Welcome!

Machine Learning Authors: Yeshim Deniz, Pat Romanski, Liz McMillan, Elizabeth White, Corey Roth

Related Topics: Microservices Expo, Java IoT, Linux Containers, Machine Learning , Agile Computing, @DXWorldExpo

Microservices Expo: Article

Understanding Application Performance on the Network | Part 1

A foundation for network triage

As a network professional, one of your newer roles is likely troubleshooting poor application performance. For most of us, our jobs have advanced beyond network "health," towards sharing - if not owning - responsibility for application delivery. There are many reasons for this more justifiable than the adage that the network is first to be blamed for performance problems. (Your application and system peers feel they are first to be blamed as well.) Two related influencing trends come to mind:

  1. Increased globalization, coupled with (in fact facilitated by) inexpensive bandwidth means that the network is becoming a more critical part of the business at the same time its constraint is shifting from bandwidth to latency.
  2. Many of the network devices - appliances - that sit in the path between remote offices and data centers are application-fluent, designed to enhance and speed application performance, often by spoofing application behaviors; in fact, many of these have evolved in response to problems introduced by increased network latency.

In an ideal world, your application performance management (APM) solution or your application-aware network performance management (AANPM) solution would automatically isolate the fault domain for you, providing all the diagnostic evidence you need to take the appropriate corrective actions. The reality is that this isn't always the case; intermittent problems, unexpected application or network behaviors, inefficient configuration settings, or just a desire for more concrete proof mean that manual troubleshooting remains a frequent exercise. Although it may seem like there are a near-unlimited number of root causes of poor application performance, and that trial and error, guesswork and finger-pointing are valid paths toward resolution, the truth is much different. In a series of network triage blog posts, I'll identify the very limited realm of possible performance constraints, explain how to measure and quantify their impact, illustrate these using network packet trace diagrams, and offer meaningful and supportable actions you might evaluate to correct the problem. Understanding how to detect these possible performance problems (there are twelve altogether) will help you troubleshoot faster, more accurately, with greater insight, while collaborating more effectively with your application and system peers.

In this introductory entry, I present the request/reply application paradigm assumption upon which most of the analyses depend, illustrate key packet-level measurements, and provide a list of the 12 bottleneck categories we'll discuss in future blog entries to the series.

Packet Flow Diagrams
Throughout the blog series I will be using packet flow diagrams to illustrate message flows on the network, often to emphasize TCP's influence on these flows. Some are drawings to illustrate concepts and theory, others are screenshots from Compuware's Transaction Trace Analysis that illustrate the pertinent performance bottleneck. The diagram conventions are simple:

  • Each arrow represents one TCP packet
  • Blue arrows are used to represent data packets
  • Red arrows are used to represent TCP ACK packets
  • The slope of the arrow represents network delay
  • Time flows from top to bottom

Terminology
We will frequently use the term "operation," which we define as the unit of work that an application performs on behalf of a user; we sometimes describe it as "Click (or Enter key) to screen update." Business transactions are made up of one or more operations; for example, a user may click through a series of screens (operations) to complete a customer order update. Operations are an important demarcation point, as they represent the unique performance dimension important to the business, to the user, and to IT. The time a user waits for the system to execute an operation impacts business transaction performance and therefore productivity, and is dictated by the performance of lower-level IT-managed hardware, software and services. Note that this terminology may differ somewhat from network probes that often use the term "transaction" to reference session-layer request-response exchanges, which we discuss next.

Request/Reply Paradigm
We assume a client/server or request/reply paradigm, with TCP as the transport; this covers virtually all of what we might refer to as interactive business applications. It would include, for example, web-based apps, "fat client" apps, file server access, file transfers, backups, etc. It specifically excludes voice and video streaming as well as the presentation tier of thin-client solutions that use protocols such as ICA and PCoIP.

For each operation, there will be at least one application-level request and one corresponding application-level reply. These can be considered application messages, sometimes referred to as application-layer protocol data units (PDUs). Consider a simple client-server operation. At the application layer, a request message is passed to the client's TCP stack (TCP socket) for segmentation (into packets), addressing, and transmission; these lower layer TCP stack functions are essentially transparent to the application. At the receiving end (the server), the data from the network packets is reassembled into the application layer message and delivered to the listener service for processing. Once processing is complete, the server application passes the reply message to the server's TCP stack, and the message contents are similarly segmented and transferred across the network to the client. The performance of these request/reply message exchanges is constrained by two factors; message processing (at the server or client) and message transmission (across the network).

It is helpful, then, to consider this request/reply message exchange as the basis for performance analysis; the reassembled messages represent our network-centric insight into the application, while the packets visible in the trace file inform us how efficiently the network transports these messages.

For further insight click here for the full article, and stay tuned for Part II.

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
Poor data quality and analytics drive down business value. In fact, Gartner estimated that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. By eroding trust in information, analytics and the business decisions based on these, it is a serious impediment to digital transformation.
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.
Discussions of cloud computing have evolved in recent years from a focus on specific types of cloud, to a world of hybrid cloud, and to a world dominated by the APIs that make today's multi-cloud environments and hybrid clouds possible. In this Power Panel at 17th Cloud Expo, moderated by Conference Chair Roger Strukhoff, panelists addressed the importance of customers being able to use the specific technologies they need, through environments and ecosystems that expose their APIs to make true change and transformation possible.
In an era of historic innovation fueled by unprecedented access to data and technology, the low cost and risk of entering new markets has leveled the playing field for business. Today, any ambitious innovator can easily introduce a new application or product that can reinvent business models and transform the client experience. In their Day 2 Keynote at 19th Cloud Expo, Mercer Rowe, IBM Vice President of Strategic Alliances, and Raejeanne Skillern, Intel Vice President of Data Center Group and GM, discussed how clients in this new era of innovation can apply data, technology, plus human ingenuity to springboard to advance new business value and opportunities.
The current age of digital transformation means that IT organizations must adapt their toolset to cover all digital experiences, beyond just the end users’. Today’s businesses can no longer focus solely on the digital interactions they manage with employees or customers; they must now contend with non-traditional factors. Whether it's the power of brand to make or break a company, the need to monitor across all locations 24/7, or the ability to proactively resolve issues, companies must adapt to the new world.