iModel

Deriving analytic performance models requires detailed knowledge of the architecture and behavior of the computer system being modeled as well as modeling skills. This detailed knowledge may not be readily available (or it may be impractical to gather) given the dynamic nature of production computing environments. This article presents a framework, called iModel , for automatically deriving and parameterizing analytic performance models for multi-tiered computer systems. Analytic performance models consist of a workload model and a system model. iModel uses system logs and configuration files to generate a high-level characterization of the system; e.g., open queuing network (QN) model versus closed QN model. By harvesting more information from the system logs and configuration files, iModel generates a workload model by inferring user-system interaction patterns in the form of a Customer Behavior Model Graph (CBMG) and generates a system model by discovering system components and their interaction patterns in the form of a Client-Server Interaction Diagram (CSID). iModel includes a library of well-known single-queue and QN models and their solutions stored in an XML-based repository. The generated workload model and system model are compared to the model repository to determine which model in the repository best matches the system’s observable behavior and architecture. This article also presents a black-box optimization approach that is used to derive analytic model parameters by observing the input-output relationships of a real system. This optimization approach can be used in any computer system (multi-tier or not) that can be modeled by single queues or QNs. The important question is whether the automatically generated and parameterized performance model has predictive power, i.e., can the derived model predict the output values that would be observed in the real system for different values of the input? The results presented in this article demonstrate that the analytic performance models derived by iModel are relatively robust and have predictive power over a wide range of input values.

Factory optima: a web-based system for composition and analysis of manufacturing service networks based on a reusable model repository

This paper reports on the development of Factory Optima, a web-based system that allows manufacturing process engineers to compose, optimise and perform trade-off analysis of manufacturing and contract service networks based on a reusable repository of performance models. Performance models formally describe process feasibility constraints and metrics of interest, such as cost, throughput and CO ₂ emissions, as a function of fixed and control parameters, such as equipment and contract properties and settings. The repository contains performance models representing (1) unit manufacturing processes, (2) base contract services and (3) a composite steady-state service network. The proposed framework allows process engineers to hierarchically compose model instances of service networks, which can represent production cells, lines, factory facilities and supply chains, and perform deterministic optimisation based on mathematical programming and Pareto-optimal trade-off analysis. Factory Optima is demonstrated using a case study of a service network for a heat sink product which involves contract vendors and manufacturing activities, including cutting, shearing, Computer Numerical Control (CNC) machining with milling and drilling operations, quality inspection, finishing, and assembly.

Probabilistic scalable P2P resource location services

Scalable resource discovery services form the core of directory and other middleware services. Scalability requirements preclude centralized solutions. The need to have directory services that are highly robust and that can scale with the number of resources and the performance of individual nodes, points to Peer-to-Peer (P2P) architectures as a promising approach. The resource location problem can be simply stated as "given a resource name, find the location of a node or nodes that manage the resource." We call this the deterministic location problem. In a very large network, it is clearly not feasible to contact all nodes to locate a resource. Therefore, we modify the problem statement to "given a resource name, find with a given probability, the location of a node or nodes that manage the resource." We call this a probabilistic location approach. We present a protocol that solves this problem and develop an analytical model to compute the probability that a directory entry is found, the fraction of peers involved in a search, and the average number of hops required to find a directory entry. Numerical results clearly show that the proposed approach achieves high probability of finding the entry while involving a relatively small fraction of the total number of peers. The analytical results are further validated by results obtained from an implementation of the proposed protocol in a cluster of workstations.