Complex networks are an important methodology to model several (if not all) aspects of the real world, in which multiple entities interact, in some way. While many aspects related to such interactions can be investiga...
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Complex networks are an important methodology to model several (if not all) aspects of the real world, in which multiple entities interact, in some way. While many aspects related to such interactions can be investigated by looking at the general mathematical metrics of the networks, an alternative approach lies in the simulation of some application protocol on top of (large scale) complex networks. In this paper, we present a study on this intricate problem. The complexity of the simulation is due to the need to model all the interactions among network nodes. We focus on discrete-event simulation, a simulation methodology that enables both sequential (i.e. monolithic) and parallel and distributed simulation (i.e. PADS) approaches. We discuss the performance and scalability requirements that the simulator should have. We also introduce a case study based on the agent-based simulation of gossip dissemination on top of a complex network. To demonstrate the viability of this simulation technique, we focus on a tool we built to simulate complex networks. The tool exploits adaptive partitioning mechanisms, which are essential to reduce the communication overhead in the PADS. An experimental evaluation has been conducted using different network topologies and simulator setups. Results demonstrate the feasibility of the approach to simulate complex networks. (C) 2022 Elsevier Inc. All rights reserved.
This paper presents FT-GAIA, a software-based fault-tolerant parallel and distributed simulation middleware. FT-GAIA has being designed to reliably handle parallel and distributed simulation (PADS) models, which are n...
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This paper presents FT-GAIA, a software-based fault-tolerant parallel and distributed simulation middleware. FT-GAIA has being designed to reliably handle parallel and distributed simulation (PADS) models, which are needed to properly simulate and analyze complex systems arising in any kind of scientific or engineering field. PADS takes advantage of multiple execution units run in multicore processors, cluster of workstations or HPC systems. However, large computing systems, such as HPC systems that include hundreds of thousands of computing nodes, have to handle frequent failures of some components. To cope with this issue, FT-GAIA transparently replicates simulation entities and distributes them on multiple execution nodes. This allows the simulation to tolerate crash-failures of computing nodes. Moreover, FT-GAIA offers some protection against Byzantine failures, since interaction messages among the simulated entities are replicated as well, so that the receiving entity can identify and discard corrupted messages. Results from an analytical model and from an experimental evaluation show that FT-GAIA provides a high degree of fault tolerance, at the cost of a moderate increase in the computational load of the execution units.
New challenges and opportunities emerge as computing interacts with our surroundings in unprecedented ways. One of these challenges is the energy consumed by computations and communications. In large cloud-based compu...
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New challenges and opportunities emerge as computing interacts with our surroundings in unprecedented ways. One of these challenges is the energy consumed by computations and communications. In large cloud-based computing systems, it is a major concern because it forms the largest proportion of the environmental and operational costs of data centers. In mobile systems, it directly impacts battery life. This work focuses on understanding and reducing power and energy consumption of the parallel and distributed execution of discrete event simulations, an area not extensively studied in the past. We first empirically characterize the energy consumption of widely used synchronization algorithms. Then a model and techniques are presented and exercised to create energy profile of a distributedsimulation system. These demonstrate that distributed execution and synchronization can incur a significant energy and power overhead. To study and optimize the energy required for distributed execution, a property termed zero-energy synchronization is proposed. A zero-energy synchronization algorithm based on an oracle is presented, and a practical implementation is discussed. A more generic synchronization algorithm termed Low Energy YAWNS (LEY) is also proposed. LEY represents the first attempt to design a synchronization algorithm for energy efficiency and, in principle, can achieve zero-energy synchronization for a large class of distributedsimulation applications. To employ the energy efficiency of specialized computing hardware platforms, recurrence relations for simulating G/G/1 queueing networks, directly implementable using library primitives, are proposed. In addition to optimizations and scalability they offer, the use of library primitives ease development and open up avenues for adapting the simulation for custom hardware. Composition of parallel prefix scans further improve the energy efficiency of the proposed recurrences and similar sequences of parallel prefix sca
Allocating appropriate resource for parallel and distributed simulation (PADS) applications in clouds is an intuitive way to improve their execution efficiency. However, the heterogeneity of virtual machine (VMs) in c...
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Allocating appropriate resource for parallel and distributed simulation (PADS) applications in clouds is an intuitive way to improve their execution efficiency. However, the heterogeneity of virtual machine (VMs) in clouds with respect to both their computing power and network latency influences the execution efficiency of PADS applications on different combinations of VMs. Besides, frequent synchronization is one of the characteristics during the execution of PADS applications, which seriously challenges the prediction of the influence of VMs' computing power and network latency on their execution efficiency, and makes allocating appropriate VMs difficult as a result. This paper first proposes a revivification-based prediction model (ERP), which revives the execution based on statistical data from actual execution of PADS applications to predict the running time of PADS applications on different combinations of VMs. Then, an ERP-based Allocation algorithm, namely, ERPA, is raised to optimize VMs allocation to minimize the running time of PADS applications in clouds. A series of experiments are conducted to compare the proposed ERPA with three resource allocation algorithms, ie, Gang-scheduling-based, Makespan-based, and Max-Min-based algorithms, and the experimental results demonstrate the advantage of ERPA in improving execution efficiency of PADS applications in clouds. In particular, for communication-sensitive PADS applications, the advantage of ERPA is more significant.
Time synchronization is critical for parallel and distributed simulation (PADS). In this paper we first compare the two main synchronization mechanisms in PADS, namely conservative and optimistic protocols. Then we pr...
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ISBN:
(纸本)9781509040421
Time synchronization is critical for parallel and distributed simulation (PADS). In this paper we first compare the two main synchronization mechanisms in PADS, namely conservative and optimistic protocols. Then we propose a Minimum Time Buckets synchronization algorithm, which can optimistically schedule events without sending anti-messages. Comparing to the well-known Breathing Time Bucket (MTB) synchronization algorithm, our algorithm reduces the waiting time and rollback probability for logical processes, yet it commits the same number of events in each time advance interval. Experiment results show that the MTB algorithm can effectively decrease the rollback rate, reduce the execution time, and achieve a higher speed up.
Recent advances in computing architectures and networking are bringing parallel computing systems to the masses so increasing the number of potential users of these kinds of systems. In particular, two important techn...
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Recent advances in computing architectures and networking are bringing parallel computing systems to the masses so increasing the number of potential users of these kinds of systems. In particular, two important technological evolutions are happening at the ends of the computing spectrum: at the "small'' scale, processors now include an increasing number of independent execution units (cores), at the point that a mere CPU can be considered a parallel shared-memory computer;at the "large'' scale, the Cloud Computing paradigm allows applications to scale by offering resources from a large pool on a pay-as-you-go model. Multi-core processors and Clouds both require applications to be suitably modified to take advantage of the features they provide. Despite laying at the extreme of the computing architecture spectrum - multi-core processors being at the small scale, and Clouds being at the large scale - they share an important common trait: both are specific forms of parallel/distributed architectures. As such, they present to the developers well known problems of synchronization, communication, workload distribution, and so on. Is parallel and distributed simulation ready for these challenges? In this paper, we analyze the state of the art of parallel and distributed simulation techniques, and assess their applicability to multi-core architectures or Clouds. It turns out that most of the current approaches exhibit limitations in terms of usability and adaptivity which may hinder their application to these new computing architectures. We propose an adaptive simulation mechanism, based on the multi-agent system paradigm, to partially address some of those limitations. While it is unlikely that a single approach will work well on both settings above, we argue that the proposed adaptive mechanism has useful features which make it attractive both in a multi-core processor and in a Cloud system. These features include the ability to reduce communication costs by migrating si
In the past, several time synchronization methods have been proposed for parallel and distributed simulation (PDS). Among them, one widely used conservative method is the Chandy-Misra-Bryant (CMB) algorithm. In the CM...
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ISBN:
(纸本)9781665435406
In the past, several time synchronization methods have been proposed for parallel and distributed simulation (PDS). Among them, one widely used conservative method is the Chandy-Misra-Bryant (CMB) algorithm. In the CMB algorithm, many null messages may be exchanged among logical processes to advance their clocks so that deadlock will not occur among them. In this work, using a data-plane programmable P4 hardware switch, we design and implement a data fusion-based approach inside the packet processing pipeline of the P4 switch. Our approach extracts the timestamp carried in exchanged null messages, computes the fusion results of these timestamps, drops unnecessary null messages inside the switch, generates new messages carrying the fusion results, and sends these generated messages to only the logical processes that can benefit from receiving these messages. Experimental results show that on an 8-host testbed, our approach can speed up a PDS by a factor of 2.75 and 1.65 when compared with the unicast and multicast approaches, respectively.
The need for accurate solar power forecasting is critical for grid stability as solar energy becomes more prevalent. This paper presents a new framework called Cloud-based Analysis and Integration for Data Efficiency ...
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The need for accurate solar power forecasting is critical for grid stability as solar energy becomes more prevalent. This paper presents a new framework called Cloud-based Analysis and Integration for Data Efficiency (CAIDE) for real-time monitoring and forecasting of solar irradiance in sensor farms. CAIDE can handle multiple sensor farms, enhance predictive models in real-time, and is built on Model Based Systems Engineering (MBSE) and Internet of Things (IoT) technologies. It can correct its forecasts, ensuring they stay current, and operates on various architectures, ensuring scalability. Tested on multiple sensor farms, CAIDE proved to be scalable and improved the initial accuracy of solar power production forecasts in real-time. This framework is significant for solar plant deployment and the advancement of renewable energy.
Network simulations play an important role in the evolution of V2X (Vehicle-to-Everything) communication services. Large-scale simulations involving a huge number of connected vehicles and/or heavy network traffic, ho...
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ISBN:
(纸本)9798400705816
Network simulations play an important role in the evolution of V2X (Vehicle-to-Everything) communication services. Large-scale simulations involving a huge number of connected vehicles and/or heavy network traffic, however, typically take a long period of time to complete. In this poster, we explore a mechanism to accelerate network simulations (in terms of simulation time processed per unit wall-clock time) by the means of parallel and distributed simulations (PADS). Multiple instances of network simulators are run in parallel and loosely synchronized to process simulation events in a distributed manner. We develop a proof-of-concept implementation of the framework to showcase its feasibility. The results indicate that the proposed framework achieves up to 10 times faster simulation speed with eight parallel network simulation instances.
parallel and distributed simulation (PDS) is often employed to tackle the computational intensity of system-level simulation of real-world complex embedded and cyber-physical systems (CPSs). However, CPS models compri...
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parallel and distributed simulation (PDS) is often employed to tackle the computational intensity of system-level simulation of real-world complex embedded and cyber-physical systems (CPSs). However, CPS models comprise heterogeneous components with diverge semantics for which incompatible PDS approaches are developed. We propose an automated PDS flow based on a formal modeling framework-with necessary extensions-targeting heterogeneous embedded and CPS design. The proposed flow characterizes the sequential executable specification of a heterogeneous model and generates a PDS cluster. State-of-the-art graph partitioning methods are adopted and a new extensible constraint-base formulation of the model partitioning problem is developed. The applicability, effectiveness, and scalability of the proposed flow is demonstrated using case studies.
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