Parsec: a parallel simulation environment for complex systems

Bagrodia, Rajive; Meyer, R.A.; Takai, Mineo; Chen, Yuan; Zeng, Xiaodong; Martin, Jay; Song, Ha Yoon

doi:10.1109/2.722293

Cited by 471 publications

(201 citation statements)

References 13 publications

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“…The simulation model has been implemented in the PARSEC language developed at UCLA [3]. The grid model considers an application multicasting data files to R receivers through a packet network composed of a fast core network and several slower edge access networks.…”

Section: Simulation and Experimental Resultsmentioning

confidence: 99%

Designing and evaluating an active grid architecture

Bouhafs¹,

Gelas²,

Lefèvre³

et al. 2005

Future Generation Computer Systems

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Today's computational grids are using the standard IP routing functionality, that has basically remained unchanged for 2 decades, considering the network as a pure communication infrastructure. With the grid's distributed system point of view, one might consider to extend the commodity Internet's basic functionalities. Higher value functionalities can thus be offered to computational grids. In this paper, we report on our early experiences in building application-aware components and in defining an active grid architecture that would bring the usage of computational grid to a higher level than it is now (mainly batch submission of jobs). To illustrate the potential of this approach, we first present how such application-aware components could be built and then some experiments on deploying enhanced communication services for the grid. We will show how reliable multicast and QoS mechanisms could deploy specific services based on the grid application needs.

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Section: Simulation and Experimental Resultsmentioning

confidence: 99%

Designing and evaluating an active grid architecture

Bouhafs¹,

Gelas²,

Lefèvre³

et al. 2005

Future Generation Computer Systems

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“…In this section, extensive simulations are performed to compare the optimistic message logging with our proposed method (POML) with the traditional one [2] (TOML) using a discrete-event simulation language [1]. Two performance indices are used for comparison of failure-free overhead and recovery overhead respectively; the elapsed time until the same distributed execution has been completed (T complete ) and the total number of state intervals rolled back in the entire system due to process failures (NOSI).…”

Section: Methodsmentioning

confidence: 99%

On Reducing Rollback Propagation Effect of Optimistic Message Logging for Group-Based Distributed Systems

Ahn

2013

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper presents a new scalable method to considerably reduce the rollback propagation effect of the conventional optimistic message logging by utilizing positive features of reliable FIFO group communication links. To satisfy this goal, the proposed method forces group members to replicate different receive sequence numbers (RSNs), which they assigned for each identical message to their group respectively, into their volatile memories. As the degree of redundancy of RSNs increases, the possibility of local recovery for each crashed process may significantly be higher. Experimental results show that our method can outperform the previous one in terms of the rollback distance of non-faulty processes with a little normal time overhead.

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“…Examples of such environments are: YADDES (Yet Another Distributed Discrete Event Simulator) [129], SPEEDES (Synchronous Parallel Environment for Emulation and Discrete Event Simulation) [130], WARPED [131], HLA (High-Level Architecture) [132], WarpIV [133], Parsec (Parallel Simulation Environment for Complex Systems) [139], POSE (Parallel Object-oriented Simulation Environment) [140], JAMES II (JAva-based Multipurpose Environment for Simulation) [141], lsik [134], and Unified Framework [142].…”

Section: Parallel and Distributed Environmentsmentioning

confidence: 99%

Synchronization methods in parallel and distributed discrete-event simulation

Jafer

Liu

Wainer

2013

Simulation Modelling Practice and Theory

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a b s t r a c tThis work attempts to provide insight into the problem of executing discrete event simulation in a distributed fashion. The article serves as the state of the art in Parallel DiscreteEvent Simulation (PDES) by surveying existing algorithms and analyzing the merits and drawbacks of various techniques. We discuss the main characteristics of existing synchronization methods for parallel and distributed discrete event simulation. The two major categories of synchronization protocols, namely conservative and optimistic, are introduced and various approaches within each category are presented. We also present the latest efforts towards PDES on emerging platforms such as heterogeneous multicore processors, Web services, as well as Grid and Cloud environment.Ó 2012 Elsevier B.V. All rights reserved. Parallel discrete-event simulationWith the computing power and advanced software available today, modeling and simulation has become a cost-effective tool for detailed analysis of a broad array of natural and artificial systems. Parallel and distributed simulation is widely accepted as the technology of choice to speed up large-scale discrete-event simulation and to promote reusability and interoperability of simulation components. Parallel Discrete-Event Simulation (PDES) has received increasing interest as simulations become more time consuming and geographically distributed. A rich literature has already been developed in the last three decades, taking advantage of the increasing availability of highly parallel and distributed computing platforms. It has been several years since the last survey by Perumalla [5] and a refreshed review of the latest developments would be needed for us to ponder new research initiatives, especially on emerging platforms such as many-core processors, Internetscale simulation environments, and cloud-based virtualized infrastructures.In parallel and distributed simulations, the entire simulation task is divided into a set of smaller subtasks with each executed on a different processor or node. Hence, the simulation system is viewed as a collection of concurrent processes, each modeling a different part of the physical system and executing on a dedicated processor in a sequential fashion. These processes communicate with each other by exchanging time-stamped event messages. An event refers to an update to simulation system state at a specific simulation time instant. Throughout the simulation, events arrive at destination processes, and depending on the delivery ordering system of the simulation, they are processed differently. The two commonly used orderings are (1) receive-order and (2) timestamp-order. With the first type, events are delivered to the destination processes when they arrive at the destination. On the other hand, with the timestamp-order, events are delivered in non-decreasing order of their timestamp, requiring runtime checks and buffering to ensure such ordering.1569-190X/$ -see front matter Ó

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Parsec: a parallel simulation environment for complex systems

Cited by 471 publications

References 13 publications

Designing and evaluating an active grid architecture

Designing and evaluating an active grid architecture

On Reducing Rollback Propagation Effect of Optimistic Message Logging for Group-Based Distributed Systems

Synchronization methods in parallel and distributed discrete-event simulation

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