Telemetry orchestration in programmable data planes

• Grant number: 18/23092-1
• Support Opportunities: Regular Research Grants
• Start date: December 01, 2019
• End date: May 31, 2022
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Agreement: MCTI/MC
• Principal Investigator: Marcelo Caggiani Luizelli
• Grantee: Marcelo Caggiani Luizelli
• Host Institution: Campus Alegrete. Universidade Federal do Pampa (UNIPAMPA). Ministério da Educação (Brasil). Alegrete , SP, Brazil
• Associated researchers: Arthur Francisco Lorenzon ; Christian Rodolfo Esteve Rothenberg ; Diego Luis Kreutz ; Fábio Diniz Rossi
• Associated scholarship(s): 20/13189-8 - Optimizing the orchestration of in-band network telemetry, BP.TT; 20/06539-2 - Development of a component for system of orchestration of in-band telemetry, BP.TT; 20/03116-3 - Development of a component for system of orchestration of in-band telemetry, BP.TT

Abstract

In-band network telemetry is an emerging network monitoring paradigm. By collecting low-level telemetry items in real time, in-band telemetry is able to substantially enhance network-wide visibility - allowing, for example, timely detection problems such as micro-burst. Recent studies have focused on (i) developing mechanisms to increase network-wide visibility; and (ii) to design new monitoring solutions. However, little has been done to coordinate the process of collecting telemetry items in this new paradigm. This is particularly challenging for two main reasons. First, depending on which network telemetry items are collected, it might degrade network-wide visibility in terms of consistency and freshness. Second, depending on how network telemetry is collected, it might impact network performance. In this project, we investigate and model the In-band Network Telemetry Planning Problem - the problem of planning the telemetry collection process. The main idea consists of dynamic exploring the interplay between monitoring application performance metrics (e.g., accuracy and freshness) and the in-band telemetry data acquisition. Subsequently, we intend to make the proposed operational solutions in a network structure with programmability support. (AU)

Scientific publications

(9)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

CANOFRE, RONALDO; CASTRO, ARIEL G.; LORENZON, ARTHUR F.; ROSSI, FABIO D.; LUIZELLI, MARCELO C.; BAROLLI, L; HUSSAIN, F; ENOKIDO, T. Towards Efficient Selective In-Band Network Telemetry Report Using SmartNICs. ADVANCED INFORMATION NETWORKING AND APPLICATIONS, AINA-2022, VOL 1, v. 449, p. 14-pg., 2022-01-01. (18/23092-1, 20/05115-4)

BUENO, GUILHERME; SAQUETTI, MATEUS; RODRIGUES, PABLO; LAMB, IVAN; GASPARY, LUCIANO; LUIZELLI, MARCELO C.; ZHANI, MOHAMED FATEN; AZAMBUJA, JOSE RODRIGO; CORDEIRO, WEVERTON. Managing Virtual Programmable Switches: Principles, Requirements, and Design Directions. IEEE COMMUNICATIONS MAGAZINE, v. 60, n. 2, p. 7-pg., 2022-02-01. (20/05183-0, 18/23092-1)

DALLANORA, LEANDRO M.; CASTRO, ARIEL G.; DA COSTA FILHO, ROBERTO I. T.; ROSSI, FABIO D.; LORENZON, ARTHUR F.; LUIZELLI, MARCELO C.; IEEE. DyPro: Dynamic Probing Planning for In-Band Network Telemetry. 2022 27TH IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (IEEE ISCC 2022), v. N/A, p. 6-pg., 2022-01-01. (20/05115-4, 20/05183-0, 18/23092-1)

KONZEN, MARCOS PAULO; RAMIRES IZOLAN, PATRIC LINCOLN; GRIESANG, FABIO JUNIOR; DE SOUZA, PAULO SILAS; FERRETO, TIAGO COELHO; LORENZON, ARTHUR FRANCISCO; LUIZELLI, MARCELO CAGGIANI; BALZANO DE MATTOS, JULIO CARLOS; DA ROSA, CINARA EWERLING; ROSSI, FABIO DINIZ; et al. Multivariate Interpolation at the Edge to Infer Faulty IoT Sensor Metrics. PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND SERVICES SCIENCE (CLOSER), v. N/A, p. 8-pg., 2022-01-01. (18/23092-1, 20/05183-0, 20/05115-4)

LUIZELLI, MARCELO C.; CANOFRE, RONALDO; LORENZON, ARTHUR F.; ROSSI, FABIO D.; CORDEIRO, WEVERTON; CAICEDO, OSCAR M.. In-Network Neural Networks: Challenges and Opportunities for Innovation. IEEE NETWORK, v. 35, n. 6, p. 68-74, NOV-DEC 2021. (20/05183-0, 18/23092-1, 20/05115-4)

CASTRO, ARIEL G.; LORENZON, ARTHUR F.; ROSSI, FABIO D.; DA COSTA FILHO, ROBERTO I. T.; RAMOS, V, FERNANDO M.; ROTHENBERG, CHRISTIAN E.; LUIZELLI, MARCELO C.. Near-Optimal Probing Planning for In-Band Network Telemetry. IEEE COMMUNICATIONS LETTERS, v. 25, n. 5, p. 1630-1634, MAY 2021. (18/23092-1)

SAQUETTI, MATEUS; CANOFRE, RONALDO; LORENZON, ARTHUR F.; ROSSI, FABIO D.; AZAMBUJA, JOSE RODRIGO; CORDEIRO, WEVERTON; LUIZELLI, MARCELO C.. oward In-Network Intelligence: Running Distributed Artificial Neural Networks in the Data Plan. IEEE COMMUNICATIONS LETTERS, v. 25, n. 11, p. 3551-3555, NOV 2021. (18/23092-1, 20/05183-0, 20/05115-4)

DA SILVA, VINICIUS S.; NOGUEIRA, ANGELO G. D.; DE LIMA, EVERTON CAMARGO; ROCHA, HIAGO M. G. DE A.; SERPA, MATHEUS S.; LUIZELLI, MARCELO C.; ROSSI, FABIO D.; NAVAUX, PHILIPPE O. A.; BECK, ANTONIO CARLOS S.; LORENZON, ARTHUR FRANCISCO. Smart resource allocation of concurrent execution of parallel applications. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, SEP 2021. (20/05115-4, 18/23092-1)

BERNED, GUSTAVO; ROSSI, FABIO D.; LUIZELLI, MARCELO C.; BECK, ANTONIO CARLOS S.; LORENZON, ARTHUR F.; IEEE COM SOC. Decreasing the Learning Cost of Offline Parallel Application Optimization Strategies. 2020 28TH EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED AND NETWORK-BASED PROCESSING (PDP 2020), v. N/A, p. 8-pg., 2020-01-01. (18/23092-1)