Measuring TCP Round-Trip Time in the Data Plane

Chen, Xiaoqi; Kim, Hyojoon; Aman, Javed M.; Chang, W.; Lee, Mack; Rexford, Jennifer

doi:10.1145/3405669.3405823

Cited by 42 publications

(21 citation statements)

References 17 publications

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“…Even if we make assumptions about the availability of this information, measuring flow RTT at the APs is unreliable. At the SDN control plane, measuring RTT inflates a variable and high RTT based on our measurements in ODL control plane, while measuring it in the SDN data plane [24] may not scale well due to the memory space constraints. Instead, we emphasize that throughput of TCP flows is the appropriate and robust metric that indicates the collective impact of the interaction of network parameters to the user-perceived performance.…”

Section: A Inter-class Bandwidth Allocationmentioning

confidence: 97%

DiffPerf: Towards Performance Differentiation and Optimization with SDN Implementation

Aljoby¹

2021

Preprint

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Section: A Inter-class Bandwidth Allocationmentioning

confidence: 97%

DiffPerf: Towards Performance Differentiation and Optimization with SDN Implementation

Aljoby¹

2021

Preprint

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“…Content may change prior to final publication. [95] which passively measures the RTT of TCP traffic in ISP networks. RTT measurement is important for detecting spoofing and routing attacks, ensuring Service Level Agreements (SLAs) compliance, measuring the Quality of Experience (QoE), improving congestion control, and many others.…”

Section: ) Queue and Other Metrics Measurementmentioning

confidence: 99%

“…ConQuest [88] taps on the ingress and egress links of a legacy router and uses a P4 switch to perform advanced fine-grained queue monitoring techniques. Note that legacy routers only support polling the total queue length (2) the switch calculates the round-trip time by using its highprecision timer (see [95] for details on how to associate the SEQ/ACKs to compute the RTT); (3) the switch report the RTT samples to an external server.…”

Section: G Interoperabilitymentioning

confidence: 99%

“…FIGURE 27: Example of using taps in a campus network to compute the round-trip time in the data plane. (1) The traffic is passively collected by the P4 switch;(2) the switch calculates the round-trip time by using its highprecision timer (see[95] for details on how to associate the SEQ/ACKs to compute the RTT); (3) the switch report the RTT samples to an external server.…”

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confidence: 99%

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An Exhaustive Survey on P4 Programmable Data Plane Switches: Taxonomy, Applications, Challenges, and Future Trends

2021

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Traditionally, the data plane has been designed with fixed functions to forward packets using a small set of protocols. This closed-design paradigm has limited the capability of the switches to proprietary implementations which are hard-coded by vendors, inducing a lengthy, costly, and inflexible process. Recently, data plane programmability has attracted significant attention from both the research community and the industry, permitting operators and programmers in general to run customized packet processing functions. This open-design paradigm is paving the way for an unprecedented wave of innovation and experimentation by reducing the time of designing, testing, and adopting new protocols; enabling a customized, top-down approach to develop network applications; providing granular visibility of packet events defined by the programmer; reducing complexity and enhancing resource utilization of the programmable switches; and drastically improving the performance of applications that are offloaded to the data plane. Despite the impressive advantages of programmable data plane switches and their importance in modern networks, the literature has been missing a comprehensive survey. To this end, this paper provides a background encompassing an overview of the evolution of networks from legacy to programmable, describing the essentials of programmable switches, and summarizing their advantages over Softwaredefined Networking (SDN) and legacy devices. The paper then presents a unique, comprehensive taxonomy of applications developed with P4 language; surveying, classifying, and analyzing more than 200 articles; discussing challenges and considerations; and presenting future perspectives and open research issues.

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“…This training time estimation implicitly assumes that the Round Trip Time (RTT) between Edge Computing Nodes and Aggregator is negligible with respect to the training time per round. In our settings, the latter is around 10s, so the assumption is reasonable since RTT is expected to be in the order of hundreds of ms at most [69]. Also the FedAVG execution time at the Aggregator can be considered negligible, being an inexpensive sequence of weighted sums.…”

Section: ) Training Time and Forecasting Performancementioning

confidence: 99%

Short-Term Energy Consumption Forecasting at the Edge: A Federated Learning Approach

Savi

Olivadese

2021

IEEE Access

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Residential short-term energy consumption forecasting plays an essential role in modern decentralized power systems. The rise of innovative prediction methods able to handle the high volatility of users' electrical load has posed the basis to accomplish this task. However these methods, which mostly rely on Artificial Neural Networks, require that a huge amount of users' fine-grained sensitive consumption data are centrally collected to train a generalized forecasting model, with implications on privacy and scalability. This paper proposes an innovative architecture specifically designed to overcome this need. By exploiting Federated Learning and Edge Computing capabilities, many Long Short-Term Memory (LSTM) models are locally trained by different users based on their own historical energy consumption samples. Such models are then aggregated by a specific-purpose node to build a generalized model that is re-distributed for improved forecasting at the edge. For better forecasting, our proposed local training procedure takes as input relevant features related to calendar (i.e., hour, weekday and average consumption of previous days) and weather conditions (i.e., clustered apparent temperature), and the architecture can group users according to consumption similarities (using K-means) or socioeconomic affinities. We thoroughly evaluate the approach through simulations, showing that it can lead to similar forecasting performance than a stateof-the-art centralized solution in terms of Root Mean Square Error (RMSE), but with up to an order of magnitude lower training time and up to 50 times less exchanged data when samples are recorded at finer granularity than one hour. Nonetheless, it keeps sensitive data local and therefore guarantees users' privacy.

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Measuring TCP Round-Trip Time in the Data Plane

Cited by 42 publications

References 17 publications

DiffPerf: Towards Performance Differentiation and Optimization with SDN Implementation

DiffPerf: Towards Performance Differentiation and Optimization with SDN Implementation

An Exhaustive Survey on P4 Programmable Data Plane Switches: Taxonomy, Applications, Challenges, and Future Trends

Short-Term Energy Consumption Forecasting at the Edge: A Federated Learning Approach

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