Conservative Distributed Discrete Event Simulation on Amazon EC2

Vanmechelen, Kurt; Munck, Silas De; Broeckhove, J.

doi:10.1109/ccgrid.2012.73

Cited by 18 publications

(14 citation statements)

References 18 publications

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“…We also adopt a unique approach by focusing at the lowest level, namely at the level of the hypervisor itself. Recently, Vanmechelen et al [2012] reported evaluation of a set of conservative synchronization protocols on EC2, suggesting conservative algorithms that could perform better in the Cloud infrastructure. Overall, the area is nascent, and much additional research is needed to explore the space opened by the new metrics beyond raw speed of PDES execution.…”

Section: Related Workmentioning

confidence: 99%

Efficient Parallel Discrete Event Simulation on Cloud/Virtual Machine Platforms

Yoginath

Perumalla

2015

ACM Trans. Model. Comput. Simul.

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Cloud and Virtual Machine (VM) technologies present new challenges with respect to performance and monetary cost in executing parallel discrete event simulation (PDES) applications. Due to the introduction of overall cost as a metric, the traditional use of the highest-end computing configuration is no longer the most obvious choice. Moreover, the unique runtime dynamics and configuration choices of Cloud and VM platforms introduce new design considerations and runtime characteristics specific to PDES over Cloud/VMs. Here, an empirical study is presented to help understand the dynamics, trends, and trade-offs in executing PDES on Cloud/VM platforms. Performance and cost measures obtained from multiple PDES applications executed on the Amazon EC2 Cloud and on a high-end VM host machine reveal new, counterintuitive VM-PDES dynamics and guidelines. One of the critical aspects uncovered is the fundamental mismatch in hypervisor scheduler policies designed for general Cloud workloads versus the virtual time ordering needed for PDES workloads. This insight is supported by experimental data revealing the gross deterioration in PDES performance traceable to VM scheduling policy. To overcome this fundamental problem, the design and implementation of a new deadlock-free scheduler algorithm are presented, optimized specifically for PDES applications on VMs. The scalability of our scheduler has been tested in up to 128 VMs multiplexed on 32 cores, showing significant improvement in the runtime relative to the default Cloud/VM scheduler. The observations, algorithmic design, and results are timely for emerging Cloud/VM-based installations, highlighting the need for PDES-specific support in high-performance discrete event simulations on Cloud/VM platforms.

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Section: Related Workmentioning

confidence: 99%

Efficient Parallel Discrete Event Simulation on Cloud/Virtual Machine Platforms

Yoginath

Perumalla

2015

ACM Trans. Model. Comput. Simul.

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“…To that end, we surveyed prior efforts [17,[27][28][29] that focused on deploying parallel discrete event simulations (PDES) [16] in the cloud, which reveal that the performance of the simulation deteriorates as the size of the cluster distributed across the cloud increases. This occurs due primarily to the limited bandwidth and overhead of the time synchronization protocols needed in the cloud [42]. Thus, cloud deployment for this category of simulations is still limited.…”

Section: Introductionmentioning

confidence: 98%

A simulation as a service cloud middleware

et al. 2015

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Many seemingly simple questions that individual users face in their daily lives may actually require substantial number of computing resources to identify the right answers. For example, a user may want to determine the right thermostat settings for different rooms of a house based on a tolerance range such that the energy consumption and costs can be maximally reduced while still offering comfortable temperatures in the house. Such answers can be determined through simulations. However, some simulation models as in this example are stochastic, which require the execution of a large number of simulation tasks and aggregation of results to ascertain if the outcomes lie within specified confidence intervals. Some other simulation models, such as the study of traffic conditions using simulations may need multiple instances to be executed for a number of different parameters. Cloud computing has opened up new avenues for individuals and organizations with limited resources to obtain answers to problems that hitherto required expensive and computationally-intensive resources. This paper presents SIMaaS, which is a cloudbased Simulation-as-a-Service to address these challenges. We demonstrate how lightweight solutions using Linux containers (e.g., Docker) are better suited to support such services instead of heavyweight hypervisor-based solutions, which are shown to incur substantial overhead in provisioning virtual machines on-demand. Empirical results validating our claims are presented in the context of two case studies.

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“…Some discrete-event simulation approaches offer enhanced flexibility in defining process event flow [5,18]. Others have built distributed discrete-event simulators [6,17] to exploit the power of a distributed environment, but have not focused on the resources.…”

Section: Related Workmentioning

confidence: 99%

Resource Specification for Prototyping Human-Intensive Systems

Shin

Brun

Osterweil

et al. 2015

Fundamental Approaches to Software Engineering

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Abstract. Today's software systems rely heavily on complex resources, such as humans. Human-intensive systems are particularly important in our society, especially in the healthcare, financial, and software development domains. One challenge in developing such systems is that the system design must account for the constraints, capabilities, and allocation policies of their complex resources, particularly the humans. The resources, their capabilities, and their allocation policies and constraints need to be carefully specified, and modeled. Toward the goal of supporting the design of systems that make effective use of such resources, we introduce a resource specification language and a process-aware, discrete-event simulation engine that simulates system executions while adhering to these resource specifications. The simulation supports (1) modeling the resources that are used by the system, and the ways in which they are used, (2) experimenting with different resource capability mixes and allocation policies, and (3) identifying such undesirable situations as bottlenecks, and inefficiencies that result from these mixes and policies. The joint use of detailed resource specifications and simulation supports rapid evaluation of human-intensive system designs. We evaluate our specification language and simulation framework in the healthcare domain, on a software system for managing a hospital emergency department.

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Conservative Distributed Discrete Event Simulation on Amazon EC2

Cited by 18 publications

References 18 publications

Efficient Parallel Discrete Event Simulation on Cloud/Virtual Machine Platforms

Efficient Parallel Discrete Event Simulation on Cloud/Virtual Machine Platforms

A simulation as a service cloud middleware

Resource Specification for Prototyping Human-Intensive Systems

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