Welcome!

Agile Computing Authors: Liz McMillan, Yeshim Deniz, Elizabeth White, Pat Romanski, Andy Thurai

Related Topics: @CloudExpo, Java IoT, Microservices Expo, Linux Containers, Containers Expo Blog, Agile Computing, @DXWorldExpo

@CloudExpo: Article

Understanding Application Performance on the Network | Part 4

Packet Loss

We know that losing packets is not a good thing; retransmissions cause delays. We also know that TCP ensures reliable data delivery, masking the impact of packet loss. So why are some applications seemingly unaffected by the same packet loss rate that seems to cripple others? From a performance analysis perspective, how do you understand the relevance of packet loss and avoid chasing red herrings?

In Part II, we examined two closely related constraints - bandwidth and congestion. In Part III, we discussed TCP slow-start and introduced the Congestion Window (CWD). In Part IV, we'll focus on packet loss, continuing the concepts from these two previous entries.

TCP Reliability
TCP ensures reliable delivery of data through its sliding window approach to managing byte sequences and acknowledgements; among other things, this sequencing allows a receiver to inform the sender of missing data caused by packet loss in multi-packet flows. Independently, a sender may detect packet loss through the expiration of its retransmission timer. We will look at the behavior and performance penalty associated with each of these cases; generally, the impact of packet loss will depend on both the characteristics of the flow and the position of the dropped packet within the flow.

The Retransmission Timer
Each packet a node sends is associated with a retransmission timer; if the timer expires before the sent data has been acknowledged, it is considered lost and retransmitted. There are two important characteristics of the retransmission timer that relate to performance. First, the default value for the initial retransmission timeout (RTO) is almost always 3000 milliseconds; this is adjusted to a more reasonable value as TCP observes actual path round-trip times. Second, the timeout value is doubled for subsequent retransmissions of a packet.

In small flows (a common characteristic of chatty operations - like web pages), the retransmission timer is the method used to detect packet loss. Consider a request or reply message of just 1000 bytes, sent in a single packet; if this packet is dropped, there will of course be no acknowledgement; the receiver has no idea the packet was sent. If the packet is dropped early in the life of a TCP connection - perhaps one of the SYN packets during the TCP 3-way handshake, or an initial GET request or a 304 Not Modified response - the dropped packet will be retransmitted only after 3 seconds have elapsed.

Triple Duplicate ACK
Within larger flows, a dropped packet may be detected before the retransmission time expires if the sender receives three duplicate ACKs; this is generally more efficient (faster) than waiting for the retransmission timer to expire. As the receiving node receives packets that are out of sequence (i.e., after the missing packet data should have been seen), it sends duplicate ACKs, the acknowledgement number repeatedly referencing the expected (missing) packet data. When the sending node receives the third duplicate ACK, it assumes the packet was in fact lost (not just delayed) and retransmits it. This event causes the sender to assume network congestion, reducing its congestion window by 50% to allow congestion to subside. Slow-start begins to increase the CWD from that new value, using a relatively conservative congestion avoidance ramp.

As an example, consider a server sending a large file to a client; the sending node is ramping up through slow-start. As the CWD reaches 24, earlier packet loss is detected via a triple duplicate ACK; the lost data is retransmitted, and the CWD is reduced to 12. Slow-start resumes from this point in its congestion avoidance mode.

While arguments abound about the inefficiency of existing congestion avoidance approaches, especially on high-speed networks, you can expect to see this behavior in today's networks.

Transaction Trace Illustration
Identifying retransmission timeouts using merged trace files is generally quite straightforward; we have proof the packet has been lost (because we see it on the sending side and not on the receiving side), and we know the delay between the dropped and retransmitted packets at the sending node. The Delta column in the Error Table indicates the retransmission delay.

Error Table entry showing a 3-second retransmission delay caused by a retransmission timeout (RTO)

For larger flows, you can illustrate the effect of dropped packets on the sender's Congestion Window by using the Time Plot view. For Series 1, graph the sender's Frames in Transit; this is essentially the CWD. For Series 2, graph the Cumulative Error Count in both directions. As errors (retransmitted packets or out-of-sequence packets) occur, the CWD will be reduced by about 50%.

Time Plot view showing the impact of packet loss (blue plot) on the Congestion Window (brown plot)

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.


IoT & Smart Cities Stories
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-c...
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 ...
The explosion of new web/cloud/IoT-based applications and the data they generate are transforming our world right before our eyes. In this rush to adopt these new technologies, organizations are often ignoring fundamental questions concerning who owns the data and failing to ask for permission to conduct invasive surveillance of their customers. Organizations that are not transparent about how their systems gather data telemetry without offering shared data ownership risk product rejection, regu...
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
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.
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Predicting the future has never been more challenging - not because of the lack of data but because of the flood of ungoverned and risk laden information. Microsoft states that 2.5 exabytes of data are created every day. Expectations and reliance on data are being pushed to the limits, as demands around hybrid options continue to grow.
Digital Transformation and Disruption, Amazon Style - What You Can Learn. Chris Kocher is a co-founder of Grey Heron, a management and strategic marketing consulting firm. He has 25+ years in both strategic and hands-on operating experience helping executives and investors build revenues and shareholder value. He has consulted with over 130 companies on innovating with new business models, product strategies and monetization. Chris has held management positions at HP and Symantec in addition to ...
Enterprises have taken advantage of IoT to achieve important revenue and cost advantages. What is less apparent is how incumbent enterprises operating at scale have, following success with IoT, built analytic, operations management and software development capabilities - ranging from autonomous vehicles to manageable robotics installations. They have embraced these capabilities as if they were Silicon Valley startups.
As IoT continues to increase momentum, so does the associated risk. Secure Device Lifecycle Management (DLM) is ranked as one of the most important technology areas of IoT. Driving this trend is the realization that secure support for IoT devices provides companies the ability to deliver high-quality, reliable, secure offerings faster, create new revenue streams, and reduce support costs, all while building a competitive advantage in their markets. In this session, we will use customer use cases...