Optimistic Synchronization of Parallel Simulations in Cloud Computing Environments

Malik, Asad Waqar; Park, Alfred; Fujimoto, Richard M.

doi:10.1109/cloud.2009.79

Cited by 39 publications

(44 citation statements)

References 14 publications

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“…al have proposed an approach for using optimistic time synchronization protocols in cloud environments [11]. In [12], they propose an optimistic time synchronization protocol that is better tailored to execution platforms such as clouds that exhibit longer communication delays and higher external interference from other users' computations, as compared to tightly coupled local clusters. Their evaluation does not use real cloud offerings though, as they deploy the simulation on a locally controlled set of machines that belong to different LANs.…”

Section: Related Workmentioning

confidence: 99%

Conservative Distributed Discrete Event Simulation on Amazon EC2

Vanmechelen

Munck

Broeckhove

2012

2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (Ccgrid 2012)

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A discrete-event simulator's ability to distribute the execution of a simulation model allows one to deal with the memory limitations of a single computational resource, and thereby increase the scale or level of detail at which models can be studied. In addition, distribution has the potential to reduce the round trip time of a simulation by incorporating multiple computational cores into the simulation's execution. However, such gains can be voided by the overhead that time synchronization protocols introduce. These protocols are required to prevent the occurrence of causality errors during a parallel execution of a simulation. The overhead depends on the protocol, characteristics of the simulation model, and the architecture of the computational resources used. Recently, infrastructure-as-a-service offerings in cloud computing have introduced flexibility in acquiring computational resources on a pay-as-you-go basis. At present, it is unclear to what extent these offerings are suited for the distributed execution of discrete-event simulations, and how the characteristics of different resource types impact the runtime performance of distributed simulations. In this paper we investigate this issue, and assess the performance of different conservative time synchronization protocols on a range of cloud resource types that are currently available on Amazon EC2.

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Section: Related Workmentioning

confidence: 99%

Conservative Distributed Discrete Event Simulation on Amazon EC2

Vanmechelen

Munck

Broeckhove

2012

2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (Ccgrid 2012)

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“…The scalabity for parallel simulators means that the simulation speedup constantly rises when the number of allocated host processors for simulation is increased [61]. Over the decades of research in the field, many optimization methods such as Lazy Cancellation [62], Lazy Reevaluation [63], Direct Cancellation [50], Early Cancellation [64], Space-time Simulation, Optimistic Time Windows [65], Wolf Calls [66] and Time Warp Straggler Message Identification [67] have been proposed to improve the overall performance of PDES based Simulators. However, PDES based parallel simulators are not yet truly competitive with performant sequential simulators with respect to simulation accuracy and scalability.…”

Section: Terminology and Classificationsmentioning

confidence: 99%

Manycore simulation for peta-scale system design: Motivation, tools, challenges and prospects

Zarrin

Aguiar

Barraca

2017

Simulation Modelling Practice and Theory

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractThe architecture design of peta-scale computing systems is complex and presents lots of difficulties to designs, as current tools lack support for relevant features of future scenarios. Novel systems must be designed with great care and tools, such as manycore architecture simulators, must be adapted accordingly. However, current simulation tools are very slow, often specific-purposeoriented, suffer from various issues and are rarely able to simulate thousands of cores. The emergence of peta-scale systems and the upcoming manycore era brings nevertheless new challenges to computing systems and architectures, adding further difficulties and requirements on the development of the corresponding simulators. Furthermore, the design of architecture simulators for manycore systems involve methods and techniques from various interdisciplinary research areas, which in turn brings more challenges in different aspects. As system complexity grows, the growth of the simulation capacity is being outpaced (reaching the so called simulation wall). In this paper, we present the challenges for simulating future large scale manycore environments, and we investigate the adequacy of current modeling and simulation tools, methodologies and techniques. The aim of this work is to highlight how current approaches can best deal with the identified problems, smoothing the challenges of research in future peta-scale systems.

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“…The Time Warp Straggler Message Identification Protocol (TW-SMIP) [64] is advanced to handle optimistic synchronization. In the TW-SMIP, heartbeat messages are used to detect straggler messages.…”

Section: Tw-smipmentioning

confidence: 99%

A Multi-Layered Scheme for Distributed Simulations on the Cloud Environment

Guan

Grande

Boukerche

2019

IEEE Trans. Cloud Comput.

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In order to improve simulation performance and integrate simulation resources among geographically distributed locations, the concept of distributed simulation is proposed.Several types of distributed simulation standards, such as DIS and HLA are established to formalize simulations and achieve reusability and interoperability of simulation components. In order to implement these distributed simulation standards and manage the underlying system of distributed simulation applications, Grid Computing and CloudComputing technologies are employed to tackle the details of operation, configuration, and maintenance of the simulation platforms in which simulation applications are deployed. However, for modelers who may not be familiar with the management of distributed systems, it is challenging to create a simulation-run-ready environment that incorporates different types of computing resources and network environments. In this thesis, we propose a new multi-layered cloud-based scheme for enabling modeling and simulation based on different distributed simulation standards. The scheme is designed to ease the management of underlying resources and achieve rapid elasticity, providing unlimited computing capability to end users; energy consumption, security, multi-user availability, scalability and deployment issues are all considered. We describe a mechanism for handling diverse network environments. With its adoption, idle public resources can easily be configured as additional computing capabilities for the local resource pool.A fast deployment model is built to relieve the migration and installation process of this platform. An energy conservation strategy is utilized to reduce the energy consumption of computing resources. Security components are also implemented to protect sensitive information and block malicious attacks in the cloud. In the experiments, the proposed scheme is compared with its corresponding grid computing platform; the cloud computing platform achieves a similar performance, but incorporates many of the cloud's advantages.ii

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Optimistic Synchronization of Parallel Simulations in Cloud Computing Environments

Cited by 39 publications

References 14 publications

Conservative Distributed Discrete Event Simulation on Amazon EC2

Conservative Distributed Discrete Event Simulation on Amazon EC2

Manycore simulation for peta-scale system design: Motivation, tools, challenges and prospects

A Multi-Layered Scheme for Distributed Simulations on the Cloud Environment

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