Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data

Resource demand estimation is essential for the application of analyical models, such as queueing networks, to real-world systems. In this paper, we investigate maximum likelihood (ML) estimators for service demands in closed queueing networks with load-independent and load-dependent service times. Stemming from a characterization of necessary conditions for ML estimation, we propose new estimators that infer demands from queue-length measurements, which are inexpensive metrics to collect in real systems. One advantage of focusing on queue-length data compared to response times or utilizations is that confidence intervals can be rigorously derived from the equilibrium distribution of the queueing network model. Our estimators and their confidence intervals are validated against simulation and real system measurements for a multi-tier application

Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data

Resource demand estimation is essential for the application of analyical models, such as queueing networks, to real-world systems. In this paper, we investigate maximum likelihood (ML) estimators for service demands in closed queueing networks with load-independent and load-dependent service times. Stemming from a characterization of necessary conditions for ML estimation, we propose new estimators that infer demands from queue-length measurements, which are inexpensive metrics to collect in real systems. One advantage of focusing on queue-length data compared to response times or utilizations is that confidence intervals can be rigorously derived from the equilibrium distribution of the queueing network model. Our estimators and their confidence intervals are validated against simulation and real system measurements for a multi-tier application

Empirical Validation of MoDe4SLA; Approach for Managing Service Compositions

For companies managing complex Web service compositions, challenges arise which go far beyond simple bilateral contract monitoring. For example, it is not only important to determine whether or not a component (i.e., Web service) in a composition is performing properly, but also to understand what the impact of its performance is on the overall service composition. To tackle this challenge, in previous work we developed MoDe4SLA which allows managing and monitoring dependencies between services in a composition. This paper empirically validates MoDe4SLA through an extensive and interactive experiment among 34 participants

Benchmarking models and tools for distributed Web-server systems

This tutorial reviews benchmarking tools and techniques that can be used to evaluate the performance and scalability of highly accessed Web-server systems. The focus is on design and testing of locally and geographically distributed architectures where the performance evaluation is obtained through workload generators and analyzers in a laboratory environment. The tutorial identifies the qualities and issues of existing tools with respect to the main features that characterize a benchmarking tool (workload representation, load generation, data collection, output analysis and report) and their applicability to the analysis of distributed Web-server systems

Multicriteria evaluation-based framework for composite web service selection

International audienceThe present paper seeks to propose a general framework to composite Web services selection. The proposed framework extends the conventional Web services architecture by adding a new component in the registry devoted to multicriteria classification of compositions into different ordered Quality of Service (QoS) classes. This additional component takes as input the specification of the desired service, a set of functional and non-functional evaluation criteria, a set of QoS-ordered classes, and a set of preference parameters, and generates as output a classification of composite Web services into different QoS-ordered classes. In addition to the description of the proposed framework, the paper proposes solutions to construct, evaluate and classify compositeWeb services. The paper also briefly presents the developed prototype and then illustrates and discusses some computational aspects of the proposed framework using numerical data

A Performance Comparison of QoS-Driven Service Selection Approaches

Service selection has been widely investigated as an effective adaptation mechanism that allows a service broker, offering a composite service, to bind each task of the abstract composition to a corresponding implementation, selecting it from a set of candidates. The selection aims typically to fulfill the Quality of Service (QoS) requirements of the composite service, considering several QoS parameters in the decision. We compare the performance of two representative examples of the per-request and per-flow approaches that address the service selection issue at a different granularity level. We present experimental results obtained with a prototype implementation of a service broker. Our results show the ability of the per-flow approach in sustaining an increasing traffic of requests, while the per-request approach appears more suitable to offer a finer customizable service selection in a lightly loaded system