Statistical Approaches to Predicting and Diagnosing Performance Problems in Component-Based Distributed Systems: An Experimental Evaluation

Corrêa, Sand Luz; Cerqueira, Renato

doi:10.1109/saso.2010.32

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…In [6], statistical approaches are applied to characterize performance problems in middle-ware-based systems. Monte Carlo methods and simulation are used in many fields such as well drilling [5], project scheduling [18], and finance [9] to assess risk.…”

Section: Related Workmentioning

confidence: 99%

Dependable Risk-Aware Efficiency Improvement for Self-Organizing Emergent Systems

Hudson

Denzinger

Kasinger

et al. 2011

2011 IEEE Fifth International Conference on Self-Adaptive and Self-Organizing Systems

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An efficiency improvement advisor agent acts as a consultation service for a self-organizing multi-agent system that improves operational efficiency. It identifies recurrent tasks in past problems that allow the creation of so-called exception rules for individual agents to limit future inefficient behavior. There exists the danger that introduced rules could possibly infringe on the flexibility and therefore reliability of the system. In this paper, we present a dependable riskaware efficiency improvement advisor that uses Monte Carlo simulation techniques in strategic analysis assessing the longterm potential and risks of prospective rules. Our experimental evaluation, for the domain of dynamic pickup and delivery problems, shows that the result is a minimal, yet effective, set of risk-averse exception rules. These rules can be provided to individual agents to reliably achieve an overall long-term improvement in efficiency while maintaining flexibility.

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Section: Related Workmentioning

confidence: 99%

Dependable Risk-Aware Efficiency Improvement for Self-Organizing Emergent Systems

Hudson

Denzinger

Kasinger

et al. 2011

2011 IEEE Fifth International Conference on Self-Adaptive and Self-Organizing Systems

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show abstract

“…Building on these works, we integrate priorities into our multi-level load balancer. Another popular approach to SLOaware resource provisioning is predictive modeling [10,51]. Given the bursty and at times unpredictable IoT traffic [29,63], the effectiveness of these models in the IoT space is unclear.…”

Section: Related Workmentioning

confidence: 99%

MMLite

Nagendra

Bhattacharya

Gandhi

et al. 2019

Proceedings of the 2019 ACM Symposium on SDN Research

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With increase in cellular-enabled IoT devices having diverse traffic characteristics and service level objectives (SLOs), handling the control traffic in a scalable and resource-efficient manner in the cellular packet core network is critical. The traditional monolithic design of the cellular core adopted by service-providers is inflexible with respect to the diverse requirements and bursty loads of IoT devices, specifically for properties such as elasticity, customizability, and scalability. To address this key challenge, we focus on the most critical control plane component of the cellular packet core network, the Mobility Management Entity (MME). We present MMLite, a functionally decomposed and stateless MME design wherein individual control procedures are implemented as microservices and states are decoupled from their processing, thus enabling elasticity and fault tolerance. For SLO compliance, we develop a multi-level load balancing approach based on skewed consistent hashing to efficiently distribute incoming connections. We evaluate the performance benefits of MMLite over existing approaches with respect to scaling, fault tolerance, SLO compliance and resource efficiency.

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“…Correa and Cerqueira [31] use statistical approaches to predict and diagnose performance problems in component-based distributed systems. For their technique, they compare decision tree, Bayesian network and support vector machine approaches for classifying.…”

Section: Related Workmentioning

confidence: 99%

Performance optimization of deployed software-as-a-service applications

Bezemer

Zaidman

2014

Journal of Systems and Software

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The goal of performance maintenance is to improve the performance of a software system after delivery. As the performance of a system is often characterized by unexpected combinations of metric values, manual analysis of performance is hard in complex systems. In this paper, we propose an approach that helps performance experts locate and analyze spots -so called performance improvement opportunities (PIOs) -, for possible performance improvements. PIOs give performance experts a starting point for performance improvements, e.g., by pinpointing the bottleneck component. The technique uses a combination of association rules and performance counters to generate the rule coverage matrix, a matrix which assists with the bottleneck detection.In this paper, we evaluate our technique in two cases studies. In the first, we show that our technique is accurate in detecting the timeframe during which a PIO occurs. In the second, we show that the starting point given by our approach is indeed useful and assists a performance expert in diagnosing the bottleneck component in a system with high precision.

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Statistical Approaches to Predicting and Diagnosing Performance Problems in Component-Based Distributed Systems: An Experimental Evaluation

Cited by 5 publications

References 22 publications

Dependable Risk-Aware Efficiency Improvement for Self-Organizing Emergent Systems

Dependable Risk-Aware Efficiency Improvement for Self-Organizing Emergent Systems

MMLite

Performance optimization of deployed software-as-a-service applications

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