Configuration of Distributed Message Converter Systems using Performance Modeling

To find a configuration of a distributed system satisfying performance goals is a complex search problem that involves many design parameters, like hardware selection, job distribution and process configuration. Performance models are a powerful tools to analyse potential system configurations, however, their evaluation is expensive, such that only a limited number of possible configurations can be evaluated. In this paper we present a systematic method to find a satisfactory configuration with feasible effort, based on a two-step approach. First, using performance estimates a hardware configuration is determined and then the software configuration is incrementally optimized evaluating Layered Queueing Network models. We applied this method to the design of performant EDI converter systems in the financial domain, where increasing message volumes need to be handled due to the increasing importance of B2B interaction

Computationally Efficient Simulation of Queues: The R Package queuecomputer

Large networks of queueing systems model important real-world systems such as MapReduce clusters, web-servers, hospitals, call centers and airport passenger terminals. To model such systems accurately, we must infer queueing parameters from data. Unfortunately, for many queueing networks there is no clear way to proceed with parameter inference from data. Approximate Bayesian computation could offer a straightforward way to infer parameters for such networks if we could simulate data quickly enough. We present a computationally efficient method for simulating from a very general set of queueing networks with the R package queuecomputer. Remarkable speedups of more than 2 orders of magnitude are observed relative to the popular DES packages simmer and simpy. We replicate output from these packages to validate the package. The package is modular and integrates well with the popular R package dplyr. Complex queueing networks with tandem, parallel and fork/join topologies can easily be built with these two packages together. We show how to use this package with two examples: a call center and an airport terminal.Comment: Updated for queuecomputer_0.8.

Parallel, iterative solution of sparse linear systems: Models and architectures

A model of a general class of asynchronous, iterative solution methods for linear systems is developed. In the model, the system is solved by creating several cooperating tasks that each compute a portion of the solution vector. A data transfer model predicting both the probability that data must be transferred between two tasks and the amount of data to be transferred is presented. This model is used to derive an execution time model for predicting parallel execution time and an optimal number of tasks given the dimension and sparsity of the coefficient matrix and the costs of computation, synchronization, and communication. The suitability of different parallel architectures for solving randomly sparse linear systems is discussed. Based on the complexity of task scheduling, one parallel architecture, based on a broadcast bus, is presented and analyzed

Run Time Approximation of Non-blocking Service Rates for Streaming Systems

Stream processing is a compute paradigm that promises safe and efficient parallelism. Modern big-data problems are often well suited for stream processing's throughput-oriented nature. Realization of efficient stream processing requires monitoring and optimization of multiple communications links. Most techniques to optimize these links use queueing network models or network flow models, which require some idea of the actual execution rate of each independent compute kernel within the system. What we want to know is how fast can each kernel process data independent of other communicating kernels. This is known as the "service rate" of the kernel within the queueing literature. Current approaches to divining service rates are static. Modern workloads, however, are often dynamic. Shared cloud systems also present applications with highly dynamic execution environments (multiple users, hardware migration, etc.). It is therefore desirable to continuously re-tune an application during run time (online) in response to changing conditions. Our approach enables online service rate monitoring under most conditions, obviating the need for reliance on steady state predictions for what are probably non-steady state phenomena. First, some of the difficulties associated with online service rate determination are examined. Second, the algorithm to approximate the online non-blocking service rate is described. Lastly, the algorithm is implemented within the open source RaftLib framework for validation using a simple microbenchmark as well as two full streaming applications.Comment: technical repor

The cost of conservative synchronization in parallel discrete event simulations

The performance of a synchronous conservative parallel discrete-event simulation protocol is analyzed. The class of simulation models considered is oriented around a physical domain and possesses a limited ability to predict future behavior. A stochastic model is used to show that as the volume of simulation activity in the model increases relative to a fixed architecture, the complexity of the average per-event overhead due to synchronization, event list manipulation, lookahead calculations, and processor idle time approach the complexity of the average per-event overhead of a serial simulation. The method is therefore within a constant factor of optimal. The analysis demonstrates that on large problems--those for which parallel processing is ideally suited--there is often enough parallel workload so that processors are not usually idle. The viability of the method is also demonstrated empirically, showing how good performance is achieved on large problems using a thirty-two node Intel iPSC/2 distributed memory multiprocessor

Methods for Performance Evaluation of Parallel Computer Systems

Although parallel computers have existed for many years, recently there has been a surge of academic, industrial and governmental interest in parallel computing. Commercially manufactured parallel computers have started to become available. Many new experimental parallel architectures are reported in the literature every year. Software for many types of applications, from scientific number crunching to artificial intelligence, is being written to run on parallel machines. Performance is an essential consideration both in the design of new systems and the deployment of existing systems. Users of computers wish to utilize their hardware and software systems as efficiently as possible. Over the years, a field known as computer performance evaluation has arisen to address the problem of quantifying and predicting computer performance. Methods exist that can determine how efficiently a system's resources are being used. These can help track down the probable causes of performance problems

Computer architecture evaluation for structural dynamics computations: Project summary

The intent of the proposed effort is the examination of the impact of the elements of parallel architectures on the performance realized in a parallel computation. To this end, three major projects are developed: a language for the expression of high level parallelism, a statistical technique for the synthesis of multicomputer interconnection networks based upon performance prediction, and a queueing model for the analysis of shared memory hierarchies

Methodology for modeling high performance distributed and parallel systems

Performance modeling of distributed and parallel systems is of considerable importance to the high performance computing community. To achieve high performance, proper task or process assignment and data or file allocation among processing sites is essential. This dissertation describes an elegant approach to model distributed and parallel systems, which combines the optimal static solutions for data allocation with dynamic policies for task assignment. A performance-efficient system model is developed using analytical tools and techniques. The system model is accomplished in three steps. First, the basic client-server model which allows only data transfer is evaluated. A prediction and evaluation method is developed to examine the system behavior and estimate performance measures. The method is based on known product form queueing networks. The next step extends the model so that each site of the system behaves as both client and server. A data-allocation strategy is designed at this stage which optimally assigns the data to the processing sites. The strategy is based on flow deviation technique in queueing models. The third stage considers process-migration policies. A novel on-line adaptive load-balancing algorithm is proposed which dynamically migrates processes and transfers data among different sites to minimize the job execution cost. The gradient-descent rule is used to optimize the cost function, which expresses the cost of process execution at different processing sites. The accuracy of the prediction method and the effectiveness of the analytical techniques is established by the simulations. The modeling procedure described here is general and applicable to any message-passing distributed and parallel system. The proposed techniques and tools can be easily utilized in other related areas such as networking and operating systems. This work contributes significantly towards the design of distributed and parallel systems where performance is critical