1,489 research outputs found
Towards Data-driven Simulation of End-to-end Network Performance Indicators
Novel vehicular communication methods are mostly analyzed simulatively or
analytically as real world performance tests are highly time-consuming and
cost-intense. Moreover, the high number of uncontrollable effects makes it
practically impossible to reevaluate different approaches under the exact same
conditions. However, as these methods massively simplify the effects of the
radio environment and various cross-layer interdependencies, the results of
end-to-end indicators (e.g., the resulting data rate) often differ
significantly from real world measurements. In this paper, we present a
data-driven approach that exploits a combination of multiple machine learning
methods for modeling the end-to-end behavior of network performance indicators
within vehicular networks. The proposed approach can be exploited for fast and
close to reality evaluation and optimization of new methods in a controllable
environment as it implicitly considers cross-layer dependencies between
measurable features. Within an example case study for opportunistic vehicular
data transfer, the proposed approach is validated against real world
measurements and a classical system-level network simulation setup. Although
the proposed method does only require a fraction of the computation time of the
latter, it achieves a significantly better match with the real world
evaluations
OpenKnowledge at work: exploring centralized and decentralized information gathering in emergency contexts
Real-world experience teaches us that to manage emergencies, efficient crisis response coordination is crucial; ICT infrastructures are effective in supporting the people involved in such contexts, by supporting effective ways of interaction. They also should provide innovative means of communication and information management. At present, centralized architectures are mostly used for this purpose; however, alternative infrastructures based on the use of distributed information sources, are currently being explored, studied and analyzed. This paper aims at investigating the capability of a novel approach (developed within the European project OpenKnowledge1) to support centralized as well as decentralized architectures for information gathering. For this purpose we developed an agent-based e-Response simulation environment fully integrated with the OpenKnowledge infrastructure and through which existing emergency plans are modelled and simulated. Preliminary results show the OpenKnowledge capability of supporting the two afore-mentioned architectures and, under ideal assumptions, a comparable performance in both cases
A Reliable and Low Latency Synchronizing Middleware for Co-simulation of a Heterogeneous Multi-Robot Systems
Search and rescue, wildfire monitoring, and flood/hurricane impact assessment
are mission-critical services for recent IoT networks. Communication
synchronization, dependability, and minimal communication jitter are major
simulation and system issues for the time-based physics-based ROS simulator,
event-based network-based wireless simulator, and complex dynamics of mobile
and heterogeneous IoT devices deployed in actual environments. Simulating a
heterogeneous multi-robot system before deployment is difficult due to
synchronizing physics (robotics) and network simulators. Due to its
master-based architecture, most TCP/IP-based synchronization middlewares use
ROS1. A real-time ROS2 architecture with masterless packet discovery
synchronizes robotics and wireless network simulations. A velocity-aware
Transmission Control Protocol (TCP) technique for ground and aerial robots
using Data Distribution Service (DDS) publish-subscribe transport minimizes
packet loss, synchronization, transmission, and communication jitters. Gazebo
and NS-3 simulate and test. Simulator-agnostic middleware. LOS/NLOS and TCP/UDP
protocols tested our ROS2-based synchronization middleware for packet loss
probability and average latency. A thorough ablation research replaced NS-3
with EMANE, a real-time wireless network simulator, and masterless ROS2 with
master-based ROS1. Finally, we tested network synchronization and jitter using
one aerial drone (Duckiedrone) and two ground vehicles (TurtleBot3 Burger) on
different terrains in masterless (ROS2) and master-enabled (ROS1) clusters. Our
middleware shows that a large-scale IoT infrastructure with a diverse set of
stationary and robotic devices can achieve low-latency communications (12% and
11% reduction in simulation and real) while meeting mission-critical
application reliability (10% and 15% packet loss reduction) and high-fidelity
requirements
Building a generalized distributed system model
A modeling tool for both analysis and design of distributed systems is discussed. Since many research institutions have access to networks of workstations, the researchers decided to build a tool running on top of the workstations to function as a prototype as well as a distributed simulator for a computing system. The effects of system modeling on performance prediction in distributed systems and the effect of static locking and deadlocks on the performance predictions of distributed transactions are also discussed. While the probability of deadlock is considerably small, its effects on performance could be significant
BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning
An ever increasing number of configuration parameters are provided to system
users. But many users have used one configuration setting across different
workloads, leaving untapped the performance potential of systems. A good
configuration setting can greatly improve the performance of a deployed system
under certain workloads. But with tens or hundreds of parameters, it becomes a
highly costly task to decide which configuration setting leads to the best
performance. While such task requires the strong expertise in both the system
and the application, users commonly lack such expertise.
To help users tap the performance potential of systems, we present
BestConfig, a system for automatically finding a best configuration setting
within a resource limit for a deployed system under a given application
workload. BestConfig is designed with an extensible architecture to automate
the configuration tuning for general systems. To tune system configurations
within a resource limit, we propose the divide-and-diverge sampling method and
the recursive bound-and-search algorithm. BestConfig can improve the throughput
of Tomcat by 75%, that of Cassandra by 63%, that of MySQL by 430%, and reduce
the running time of Hive join job by about 50% and that of Spark join job by
about 80%, solely by configuration adjustment
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