussed how this same philosophy can be applied to highly scaled applications, and can dramatically increase your resilience to failure.
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by sharing information within the building and with outside city infrastructure via real time shared cloud capabilities.
As Cybric's Chief Technology Officer, Mike D. Kail is responsible for the strategic vision and technical direction of the platform. Prior to founding Cybric, Mike was Yahoo's CIO and SVP of Infrastructure, where he led the IT and Data Center functions for the company. He has more than 24 years of IT Operations experience with a focus on highly-scalable architectures.