tprof: Performance profiling via structural aggregation and automated analysis of distributed systems traces
Open accessAbstract
The traditional approach for performance debugging relies upon performance profilers (e.g., gprof, VTune) that provide average function runtime information. These aggregate statistics help identify slow regions affecting the entire workload, but they are ill-suited for identifying slow regions that only impact a fraction of the workload, such as tail latency effects. This paper takes a new approach to performance profiling by utilizing distributed tracing systems (e.g., Dapper, Zipkin, Jaeger). Since traces provide detailed timing information on a per-request basis, it is possible to group and aggregate tracing data in many different ways to identify the slow parts of the system. Our new approach to trace aggregation uses the structure embedded within traces to hierarchically group similar traces and calculate increasingly detailed aggregate statistics based on how the traces are grouped. We also develop an automated tool for analyzing the hierarchy of statistics to identify the most likely performance issues. Our case study across two complex distributed systems illustrates how our tool is able to find multiple performance issues that lead to 10x and 28x performance improvements in terms of average and tail latency, respectively. Our comparison with a state-of-the-art industry tool shows that our tool can pinpoint performance slowdowns more accurately than current approaches.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
tprof organizes traces into hierarchical groups based on request types, span structures, and parent-child relationships, enabling aggregate trace representations for scalable analysis.
“tprof [84] organizes traces into hierarchical groups based on request types, span structures, and parent-child relationships, enabling aggregate trace representations for scalable analysis.”
From Anomaly detection and root-cause identification in microservices: a survey (2026) · cites this paper as [84]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
2017 USENIX Annual Technical Conference (USENIX ATC'17)
Quoc, D. L., Beck, M. S., Bhatotia, P., Chen, R., Fetzer, C. W., & Strufe, T. (2017) · Cited by 3,091 · see citing works
The Network Data Repository with Interactive Graph Analytics and Visualization
Rossi, R. A., & Ahmed, N. K. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9277 · Cited by 2,619 · see citing works
Detecting large-scale system problems by mining console logs
Xu, W., Huang, L., Fox, A., Patterson, D., & Jordan, M. I. (2009) · Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles · doi:10.1145/1629575.1629587 · Cited by 1,332 · see citing works
Self-citationGraham, S. L., Kessler, P. B., & McKusick, M. K. (1982) · doi:10.1145/800230.806987 · Cited by 975 · see citing works
Gan, Y., Zhang, Y., Cheng, D., Shetty, A., Rathi, P., Katarki, N., Bruno, A., Hu, J., Ritchken, B., Jackson, B., et al. (2019) · Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems · doi:10.1145/3297858.3304013 · Cited by 665 · see citing works
Using magpie for request extraction and workload modelling
Barham, P., Donnelly, A., Isaacs, R., & Mortier, R. (2004) · Operating Systems Design and Implementation · Cited by 571 · see citing works
Execution Anomaly Detection in Distributed Systems through Unstructured Log Analysis
Fu, Q., Lou, J., Wang, Y., & Jiang, L. (2009) · Proceedings · doi:10.1109/icdm.2009.60 · Cited by 555 · see citing works
Dapper, a Large-Scale Distributed Systems Tracing Infrastructure
Sigelman, B. H., Barroso, L. A., Burrows, M., Stephenson, P., Plakal, M., Beaver, D., Jaspan, S., & Shanbhag, C. (2010) · Cited by 477 · see citing works
X-trace: a pervasive network tracing framework
Fonseca, R., Porter, G., Katz, R. H., Shenker, S. J., & Stoica, I. G. (2007) · Cited by 438 · see citing works
Zhou, X., Peng, X., Xie, T., Sun, J., Ji, C., Li, W., & Ding, D. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2018.2887384 · Cited by 410 · see citing works
Making sense of performance in data analytics frameworks
Ousterhout, K., Rasti, R., Ratnasamy, S., Shenker, S. J., & Chun, B.-G. (2015) · Networked Systems Design and Implementation · Cited by 337 · see citing works
Gan, Y., Zhang, Y., Hu, K., Cheng, D., He, Y., Pancholi, M., & Delimitrou, C. (2019) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3297858.3304004 · Cited by 268 · see citing works
Unsupervised Detection of Microservice Trace Anomalies through Service-Level Deep Bayesian Networks
Liu, P., Xu, H., Ouyang, Q., Jiao, R., Chen, Z., Zhang, S., Yang, J., Mo, L., Zeng, J., Xue, W., et al. (2020) · 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) · doi:10.1109/issre5003.2020.00014 · Cited by 252 · see citing works
What Bugs Live in the Cloud? A Study of 3000+ Issues in Cloud Systems
Gunawi, H. S., Hao, M., Leesatapornwongsa, T., Patana-anake, T., Do, T., Adityatama, J., Eliazar, K. J., Laksono, A., Lukman, J. F., Martin, V., et al. (2014) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/2670979.2670986 · Cited by 211 · see citing works
(2015) · doi:10.1145/2815400.2815415 · Cited by 202 · see citing works
X-ray: automating root-cause diagnosis of performance anomalies in production software
Attariyan, M., Chow, M., & Flinn, J. (2012) · doi:10.5555/2387880.2387910 · Cited by 186 · see citing works
The mystery machine: end-to-end performance analysis of large-scale internet services
Chow, M., Meisner, D., Flinn, J., Peek, D., & Wenisch, T. F. (2014) · Operating Systems Design and Implementation · doi:10.5555/2685048.2685066 · Cited by 182 · see citing works
Benchmarking microservice systems for software engineering research
Zhou, X., Peng, X., Xie, T., Sun, J., Xu, C., Ji, C., & Zhao, W. (2018) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3183440.3194991 · Cited by 181 · see citing works
CloudRanger: Root Cause Identification for Cloud Native Systems
Wang, P., Xu, J., Ma, M., Lin, W., Pan, D., Wang, Y., & Chen, P. (2018) · 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID) · doi:10.1109/ccgrid.2018.00076 · Cited by 181 · see citing works
Diagnosing performance changes by comparing request flows
Sambasivan, R. R., Zheng, A. X., Rosa, M. D., Krevat, E., Whitman, S., Stroucken, M., Wang, W. Y., Xu, L., & Ganger, G. R. (2011) · doi:10.5555/1972457.1972463 · Cited by 178 · see citing works
Sage: practical and scalable ML-driven performance debugging in microservices
Gan, Y., Liang, M., Dev, S., Lo, D., & Delimitrou, C. (2021) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3445814.3446700 · Cited by 177 · see citing works
Anomaly Detection from System Tracing Data Using Multimodal Deep Learning
Nedelkoski, S., Cardoso, J., & Kao, O. (2019) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2019.00038 · Cited by 151 · see citing works
(2017) · doi:10.1145/3132747.3132749 · Cited by 144 · see citing works
Thereska, E., Salmon, B., Strunk, J. D., Wachs, M., Abd-El-Malek, M., Lopez, J. C., & Ganger, G. R. (2006) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/1140103.1140280 · Cited by 129 · see citing works
Cherkasova, L. A., Ozonat, K., Ningfang Mi, A., Symons, J., & Evgenia Smirni, A. (2008) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn.2008.4630116 · Cited by 116 · see citing works
lprof: a non-intrusive request flow profiler for distributed systems
Zhao, X., Zhang, Y., Lion, D., Ullah, M. F., Luo, Y., Yuan, D., & Stumm, M. (2014) · TSpace (University of Toronto) · doi:10.5555/2685048.2685099 · Cited by 111 · see citing works
Curtsinger, C., & Berger, E. D. (2015) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/2815400.2815409 · Cited by 102 · see citing works
FChain: Toward Black-Box Online Fault Localization for Cloud Systems
Nguyen, H., Shen, Z., Tan, Y., & Gu, X. (2013) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2013.26 · Cited by 96 · see citing works
Automated anomaly detection and performance modeling of enterprise applications
Cherkasova, L. A., Ozonat, K., Ningfang Mi, A., Symons, J., & Evgenia Smirni, A. (2009) · ACM Transactions on Computer Systems · doi:10.1145/1629087.1629089 · Cited by 91 · see citing works
Anomaly Detection and Classification using Distributed Tracing and Deep Learning
Nedelkoski, S., Cardoso, J., & Kao, O. (2019) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid.2019.00038 · Cited by 91 · see citing works
CloudPD: Problem determination and diagnosis in shared dynamic clouds
Sharma, B., Jayachandran, P., Verma, A., & Das, C. R. (2013) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn.2013.6575298 · Cited by 79 · see citing works
Zhao, X., Rodrigues, K., Luo, Y., Yuan, D., & Stumm, M. (2016) · Operating Systems Design and Implementation · doi:10.5555/3026877.3026924 · Cited by 79 · see citing works
Performance Monitoring and Root Cause Analysis for Cloud-hosted Web Applications
Jayathilaka, H., Krintz, C., & Wolski, R. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052649 · Cited by 79 · see citing works
Beschastnikh, I., Wang, P., Brun, Y., & Ernst, M. D. (2016) · Communications of the ACM · doi:10.1145/2909480 · Cited by 78 · see citing works
Chen, P., Qi, Y., & Hou, D. (2016) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2016.2607739 · Cited by 73 · see citing works
Shan, H., Chen, Y., Li, X., Zhang, Y., Xiao, X., He, X., Li, M., & Ding, W. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313653 · Cited by 73 · see citing works
Dean, D. J., Nguyen, H., Gu, X., Zhang, H., Rhee, J., Arora, N., & Jiang, G. (2014) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/2670979.2670987 · Cited by 66 · see citing works
Cross-dataset time series anomaly detection for cloud systems
Zhang, X., Lin, Q., Xu, Y. D., Hsieh, K., Sui, K., Meng, X., Xu, Y., Zhang, W., Shen, F., Zhang, D., et al. (2019) · NOVA (University of Newcastle Australia) · Cited by 55 · see citing works
Principled workflow-centric tracing of distributed systems
Sambasivan, R. R., Shafer, I., Mace, J., Sigelman, B. H., Fonseca, R., & Ganger, G. R. (2016) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/2987550.2987568 · Cited by 49 · see citing works
Log summarization and anomaly detection for troubleshooting distributed systems
Gunter, D., Tierney, B. L., Brown, A., Martin Swany, A., Bresnahan, J., & Schopf, J. M. (2007) · IEEE/ACM International Conference on Grid Computing (GRID) · doi:10.1109/grid.2007.4354137 · Cited by 48 · see citing works
Universal context propagation for distributed system instrumentation
Mace, J., & Fonseca, R. (2018) · doi:10.1145/3190508.3190526 · Cited by 45 · see citing works
Sifter: Scalable Sampling for Distributed Traces, without Feature Engineering
Las-Casas, P., Papakerashvili, G., Anand, V., & Mace, J. (2019) · Proceedings of the ACM Symposium on Cloud Computing · doi:10.1145/3357223.3362736 · Cited by 42 · see citing works
Wen, S., Chabbi, M., & Liu, X. (2017) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3037697.3037729 · Cited by 41 · see citing works
Proceedings of the ACM Symposium on Cloud Computing
(2021) · doi:10.1145/3472883 · Cited by 39 · see citing works
Weighted Sampling of Execution Traces
Las-Casas, P., Mace, J., Guedes, D. O., & Fonseca, R. (2018) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3267809.3267841 · Cited by 36 · see citing works
Erlingsson, Ú., Peinado, M., Peter, S., Budiu, M., & Mainar-Ruiz, G. (2012) · ACM Transactions on Computer Systems · doi:10.1145/2382553.2382555 · Cited by 35 · see citing works
Ateş, E., Sturmann, L., Toslali, M., Krieger, O. Y., Megginson, R., Coskun, A. K., & Sambasivan, R. R. (2019) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3357223.3362704 · Cited by 34 · see citing works
Performance Analysis of Cloud Applications
Ardelean, D., Diwan, A., & Erdman, C. (2018) · Networked Systems Design and Implementation · Cited by 29 · see citing works
Apache CloudStack Cloud Computing
Sabharwal, N., & Shankar, R. (2013) · Cited by 25 · see citing works
Precise request tracing and performance debugging for multi-tier services of black boxes
Zhang, Z. H., Zhan, J., Li, Y., Wang, L., Meng, D., & Sang, B. (2009) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn.2009.5270321 · Cited by 23 · see citing works
Zeno: Diagnosing Performance Problems with Temporal Provenance.
Wu, Y., Chen, A., & Phan, L. T. X. (2019) · Networked Systems Design and Implementation · Cited by 16 · see citing works
Efficient flow profiling for detecting performance bugs
Mudduluru, R., & Ramanathan, M. K. (2016) · International Symposium on Software Testing and Analysis · doi:10.1145/2931037.2931066 · Cited by 15 · see citing works
Pinpointing performance inefficiencies via lightweight variance profiling
Su, P., Jiao, S., Chabbi, M., & Liu, X. (2019) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3295500.3356167 · Cited by 12 · see citing works
Watching for Software Inefficiencies with Witch
Wen, S., Liu, X., Byrne, J., & Chabbi, M. (2018) · ACM SIGPLAN Notices · doi:10.1145/3296957.3177159 · Cited by 5 · see citing works
InvarNet-X: A Black-Box Invariant-Based Approach to Diagnosing Big Data Systems
Chen, P., Qi, Y., & Hou, D. (2015) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2015.2497143 · Cited by 4 · see citing works
Proceedings of the 1986 SIGPLAN symposium on Compiler construction
(1986) · doi:10.1145/12276 · Cited by 4 · see citing works
Flagged first, then by citations
1 of the 57 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Charlie Curtsinger and Emery D. Berger. 2016. COZ: Finding Code that Counts with Causal Profiling. In 2016 USENIX Annual Tech- nical Conference (USENIX ATC 16). USENIX Association, Denver, CO. https://www.usenix.org/conference/atc16/technical-sessions/ presentation/curtsinger
- A. Parker, D. Spoonhower, J. Mace, B. Sigelman, and R. Isaacs. 2020. Distributed Tracing in Practice: Instrumenting, Analyzing, and Debug- ging Microservices. O'Reilly Media, Incorporated. https://books.google. com/books?id=fgfIyAEACAAJ
Cited by
Sorted by citations
Yu, G., Chen, P., Li, Y., Chen, H., Li, X., & Zheng, Z. (2023) · Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering · doi:10.1145/3611643.3616249 · Cited by 140 · see citing works
Network-Centric Distributed Tracing with DeepFlow: Troubleshooting Your Microservices in Zero Code
Shen, J., Zhang, H., Xiang, Y., Shi, X., Li, X., Shen, Y., Zhang, Z., Wu, Y., Yin, X., Wang, J., et al. (2023) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/3603269.3604823 · Cited by 54 · see citing works
Gu, S., Rong, G., Ren, T., Zhang, H., Shen, H., Yu, Y., Li, X., Ouyang, J., & Chen, C. (2023) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2023.3241299 · Cited by 46 · see citing works
Ding, R., Zhang, C., Wang, L., Xu, Y., Ma, M., Wu, X., Zhang, M., Chen, Q., Gao, X., Gao, X., et al. (2023) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3611643.3613864 · Cited by 38 · see citing works
PUTraceAD: Trace Anomaly Detection with Partial Labels based on GNN and PU Learning
Zhang, K., Zhang, C., Peng, X., & Sha, C. (2022) · 2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE) · doi:10.1109/issre55969.2022.00032 · Cited by 33 · see citing works
TraceArk: Towards Actionable Performance Anomaly Alerting for Online Service Systems
Zeng, Z., Zhang, Y., Xu, Y. D., Ma, M., Qiao, B., Zou, W., Chen, Q., Zhang, M., Zhang, X., Zhang, H., et al. (2023) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip58684.2023.00029 · Cited by 31 · see citing works
(2024) · doi:10.1145/3597503.3639088 · Cited by 22 · see citing works
From Point-wise to Group-wise: A Fast and Accurate Microservice Trace Anomaly Detection Approach
Xie, Z., Pei, C., Li, W., Jiang, H., Su, L., Li, J., Xie, G., & Pei, D. (2023) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3611643.3613861 · Cited by 20 · see citing works
Liu, J., Wang, Q., Zhang, S., Hu, L., & Silva, D. D. (2023) · International Middleware Conference · doi:10.1145/3590140.3592851 · Cited by 17 · see citing works
Li, Y., Yu, G., Chen, P., Zhang, C., & Zheng, Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20984-0_15 · Cited by 14 · see citing works
STEAM: Observability-Preserving Trace Sampling
He, S., Feng, B., Li, L., Zhang, X., Kang, Y., Lin, Q., Rajmohan, S., & Zhang, D. (2023) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3611643.3613881 · Cited by 14 · see citing works
Zhang, Y., Isaacs, R., Yue, Y., Yang, J., Zhang, L., & Vigfússon, Ý. (2023) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3620678.3624787 · Cited by 13 · see citing works
The Tale of Errors in Microservices
Lee, I.-T. A., Zhang, Z., Parwal, A., & Chabbi, M. (2024) · Proceedings of the ACM on Measurement and Analysis of Computing Systems · doi:10.1145/3700436 · Cited by 13 · see citing works
TraStrainer: Adaptive Sampling for Distributed Traces with System Runtime State
Huang, H., Zhang, X., Chen, P., He, Z., Zhi-ming, C., Yu, G., Chen, H., & Sun, C. (2024) · Proceedings of the ACM on software engineering. · doi:10.1145/3643748 · Cited by 11 · see citing works
Mint: Cost-Efficient Tracing with All Requests Collection via Commonality and Variability Analysis
Huang, H., Chen, C., Chen, K., Chen, P., Yu, G., He, Z., Wang, Y., Zhang, H., & Zhou, Q. (2025) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3669940.3707287 · Cited by 8 · see citing works
Blueprint: A Toolchain for Highly-Reconfigurable Microservice Applications
Anand, V., Garg, D., Kaufmann, A., & Mace, J. (2023) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3600006.3613138 · Cited by 8 · see citing works
SparseRCA: Unsupervised Root Cause Analysis in Sparse Microservice Testing Traces
Yao, Z., Ye, H., Pei, C., Cheng, G., Wang, G., Liu, Z., Chen, H., Cui, H., Li, Z., Li, J., et al. (2024) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre62328.2024.00045 · Cited by 8 · see citing works
Seshagiri, V., Huye, D., Liu, L., Wildani, A., & Sambasivan, R. R. (2022) · Journal of Systems Research · doi:10.5070/sr32157839 · Cited by 7 · see citing works
EXIST: Enabling Extremely Efficient Intra-Service Tracing Observability in Datacenters
Wang, X., Hou, X., Li, C., Li, Y., Liu, D., Xu, G., Yang, G., Zhang, L., Wu, Y., Yuan, X., et al. (2025) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3676641.3716283 · Cited by 5 · see citing works
A Survey of Program Analysis for Distributed Software Systems
Cai, H. (2025) · ACM Computing Surveys · doi:10.1145/3742900 · Cited by 5 · see citing works
AutoScope: Code Knowledge Enhanced Span-level Sampling for Distributed Tracing
Wu, Y., Yu, G., Jiang, Z., Li, Y., & Lyu, M. R.-T. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3830907 · Cited by 3 · see citing works
Systemizing and Mitigating Topological Inconsistencies in Alibaba's Microservice Call-graph Datasets
Huye, D., Liu, L., & Sambasivan, R. R. (2024) · ACM/SPEC International Conference on Performance Engineering (ICPE) · doi:10.1145/3629526.3645043 · Cited by 1 · see citing works
Unleashing Performance Insights with Online Probabilistic Tracing
Toslali, M., Qasim, S., Parthasarathy, S., Oliveira, F. A., Huang, H., Stringhini, G., Liu, Z., & Coskun, A. K. (2024) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e61754.2024.00015 · Cited by 1 · see citing works
Towards On-the-Fly Code Performance Profiling
Hu, X., Lin, W., Liu, Z., Ling, M., Xia, X., Wang, Y., & Lo, D. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3725212 · Cited by 1 · see citing works
LabelEase: A Semi-Automatic Tool for Efficient and Accurate Trace Labeling in Microservices
Zhang, S., Che, Z., Pan, Z., Nie, X., Sun, Y., Pan, L., & Pei, D. (2024) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre62328.2024.00032 · Cited by 1 · see citing works
Anomaly detection and root-cause identification in microservices: a survey
Barata, L. M., Sequeira, S., Lopes, E., Inácio, P. R. M., & Freire, M. M. (2026) · Cluster Computing · doi:10.1007/s10586-026-06095-9
“Deep learning is frequently applied in supervised and semi-supervised contexts.”
Bottleneck identification and failure prevention with procedural learning in 5G RAN
Sundqvist, T., Bhuyan, M. H., & Elmroth, E. (2023) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid57682.2023.00047
Poster Paper: Efficient Navigation of Cloud Performance with ’nuffTrace
Qasim, S., Toslali, M., Clark, Q., Parthasarathy, S., Oliveira, F., Liu, A., Stringhini, G., & Coskun, A. K. (2023) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e59103.2023.00035
Belkhiri, A., Attia, M. B., de Magalhães, F. G., & Nicolescu, G. (2024) · ACM/SPEC International Conference on Performance Engineering (ICPE) · doi:10.1145/3629527.3651432
Proactively Verifying Quantitative Network Policy Across Unsafe and Unreliable Environments
Li, Y., Zhang, H., Wang, J., Shi, X., Yin, X., Wang, Z., Hu, J., Miao, C., & Wu, J. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3409935
Ekhlasi, M., Fiorini, A., Dagenais, M., Ezzati‐Jivan, N., & Lamothe, M. (2025) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsme64153.2025.00056
PerfSuite: An Automatic Performance Suite for HPC Applications
Jiang, Q., Jiao, P., Wang, J., Shen, Y., Zhang, L., & Liu, Y. (2025) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126626500507
Shahedi, K., Khouzam, M., Li, H., Lamothe, M., & Khomh, F. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00286
Zhou, T., Peng, X., Zhang, J., Sha, C., Zhang, C., Yuan, Z., & Xie, S. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792164
The Projection Onto Algorithm Plane
Wang, C., & Tang, H. (2026) · Springer eBooks · doi:10.1007/978-981-95-5025-8_5
Ding, K., Ran, D., Ma, Y., Song, Z., Bian, K., & Xie, T. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3712748
Alti, B. (2026) · Journal of Intelligent Decision Making and Information Science · doi:10.59543/jidmis.v3.847
CubeTrace: Microscopic Network Tracing for Heterogeneous Cloud Gateways
Xiao, Y., Yang, Y., Zheng, J., Li, X., Gu, D., Zhang, J., Wan, M., Pei, C., Tian, C., Xu, M., et al. (2026) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/3789240.3829165
Geng, Y., Zhang, H., Wu, Z., Li, Y., Wang, J., & Yin, X. (2026) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3744916.3764518
- Authors
- Huang, L., & Zhu, T.
- Affiliations
- Pennsylvania State University (US)
- Venue
- Proceedings of the ACM Symposium on Cloud Computing
- Publisher
- ACM
- Published
- 2021-11
- Pages
- 76-91
- OpenAlex
- W3208273031
- Cited by
- 37
- References
- 57
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 9 · 2023: 12 · 2022: 3
- FWCI
- 5.55
- Type
- conference paper
- Language
- en
- Open access
- Locations
- ACM Symposium on Cloud Computing (SoCC) (publishedVersion)
- ScholarSphere (Penn State Libraries) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Keywords
- performance profiling, distributed tracing, tail latency, performance debugging, distributed systems, automated performance analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
