Performance Evaluation Of Computer Systems Research Articles

POMACS V8, N1, March 2024 Editorial

The Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) focuses on the measurement and performance evaluation of computer systems and operates in close collaboration with the ACM Special Interest Group SIGMETRICS. All papers in this issue of POMACS will be presented at the ACM SIGMETRICS/Performance 2024 conference on June 10-14, 2024, in Venice, Italy. These papers have been selected during the Fall submission round by the 93 members of the ACM SIGMETRICS/Performance 2024 program committee via a rigorous review process. Each paper was conditionally accepted (and shepherded), allowed a "one-shot" revision (to be resubmitted to one of the subsequent two SIGMETRICS/Performance deadlines), or rejected (with re-submission allowed after a year). For this issue, which represents the Fall deadline, POMACS is publishing 18 papers out of 118 submissions, of which 6 had previously received a one-shot revision decision. All submissions received at least 3 reviews and borderline cases were extensively discussed during the online program committee meeting. Based on the indicated track(s), roughly 33% of the submissions were in the Theory track, 47% were in the Measurement & Applied Modeling track, 39% were in the Systems track, and 19% were in the Learning track (papers could be part of more than one track). Many individuals contributed to the success of this issue of POMACS. First, we would like to thank the authors, who submitted their best work to SIGMETRICS/Performance/POMACS. Second, we would like to thank the program committee members who provided constructive feedback in their reviews to authors and participated in the online discussions and program committee meeting. We also thank the several external reviewers who provided their expert opinions on specific submissions that required additional input. We are also grateful to the SIGMETRICS Board Chair, Mor Harchol-Balter, the IFIP Working Group 7.3 Chair, Mark S. Squillante, the previous SIGMETRICS Board Chair, Giuliano Casale, and the past program committee Chairs, Konstantin Avratchenkov, Phillipa Gill, and Bhuvan Urgaonkar, who provided a wealth of information and guidance. Finally, we are grateful to the Organization Committee and to the SIGMETRICS Board for their ongoing efforts and initiatives for creating an exciting program for ACM SIGMETRICS/Performance 2024.

POMACS V7, N2, June 2023 Editorial

The Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) focuses on the measurement and performance evaluation of computer systems and operates in close collaboration with the ACM Special Interest Group SIGMETRICS. All papers in this issue of POMACS will be presented at the ACM SIGMETRICS 2023 conference on June 19-23, 2023, in Orlando, Florida, USA. These papers have been selected during the winter submission round by the 91 members of the ACM SIGMETRICS 2023 program committee via a rigorous review process. Each paper was conditionally accepted (and shepherded), allowed a "one-shot" revision (to be resubmitted to one of the subsequent three SIGMETRICS deadlines), or rejected (with re-submission allowed after a year). For this issue, which represents the winter deadline, POMACS is publishing 11 papers out of 130 submissions. All submissions received at least 3 reviews and borderline cases were extensively discussed during the online program committee meeting. Based on the indicated track(s), roughly 28% of the submissions were in the Theory track, 44% were in the Measurement & Applied Modeling track, 43% were in the Systems track, and 22% were in the Learning track. Many individuals contributed to the success of this issue of POMACS. First, we would like to thank the authors, who submitted their best work to SIGMETRICS/POMACS. Second, we would like to thank the program committee members who provided constructive feedback in their reviews to authors and participated in the online discussions and program committee meeting. We also thank the several external reviewers who provided their expert opinion on specific submissions that required additional input. We are also grateful to the SIGMETRICS Board Chair, Giuliano Casale, and to past program committee Chairs, Niklas Carlsson, Edith Cohen, and Philippe Robert, who provided a wealth of information and guidance. Finally, we are grateful to the Organization Committee and to the SIGMETRICS Board for their ongoing efforts and initiatives for creating an exciting program for ACM SIGMETRICS 2023.

POMACS V7, N1, March 2023 Editorial

The Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) focuses on the measurement and performance evaluation of computer systems and operates in close collaboration with the ACM Special Interest Group SIGMETRICS. All papers in this issue of POMACS will be presented at the ACM SIGMETRICS 2023 conference on June 19-23, 2023, in Orlando, Florida, USA. These papers have been selected during the fall submission round by the 91 members of the ACM SIGMETRICS 2023 program committee via a rigorous review process. Each paper was conditionally accepted (and shepherded), allowed a "one-shot" revision (to be resubmitted to one of the subsequent three SIGMETRICS deadlines), or rejected (with re-submission allowed after a year). For this issue, which represents the fall deadline, POMACS is publishing 26 papers out of 119 submissions. All submissions received at least 3 reviews and borderline cases were extensively discussed during the online program committee meeting. Based on the indicated track(s), roughly 21% of the submissions were in the Theory track, 40% were in the Measurement & Applied Modeling track, 29% were in the Systems track, and 39% were in the Learning track. Many individuals contributed to the success of this issue of POMACS. First, we would like to thank the authors, who submitted their best work to SIGMETRICS/POMACS. Second, we would like to thank the program committee members who provided constructive feedback in their reviews to authors and participated in the online discussions and program committee meeting. We also thank the several external reviewers who provided their expert opinion on specific submissions that required additional input. We are also grateful to the SIGMETRICS Board Chair, Giuliano Casale, and to past program committee Chairs, Niklas Carlsson, Edith Cohen, and Philippe Robert, who provided a wealth of information and guidance. Finally, we are grateful to the Organization Committee and to the SIGMETRICS Board for their ongoing efforts and initiatives for creating an exciting program for ACM SIGMETRICS 2023.

POMACS V6, N3 December 2022 Editorial

The Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) focuses on the measurement and performance evaluation of computer systems and operates in close collaboration with the ACM Special Interest Group SIGMETRICS. All papers in this issue of POMACS will be presented at the ACM SIGMETRICS 2023 conference on June 19-13, 2023, in Orlando, Florida, USA. These papers have been selected by the 82 members of the ACM SIGMETRICS 2023 program committee via a rigorous review process. Each paper was conditionally accepted (and shepherded), allowed a "one-shot" revision (to be resubmitted to one of the subsequent three SIGMETRICS deadlines), or rejected (with re-submission allowed after a year). For this issue, which represents the summer deadline, POMACS is publishing 17 papers out of 93 submissions. All submissions received at least 3 reviews and borderline cases were extensively discussed during the online program committee meeting. Based on the indicated track(s), roughly 30% of the submissions were in the Theory track, 39% were in the Measurement Applied Modeling track, 33% were in the Systems track, and 29% were in the Learning track. Many individuals contributed to the success of this issue of POMACS. First, we would like to thank the authors, who submitted their best work to SIGMETRICS/POMACS. Second, we would like to thank the program committee members who provided constructive feedback in their reviews to authors and participated in the online discussions and program committee meeting. We also thank the several external reviewers who provided their expert opinion on specific submissions that required additional input. We are also grateful to the SIGMETRICS Board Chair, Giuliano Casale, and to past program committee Chairs, Niklas Carlsson, Edith Cohen, and Philippe Robert, who provided a wealth of information and guidance. Finally, we are grateful to the Organization Committee and to the SIGMETRICS Board for their ongoing efforts and initiatives for creating an exciting program for ACM SIGMETRICS 2023.

POMACS V6, N2, June 2022 Editorial

The ACM Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) focuses on the measurement and performance evaluation of computer systems and operates in close collaboration with the ACM Special Interest Group SIGMETRICS. All papers in this issue of POMACS will be presented during the ACM SIGMETRICS/Performance 2022 conference. The issue contains papers selected by the editorial board via a rigorous review process that follows a hybrid conference and journal model, with reviews conducted by the 101 members of our POMACS editorial board. Each paper was either conditionally accepted (and shepherded), allowed a "one-shot" revision (to be resubmitted to one of the subsequent two deadlines), or rejected (with resubmission allowed after a year). For this issue, which represents the winter deadline, we accepted 17 papers out of 126 submissions (including 4 papers that had been given a "one-shot" revision opportunity). All submitted papers received at least 3 reviews and we held an online TPC meeting. Based on the indicated primary track, roughly 31% of the submissions were in the Measurement & Applied Modeling track, 25% were in the Systems track, 23% were in the Theory track, and 21% were in the Learning track. Many people contributed to the success of this issue of POMACS. First, we would like to thank the authors, who submitted their work to SIGMETRICS/POMACS. Second, we would like to thank the TPC members for their work: constructive feedback in their reviews to authors, participation to online discussions and also to TPC meetings. We also thank several external reviewers who provided their expert opinion on specific submissions that required additional input. We are also grateful to the SIGMETRICS Board Chair, Giuliano Casale, and to past TPC Chairs. Finally, we are grateful to the Organization Committee and to the SIGMETRICS Board for their ongoing efforts and initiatives for creating an exciting program for ACM SIGMETRICS/Performance 2022.

POMACS V6, N1, March 2022 Editorial

The ACM Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) focuses on the measurement and performance evaluation of computer systems and operates in close collaboration with the ACM Special Interest Group SIGMETRICS. All papers in this issue of POMACS will be presented during the ACM SIGMETRICS/Performance 2022 conference. The issue contains papers selected by the editorial board via a rigorous review process that follows a hybrid conference and journal model, with reviews conducted by the 97 members of our POMACS editorial board. Each paper was either conditionally accepted (and shepherded), allowed a "one-shot" revision (to be resubmitted to one of the subsequent two deadlines), or rejected (with resubmission allowed after a year). For this issue, which represents the fall deadline, we accepted 25 papers out of 106 submissions (including 4 papers that had been given a "one-shot" revision opportunity). All submitted papers received at least 3 reviews and we held an online TPC meeting. Based on the indicated primary track, roughly 30% of the submissions were in the Measurement & Applied Modeling track, 28% were in the Systems track, 26% were in the Theory track, and 15% were in the Learning track. Many people contributed to the success of this issue of POMACS. First, we would like to thank the authors, who submitted their best work to SIGMETRICS/POMACS. Second, we would like to thank the TPC members who provided constructive feedback in their reviews to authors and participated in the online discussions and TPC meetings. We also thank several external reviewers who provided their expert opinion on specific submissions that required additional input. We are also grateful to the SIGMETRICS Board Chair, Giuliano Casale, and to past TPC Chairs. Finally, we are grateful to the Organization Committee and to the SIGMETRICS Board for their ongoing efforts and initiatives for creating an exciting program for ACM SIGMETRICS/Performance 2022.

BenchSubset: A framework for selecting benchmark subsets based on consensus clustering

The redundancy in the benchmark suite will increase the time for computer system performance evaluation and simulation. The most typical method to solve this problem is to select subsets based on clustering. However, it is a challenge to validate benchmark subsetting results for unlabeled benchmark suites when using the clustering method, and existing research has not considered this problem. Also, there is no quantitative evaluation method for subsetting which can reflect the universal and the diversity characteristics of the benchmark suite at the same time. To solve the above problems, we propose BenchSubset, a framework for selecting benchmark subsets based on consensus clustering, which includes Group Principal Components Analysis, consensus clustering, and a new evaluation method considering the universal and the diversity characteristics of the benchmark suite. We conducted SPEC CPU2017 subsetting experiments on Huawei's Taishan 200, then verified the effectiveness of BenchSubset in selecting a benchmark subset. Compared with the mainstream principal components analysis with hierarchical clustering (PCA-H) method, the benchmark subset selected by BenchSubset performs better in representing the universal and the diversity characteristics of SPEC CPU2017.

POMACS V5, N3, December 2021 Editorial

The ACM Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) focuses on the measurement and performance evaluation of computer systems and operates in close collaboration with the ACM Special Interest Group SIGMETRICS. All papers in this issue of POMACS will be presented during the ACM SIGMETRICS/Performance 2022 conference. The issue contains papers selected by the editorial board via a rigorous review process that follows a hybrid conference and journal model, with reviews conducted by the 93 members of our POMACS editorial board. Each paper was either conditionally accepted (and shepherded), allowed a "one-shot" revision (to be resubmitted to one of the subsequent two deadlines), or rejected (with resubmission allowed after a year). For this issue, which represents the summer deadline, POMACS publishes 17 papers out of 71 submissions. All submitted papers received at least 3 reviews and we held an online TPC meeting. Based on the indicated primary track, roughly 37% of the submissions were in the Theory track, 30% were in the Measurement & Applied Modeling track, 20% were in the Systems track, and 14% were in the Learning track. Many people contributed to the success of this issue of POMACS. First, we would like to thank the authors, who submitted their best work to SIGMETRICS/POMACS. Second, we would like to thank the TPC members who provided constructive feedback in their reviews to authors and participated in the online discussions and TPC meetings. We also thank the several external reviewers who provided their expert opinion on specific submissions that required additional input. We are also grateful to the SIGMETRICS Board Chair, Giuliano Casale, and to past TPC Chairs, Anshul Gandhi, Negar Kiyavash, and Jia Wang, who provided a wealth of information and guidance (including a template for writing this editorial note!). Finally, we are grateful to the Organization Committee and to the SIGMETRICS Board for their ongoing efforts and initiatives for creating an exciting program for ACM SIGMETRICS/Performance 2022.

Editorial

The ACM Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) focuses on the measurement and performance evaluation of computer systems and operates in close collaboration with the ACM Special Interest Group SIGMETRICS. All papers in the last three (i.e., this one and the previous two) issues of POMACS will be presented during the ACM SIGMETRICS 2021 virtual conference. Each of the three issues (one per call for papers) contains papers selected by the editorial board via a rigorous review process that follows a hybrid conference and journal model, with reviews conducted by the 86 members of our POMACS editorial board. Each paper was either conditionally accepted (and shepherded), allowed a "one-shot" revision (to be resubmitted to one of the subsequent two deadlines), or rejected (with resubmission allowed after a year). Over the three issues during which we have served as editors, POMACS has published 35 papers out of 315 submissions (84 to the Summer call, 107 to the Fall call, and 124 to the Winter call). All submitted papers received at least 3 reviews and we held an online TPC meeting for each deadline/issue. Based on the indicated primary track, roughly 16% of the submissions were in the Learning track, 29% were in the Measurement & Applied Modeling track, 26% were in the Systems track, and 29% were in the Theory track. Many people contributed to the success of POMACS over the last year. First, we would like to thank the authors, who submitted their best work to SIGMETRICS/POMACS. Second, we would like to thank the TPC members who provided constructive feedback in their reviews to authors and participated in the online discussions and TPC meetings. We also thank the several external reviewers who provided their expert opinion on specific submissions that required additional input. We are also grateful to the SIGMETRICS Board Chair, Giuliano Casale, and to past TPC Chairs, Athina Markopoulou and Y. C. Tay, who provided a wealth of information and guidance. We also thank Adam Wierman for outlining the soft tracking guidelines that were adopted for SIGMETRICS 2021. Finally, we are grateful to the Organization Committee and to the SIGMETRICS Board for their efforts and initiatives in the middle of a pandemic, including making papers and videos available online and enabling online interactions.

Machine Learning Data Center Workloads Using Generative Adversarial Networks

In this paper we study the applicability of generative adversarial networks (GANs) for the description and generation of workloads for data centers. GANs are advanced neural networks that can learn complex likelihood functions and can sample from them. The field of workload modeling is concerned with describing and generating realistic workloads for performance evaluation of computer systems, in this paper, specifically for data centers. The characterization of the workload of modern data centers is crucial in order to study the effect of changing workloads on the performance of such data centers. Previously, a number of statistical fitting techniques have been used to characterize data center workloads. This paper explores whether GANs are sufficiently capable to automatically learn such characterisations from multidimensional data sets. We describe the design and evaluation of a GAN, thereby using real-world data center traces. The learned model is evaluated by comparison to previously proposed fitting techniques. We find that the resulting GAN is very well able to reproduce a realistic data center workload. Furthermore, the approach does not require (a priori) knowledge or assumptions about the underlying models themselves, which can be seen as an advantage. It is shown that the learning approach does reach comparable quality to other fitting techniques, although still at much higher computational costs.

What probability distribution describes search?

Typically, search research papers assume that target acquisition is described by an exponential distribution. We investigate when this assumption is valid. It is obvious that two people are more effective than one person at finding a target, but how can that be quantified? The network imaging sensor (NIS) and time-dependent search parameter (TDSP) models quantify how much more effective multiple observers are at finding a target than a single individual for a wide variety of scenarios. We reference and summarize evidence supporting the NIS and TDSP models and demonstrate how NIS model results can be expressed in terms of a reduced hyperexponential distribution for scenarios where observer and target are stationary. Target acquisition probabilities are determined by analysis and confirmed by computer simulations and perception experiments. Search by multiple stationary observers looking for a stationary target is described by the hyperexponential distribution. Stationary scenarios with multiple observers are more accurately modeled by hyperexponential rather than exponential distributions. Hyperexponential distributions are an example of phase-type distributions used in queuing and in the performance evaluation of computer networks and systems. The observation that search, queuing, and computer networks share phase-type distributions facilitates cross fertilization between these fields.

Editorial

The Proceedings of the ACM series present the highest quality research conducted in diverse areas of computer science, as represented by the ACM Special Interest Groups (SIGs). The ACM Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) focuses on the measurement and performance evaluation of computer systems and operates in close collaboration with the Special Interest Group SIGMETRICS. All the papers in the last three issues of POMACS will be presented during the joint ACM SIGMETRICS / IFIP Performance 2019 conference. Despite the extraordinary progress of networks and computer systems in the last decades, these are far from perfect. There are still performance issues and security threats, and the complexity of these systems make them often difficult to manage and operate. Research is expected to contribute to the further improvements of these systems, to make them more efficient, more robust, more flexible and more predictable. In the era of global warming, there is also an urgent need to make the digital economy more green, and thus beneficial to all in the long term. The papers that have appeared in POMACS during the past year reflect the ability of the journal to attract high-quality submissions in this area. The last three issues have included papers on mainstream topics of the ACM SIGMETRICS / IFIP Performance communities, including load balancing, scheduling, resource allocation, performance evaluation and measurement, as well as emerging topics that have become prominent in recent years, such as virtualization, network science, and machine learning. As part of a new initiative to highlight work from outside the SIGMETRICS community that may spur research within the community, the 2019 conference will include a Highlights Beyond SIGMETRICS session featuring leading papers from conferences in neighboring disciplines. We thank Adam Wierman for leading this initiative.

Message from the Editors

This issue marks the completion of the first year of the Proceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS). POMACS was among the first three journals joining the recently launched Proceedings of the ACM (PACM) series, and with this issue POMACS has now published over 80 papers. The goal of the PACM series is to showcase the highest quality research conducted in diverse areas of computer science, as represented by the ACM Special Interest Groups (SIGs). ACM POMACS focuses on the computer systems measurement and performance evaluation community and operates in close collaboration with the Special Interest Group SIGMETRICS. In fact, all the papers in the last three issues of POMACS will be presented during the SIGMETRICS annual conference this summer.

Evaluation Criteria for Reliability in Computer Systems

Numerical methods, particularly finite element methods, are widely used in solving different problems. Since these methods are approximate, having a real understanding of the distribution of errors is extremely important. With the increasing number of users, the number of cause dfailuresin creases by their fault. In this article we will discuss per formance evaluation system, the performance evaluation of computer and communication systems for quality research that is finding its profit goals for the number of ways to predict the behavior of the system. One of the main parameters in determining the performance evaluation is reliability and because some complex systems cannot be easily modeled by hybrid methods (RBD), we use Markov method.

Performance Evaluation of Space Division Output Buffered Switches

Discrete-time multiserver queues have been successfully used in the performance evaluation of computer and communication systems in various contexts such as TDM, voice-data integration, deflection routing and more recently, ATM-switching technology. In digital communication systems, buffers are used for the temporary storage of digital information awaiting transmission via some communication channel(s). Usually, information presents itself in the form of fixed-size packets, and synchronous (slotted) transmission is used so that the buffers can be modelled as discrete-time queuing systems. In this article, performance analysis of space division output buffered switches operating in an ATM multimedia environment is presented. Fixed size packets arrive onto the switch inputs in each time slot. The switch is modelled as a discrete-time, batch arrival, multiserver queuing system, with infinite buffer and geometric service times. The main contribution of this work is using multiserver case and geometric service time. Many performance measures such as System occupancy, Queuing time, Unfinished work and Waiting time are analysed. Probability Generating Functions (PGFs) and expectations for the corresponding random variables are derived. The results of the analysis have been verified in many ways. First, they have been used to generate the results of some previous analyses as special cases. Second, they obviously seem to preserve classical queuing relations (e.g. the famous Little's formula). Last, they have been shown to generate intuitively acceptable graphs, when translated into numerical values.

Introduction to the theme issue on performance modeling

The increasing complexity of modern computer systems demands new and efficient performance modeling techniques to aid developers in producing the most efficient system for a desired task. Performance evaluation seeks to understand and explain system performance. It should be an integral component of the development process from the beginning to reduce the costs required to fix problems late in the development cycle. Indeed, decisions made early in the process will directly impact the quality of the final product. The model-based development paradigm, in contrast, advocates developing systems starting from models—at multiple levels of abstraction—that express domain-specific concepts precisely and intuitively while supporting automated manipulation and transformation. The goal of this theme issue is to illuminate the deep relationship between computer systems performance evaluation and system modeling. The papers presented in this issue help to answer such questions as—How can model-driven techniques that are used to understand a system’s behavior be used to improve its performance? Can modeling languages and tools be adapted for improving performance? What are the connections between modeling tool environments and performance evaluation tools and environments? The papers in this theme issue are a step towards bridging the gap between the theory and practice of performance evaluation and system modeling.

Special Issue on ‘Performance Evaluation, Modeling and Optimization of Computer Systems’

This Special Issue of SIMULATION: Transactions of The Society for Modeling and Simulation International is dedicated to the performance evaluation, modeling and optimization of computer systems. The aim of this special issue is to publish novel research work that contributes to the development of methodologies, techniques and tools, as well as their application in state-of-the-art computer systems. Some of the papers included were submitted specially for this issue, while others are extended versions of a selection of the best papers accepted and presented at the 2009 International Symposium on Performance Evaluation of Computer and Telecommunication Systems, SPECTS 2009. These papers have undergone a new round of reviews to guarantee that all of the papers included in this Special Issue have been rigorously peer-reviewed. The five articles of this Special Issue cover a variety of topics, including: performance evaluation of scheduling algorithms in clusters and distributed systems; reconstruction of load properties from observable traffic; modeling of workflows in heterogeneous distributed systems; and simulation of the performance of Flash memories. This wide range of topics reflects the many facets of this field, which has significantly progressed in the last years. The first paper, ‘Gang Scheduling in a two-cluster system implementing migrations and periodic feedback’, is authored by Zafeirios C Papazachos and Helen D Karatza. They consider scheduling gangs in a homogeneous multicluster system, in the presence of critical sporadic jobs that require immediate execution upon their arrival. Several scheduling algorithms, responsible for allocating the available system resources to the existing jobs, are examined, and their performance is studied using a simulation model. This study includes the effect of migrating tasks from a local queue to the head of another queue, both locally and through the grid. In addition, the paper studies the performance of a grid dispatching algorithm which allocates gangs to the available clusters based on periodic feedback from the clusters. In the second paper, ‘Reconstructing arrival processes to discrete queueing systems by inverse load transformation’, by Stephan Heckmuller and Bernd E Wolfinger, the problem of recovering arrival processes to discrete G/D/1 queuing systems from observable traffic is studied. Independent normally distributed arrivals and autoregressive processes are considered, which are commonly used as a model for highly aggregated traffic such as that of computer networks. Closed-form expressions are obtained for the normal distribution, while for the case of autoregressive processes, a novel use of the Tobit regression model (commonly used in social and economic sciences) allows estimates of the process parameters to be obtained with high accuracy. The estimation procedures are generalized to distributions other than the normal distribution by using the Buckley–James estimator. Finally, the authors demonstrate through simulation that the proposed methods can be used in realistic scenarios such as polling mechanisms in wireless local area networks. In the third paper, ‘Modeling and simulation of distributed computing workflows in heterogeneous network environments’, Qishi Wu and Yi Gu propose a simulation system to study the execution dynamics of distributed computing workflows. The simulator, named SDEDS (Simulation of Dynamic Execution of Distributed Systems), is designed for the performance evaluation of large-scale workflow systems, and it is implemented using multi-threaded programming and an event-driven control structure. It simulates the dynamic execution process of distributed systems with data execution on computer nodes and data transfer along network links, and evaluates the network performance of workflow scheduling or mapping algorithms. Experimental observations collected in real networks and theoretical analysis are used to confirm the simulation-based performance measurements. The fourth paper, ‘An effective duplication-based taskscheduling algorithm for heterogeneous systems’, is authored by Mahsa Hosseinzadeh and Hadi Shahriar Shahhoseini. They present a novel task scheduling

Contributors

Contributors

Guest Editorial: Performance Evaluation of Computer and Telecommunication Systems

Welcome to this special issue of Simulation: Transactions of the Society for Modeling and Simulation International on ‘Performance Evaluation of Computer and Telecommunication Systems’. The papers included in this special issue are based mainly on selected extended versions of best papers accepted in the 2008 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS 2008). We have only accepted four papers in this special issue. This means that these accepted papers have undergone a thorough and critical review process by experts in the field. Heckmuller and Wolfinger proposed an analytical model which reflects in detail the load transforming impact of a token bucket mechanism on a given (primary) load. By assuming that the arrival process of requests and tokens to the token bucket device follows a Markovian process the authors were able to specify the transformed load. The authors presented a tool which allows them to transform the load being specified as a BMAP process. This extensible tool already comprises components to realistically reflect various practically relevant transformation functionalities which are very common in current computer networks, such as fragmentation, header generation, and token/leaky bucket regulation, among others. Furthermore, they validated the proposed model by comparing the properties of the generated load models to those of simulatively transformed load models. It was found that the generated load models were able to capture the desired characteristics of the departure process with high accuracy even though, in the simulation experiments the tokens were assumed to arrive in a deterministic fashion. Possible applications of the proposed procedure in

JMT

We present the Java Modelling Tools (JMT) suite, an integrated framework of Java tools for performance evaluation of computer systems using queueing models. The suite offers a rich user interface that simplifies the definition of performance models by means of wizard dialogs and of a graphical design workspace. The performance evaluation features of JMT span a wide range of state-of-the-art methodologies including discrete-event simulation, mean value analysis of product-form networks, analytical identification of bottleneck resources in multiclass environments, and workload characterization with fuzzy clustering. The discrete-event simulator supports several advanced modeling features such as finite capacity regions, load-dependent service times, bursty processes, fork-and-join nodes, and implements spectral estimation for analysis of simulative results. The suite is open-source, released under the GNU general public license (GPL), and it is available for free download at: http://jmt.sourceforge.net.