Robson Eduardo De Grande

Optimized Federate Migration for Large-Scale HLA-Based Simulations

Federate migration is a fundamental mechanism for large-scale distributed simulations. It provides the means for simulation load-balancing and thus improves the simulations overall performance. Given its importance for simulations, several federate migration approaches have been proposed in the literature. Some approaches freeze the entire simulation, others use third-party mechanisms to transport data, and others make use of unnecessary communication and computing. Thus, in order to minimize the time spent on federate migration, we introduce a simulation agent that manages the migration steps, as well as the migrating federates communication with the other simulation entities. The use of the agent simplifies the message management transparently and avoids redundant usage of network and computing resources. We demonstrate through simulation experiments that our approach decreases federate migration latency, improving the performance of HLA simulations that run over large-scale environments.

Dynamic partitioning of distributed virtual simulations for reducing communication load

HLA-based simulations can experience performance degradation due to communication latencies between simulation federates, which generate significant cumulative overhead. Even though the HLA standard provides mechanisms to decrease the misuse of network resources, it does not present any tool to diminish the communication latencies between interactive federates. Moreover, the interaction dependencies can be predicted before simulations are initiated, but such predictions relies on the determinism of simulations, producing erroneous balancing when simulations change their load dynamically. Thus, an hierarchical three-phase dynamic communication load balancing scheme is devised to react to run-time load changes, so the scheme performs constant, periodical monitoring of resources, re-distribution of load, and migration of federates. The balancing system reorganizes the distribution of large-scale HLA-based simulations, so the communication latencies are minimized, increasing the parallelism of the distributed simulations and leading to a performance improvement. Experiments were realized to measure the benefits of the scheme, and through comparative analyses, the balancing scheme presented considerable performance improvement to HLA-based simulations.

Distributed dynamic balancing of communication load for large-scale HLA-based simulations

In large-scale distributed simulations, communication aspects are highly significant due to their direct influence on performance. The High Level Architecture (HLA) provides services for managing such simulations and reducing their communication overhead. However, HLA does not present any solution for the communication latencies caused by the network distances among simulation elements. Several dynamic balancing schemes have been proposed attempting to provide a general best solution for the performance issues caused by computation and communication imbalances. Amongst these schemes, some just perform a limited redistribution of communication load. Based on a proximity analysis of federate interactions, a distributed dynamic scheme for balancing the communication load of HLA-based simulations is devised. The design of this distributed scheme aims at improving fault tolerance, decreasing communication and computation overload, and avoiding bottlenecks in the system. The distributed balancing system, organized in the hierarchical structure, monitors simulations, redistributes load, and migrates federates. Experiments have been realized to compare the proposed distributed scheme with a centralized scheme and to prove its effectiveness for large-scale HLA-based simulations.

Dynamic load redistribution based on migration latency analysis for distributed virtual simulations

Distributed virtual simulations deployed on shared resources can frequently undergo loss of performance due to external background load, improper placement of simulation entities, or dynamic simulation load changes. The High Level Architecture (HLA) was designed as a solution for coordinating the execution of distributed simulations. Even though this framework offers management services to organize such simulations, it does not provide mechanisms for detecting and controlling load imbalances. Several balancing approaches have been designed aiming at a generic scheme for solving load imbalance issues of distributed simulations, but these approaches are concerned with issues of specific simulation applications or are unaware of environment characteristics. To overcome such limitations, a dynamic, distributed balancing scheme has been developed. However, the scheme is not aware of federate migration latencies. Since migration latency directly influences balancing efficiency and responsiveness, a redistribution scheme is proposed to measure migration delays and use such delays in the balancing algorithm to determine load deployment changes. These delays are used in a cost function that determines the redistribution behaviour of the balancing scheme. Experiments have been performed to analyze the performance gain of the proposed scheme when migration procedures introduce costly latencies into simulations.

Vehicular Cloud: Stochastic Analysis of Computing Resources in a Road Segment

Considerable attention has been assigned to Vehicular Cloud towards identifying methods to utilize under-used, available computing and physical resources of vehicles effectively. Most work on vehicular cloud is so far on the taxonomy definition level, and the dynamically changing amount of available resources characterizes vehicular cloud as more cumbersome and complex than the traditional Clouds. In this paper, we define and analyze the expected number of vehicles in a roadway segment. These vehicles can serve as the building blocks for the vehicular cloud, enabling a tremendously large set of applications that benefit the whole traffic system. To contribute with the analysis, two types of traffic scenarios are considered in this work. We use a macroscopic traffic model to investigate the free-flow traffic, and we utilize the queuing theory to observe the queuing-up traffic. The average number of vehicles within a roadway segment is calculated using stochastic models. The results show the boundaries on enabling vehicular cloud, allowing to determine a range of parameters for simulating vehicular clouds.

Predictive Dynamic Load Balancing for Large-Scale HLA-based Simulations

Due to the dependency on the resources, HLA-based simulations can experience load imbalances and consequently loose execution performance. Such imbalances are originated from external background load, inappropriate deployment of simulation entities, heterogeneity of resources, and dynamic load variations. The High Level Architecture (HLA) was developed aiming to facilitate the creation and control of distributed simulations through a design framework and management services, but such an architecture does not offer solutions for solving load imbalance issues. In order to provide mechanisms for preventing performance loss caused by load imbalances in distributed simulations, numerous balancing approaches have been developed. The majority of these mechanisms present application-specific solutions or limited awareness of environment characteristics. To cope with this problem, a distributed dynamic balancing scheme has been designed, but its redistribution algorithm, as other developed balancing schemes, is limited to just correct load distribution issues and does not react properly in presence of abrupt load changes due to be based on recent load status. Therefore, a predictive balancing scheme is proposed to provide a method to decrease the number of precipitated migration moves and to detect and prevent load imbalances based on load variation tendencies. In order to observe and evaluate the proposed scheme, experiments have been performed to compare performance gain and efficiency with the distributed balancing scheme.

Toward a Comprehensive Model for Performance Analysis of Opportunistic Routing in Wireless Mesh Networks

Opportunistic routing (OR) is a promising paradigm that has been proposed for wireless mesh networks. This routing paradigm takes advantage of the broadcast nature of the wireless medium to increase the reliability of transmissions in multihop wireless networks. The selection of a set of candidates involves satisfying the basic requirements of the model, in which packets are forwarded toward the destination. In OR, if one of the selected candidates does not receive the packet, another candidate might be able to continue forwarding the packet. The decision of which forwarder to choose is made by coordination between candidates that have successfully received the transmitted packet. In this paper, we propose a discrete-time Markov chain as a general model for OR and demonstrate how it can be used to evaluate the performance of OR protocols. We also review three well-known OR protocols that we have selected as a study case. Our study demonstrates how our model facilitates better understanding of the combination of a number of candidates and retransmissions and their significant contributions to the successful delivery of data packets. Thus, this shows that our model can help in the design of future OR protocols and efficient candidate selection algorithms.

Design and analysis of stochastic traffic flow models for vehicular clouds

Intelligent transportation systems (ITS) have attracted an increasing amount of attention within both public and private sectors due to the unprecedented number of vehicles all over the world. ITS aim to provide innovative applications and services relating to traffic management, and enable ease of access to information for various system users. The intent to utilize the excessive on-board resources in the transportation system, along with the latest computing resource management technology in conventional clouds, has cultivated the concept of the Vehicular Cloud. Evolved from Vehicular networks, the vehicular cloud can be formed by vehicles autonomously, and provides a large number of applications and services that can benefit the entire transportation system, as well as drivers, passengers, and pedestrians. However, due to high traffic mobility, the vehicular cloud is built on dynamic physical resources; as a result, it experiences several inherent challenges, which increase the complexity of its implementations. Having a detailed picture of the number of vehicles, as well as their time of availability in a given region through a model, works as a critical stepping stone for enabling vehicular clouds, as well as any other system involving vehicles moving over the traffic network. Therefore, in this paper we present a comprehensive stochastic analysis of several traffic characteristics related to the implementation of vehicular cloud inside a road segment by adopting proper traffic models. According to the analytical results, we demonstrate the feasibility of running a certain class of applications or services on the vehicular cloud, even for highly dynamic scenarios.

Measuring Communication Delay for Dynamic Balancing Strategies of Distributed Virtual Simulations

As an inherent characteristic of any distributed system, the execution and performance of distributed virtual simulations totally rely on underlay communication infrastructure and resources. The performance of such simulations is directly restricted by the communication latencies between interdependent simulation components. The high level architecture (HLA) is a framework designed with the objective of organizing these simulations through management services. However, the framework is unaware of the communication delays caused by the network distances between communicating simulation parts. These delays can result from nonplanned initial deployment or dynamic simulation changes, requiring constant load balancing. Due to the importance of balancing distributed simulations, many approaches have been designed. In order to provide a balancing system aware of the dynamic communication changes, a delay-based redistribution scheme has been designed. The scheme successfully arranges the load, but it lacks precision due to communication delay oscillations. Therefore, extensions are proposed to modify the balancing algorithm in order to avoid unnecessary, mistaken load rearrangements. In the experimental results, the delay-based scheme has been able to reduce the simulation execution time when compared with the distributed balancing scheme, and the proposed extension has been capable of increasing the precision of the balancing.

Load Prediction in HLA-Based Distributed Simulation Using Holt's Variants

Due to the dependency of HLA-Based distributed simulations on the resources of distributed environments, simulations can face load imbalances and can suffer from low performance in terms of execution time. High-Level Architecture (HLA) is a framework that simplifies the implementation of distributed simulations, and, it has been built with dedicated resources in mind. As technology is nowadays shifting towards shared resources, the following two weaknesses have become apparent in HLA: managing federates and reacting towards load imbalances on shared resources. Moreover, a number of dynamic load management systems have been designed in order to provide a solution to enable a balanced simulation environment on shared resources. These systems use some specific techniques depending on certain simulation or load aspects, to perform the balancing task. Load prediction is one such technique that improves load redistribution heuristics by preventing load imbalances. In this work, we present a number of enhancements for a prediction technique and compare their efficiency. The proposed enhancements solve observed problems with Holts implementations on dynamic load balancing systems for HLA-Based distributed simulations and provide better forecasting. As a result, these enhancements provide better forecasting for the load of the shared resources.