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

Building a distributed infrastructure for scalable triple stores

Built specifically for the Semantic Web, triple stores are required to accommodate a large number of RDF triples and remain primarily centralized. As triple stores grow and evolve with time, there is a demanding need for scalable techniques to remove resource and performance bottlenecks in such systems. To this end, we propose a fully decentralized peer-to-peer architecture for large scale triple stores in which triples are maintained by individual stakeholders, and a semantics-directed search protocol, mediated by topology reorganization, for locating triples of interest. We test our design through simulations and results show anticipated improvements over existing techniques for distributed triple stores. In addition to engineering future large scale triple stores, our work will in particular benefit the federation of stand-alone triple stores of today to achieve desired scalability

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