Pptam

Avritzer, Alberto; Menasché, Daniel Sadoc; Rufino, Vilc Queupe; Russo, Barbara; Janes, Andrea; Ferme, Vincenzo; Hoorn, André van; Schulz, Henning

doi:10.1145/3302541.3311961

Cited by 12 publications

(5 citation statements)

References 6 publications

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“…In particular, we observed that more elaborate workloads (e.g., involving different APIs) may severely reduce the proportion of "ADC requests" in requests that violate SLO (i.e., with latency L > L SLO ). For example, when using PPTAM [54], which is an elaborated Train Ticket workload used in prior work [55], [56], we observed that only ∼19% of requests that violate SLO are assigned to an ADC. Such behavior have significant implications on the validity of our study, as our evaluation is based on the assumption that ADCs represent relevant causes of latency degradation.…”

Section: Methodsmentioning

confidence: 93%

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DeLag: Using Multi-Objective Optimization to Enhance the Detection of Latency Degradation Patterns in Service-Based Systems

Traini

Cortellessa

2023

IIEEE Trans. Software Eng.

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Performance debugging in production is a fundamental activity in modern service-based systems. The diagnosis of performance issues is often time-consuming, since it requires thorough inspection of large volumes of traces and performance indices. In this paper we present DeLag, a novel automated search-based approach for diagnosing performance issues in service-based systems. DeLag identifies subsets of requests that show, in the combination of their Remote Procedure Call execution times, symptoms of potentially relevant performance issues. We call such symptoms Latency Degradation Patterns. DeLag simultaneously searches for multiple latency degradation patterns while optimizing precision, recall and latency dissimilarity. Experimentation on 700 datasets of requests generated from two microservice-based systems shows that our approach provides better and more stable effectiveness than three state-of-the-art approaches and general purpose machine learning clustering algorithms. DeLag is more effective than all baseline techniques in at least one case study (with p ≤ 0.05 and non-negligible effect size). Moreover, DeLag outperforms in terms of efficiency the second and the third most effective baseline techniques on the largest datasets used in our evaluation (up to 22%).

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Section: Methodsmentioning

confidence: 93%

“…For the Train Ticket case study, we use PPTAM [54] as a load generator, which involves 5 different user types (we refer to our replication package [52] for details on each user type). We slightly modified PPTAM to continuously change the number of users of each type at run-time.…”

Section: Methodsmentioning

confidence: 99%

DeLag: Using Multi-Objective Optimization to Enhance the Detection of Latency Degradation Patterns in Service-Based Systems

Traini

Cortellessa

2023

IIEEE Trans. Software Eng.

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“…For our pilot study, we are utilizing a dataset of traces that was generated in a previous work of the second author [40]. The dataset was created using the widely-used microservices benchmark system TrainTicket [16,26,[44][45][46], and the load generator PPTAM [4]. Our technology stack includes the use of Jaeger [38] as the distributed tracing collector and Elasticsearch [11] for storing both common and optimized traces.…”

Section: Ongoing Workmentioning

confidence: 99%

Enhancing Trace Visualizations for Microservices Performance Analysis

Leone

Traini

2023

Companion of the 2023 ACM/SPEC International Conference on Performance Engineering

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Performance analysis of microservices can be a challenging task, as a typical request to these systems involves multiple Remote Procedure Calls (RPC) spanning across independent services and machines. Practitioners primarily rely on distributed tracing tools to closely monitor microservices performance. These tools enable practitioners to trace, collect, and visualize RPC workflows and associated events in the context of individual end-to-end requests. While effective for analyzing individual end-to-end requests, current distributed tracing visualizations often fall short in providing a comprehensive understanding of the system's overall performance. To address this limitation, we propose a novel visualization approach that enables aggregate performance analysis of multiple end-to-end requests. Our approach builds on a previously developed technique for comparing structural differences of request pairs and extends it for aggregate performance analysis of sets of requests. This paper presents our proposal and discusses our preliminary ongoing progress in developing this innovative approach. CCS CONCEPTS• Software and its engineering → Software performance; Maintaining software; Software evolution; • Human-centered computing → Visual analytics; Visualization toolkits.

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“…This approach generates a mixture of different request types that change dynamically, closely resembling real-world scenarios. We utilize Production and Performance Testing-based Application Monitoring (PPTAM) [21] as our load generator, which incorporates 5 distinct user types. We made slight modifications to PPTAM to ensure that the number of users for each request type changes continuously during runtime.…”

Section: ) Experimental Testbedmentioning

confidence: 99%

Real-Time Anomaly Detection Using Distributed Tracing in Microservice Cloud Applications

Raeiszadeh,

Ebrahimzadeh,

Saleem

et al. 2023

2023 IEEE 12th International Conference on Cloud Networking (CloudNet)

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Distributed tracing plays a vital role in microservice infrastructure, and learning-based trace analysis has been utilized to detect anomalies within such systems. However, existing approaches for learning-based trace-based anomaly detection face certain limitations. Some assume that trace patterns can be learned solely from normal executions, while others depend on anomaly injection to generate labeled traces categorized as normal or anomalous. However, in practical scenarios, anomalies may also happen during the normal execution. Moreover, a wide variety of anomalies may occur in practice, which cannot be captured solely through anomaly injection. To address these issues, we propose a Trace-Driven Anomaly Detection (TDAD) approach based on a Span Causal Graph (SCG) representation, which trains a model using a Graph Neural Network (GNN) and Positive and Unlabeled (PU) learning. This technique allows the model parameters to be optimized by estimating the underlying data distribution. As a result, TDAD can be effectively trained using a small number of labeled anomalous traces along with a relatively large number of unlabeled traces. Our evaluation reveals that TDAD outperforms not only the existing unsupervised trace-based anomaly detection methods by 11.9% in terms of F1-score but also a supervised learning-based benchmark by 12x in terms of detection time.

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Pptam

Cited by 12 publications

References 6 publications

DeLag: Using Multi-Objective Optimization to Enhance the Detection of Latency Degradation Patterns in Service-Based Systems

DeLag: Using Multi-Objective Optimization to Enhance the Detection of Latency Degradation Patterns in Service-Based Systems

Enhancing Trace Visualizations for Microservices Performance Analysis

Real-Time Anomaly Detection Using Distributed Tracing in Microservice Cloud Applications

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