28,177 research outputs found
Dynamic load balancing policy with communication and computation elements in grid computing with multi-agent system integration
The policy in dynamic load balancing, classification and function are variety based on the focus study for each research. They are different but employing the same strategy to obtain the load balancing. The communication processes between policies are explored within the dynamic load balancing and decentralized approaches. At the same time the computation processes also take into consideration for further steps. Multi-agent system characteristics and capabilities are explored too. The unique capabilities offered by multi-agent systems can be integrated or combined with the structure of dynamic load balancing to produce a better strategy and better load balancing algorithm
Dynamic load balancing policy in grid computing with multi-agent system integration
The policy in dynamic load balancing, classification and function are variety based on the focus study for each research. They are different but employing the same strategy to obtain the load balancing. The communication processes between policies are explored within the dynamic load balancing and decentralized approaches. Multi-agent system characteristics and capabilities are explored too. The unique capabilities offered by multi-agent systems can be integrated or combined with the structure of dynamic load balancing to produce a better strategy to produce a better dynamic load balancing algorithm with multi-agent systems
HPC Enhanced Large Urban Area Evacuation Simulations with Vision based Autonomously Navigating Multi Agents
AbstractAn evacuation simulation code based on Multi Agent Systems (MAS), with moderately complex agents in 2D grid envi- ronments, is developed. The main objective of this code is to estimate the effectiveness of the measures taken to smoothen and speedup the evacuation process of a large urban area, in time critical events like tsunami. A vision based autonomous navigation algorithm, which enables the agents to move through an urban environment and reach a far visible destination, is implemented. This simple algorithm enables a visitor agent to navigate through urban area and reach a destination which is several kilometers away. The navigation algorithm is verified comparing the simulated evacuation time and the paths taken by individual agents with those of theoretical. Further, a parallel computing extension is developed for studying mass evacuation of large areas; vision based autonomous navigation is computationally intensive. Several strategies like communication hiding, dynamic load balancing, etc. are implemented to attain high parallel scalability. Preliminary tests on the K-computer attained strong scalability above 94% at least up to 2048 CPU cores, with 2 million agents
The Simulation Model Partitioning Problem: an Adaptive Solution Based on Self-Clustering (Extended Version)
This paper is about partitioning in parallel and distributed simulation. That
means decomposing the simulation model into a numberof components and to
properly allocate them on the execution units. An adaptive solution based on
self-clustering, that considers both communication reduction and computational
load-balancing, is proposed. The implementation of the proposed mechanism is
tested using a simulation model that is challenging both in terms of structure
and dynamicity. Various configurations of the simulation model and the
execution environment have been considered. The obtained performance results
are analyzed using a reference cost model. The results demonstrate that the
proposed approach is promising and that it can reduce the simulation execution
time in both parallel and distributed architectures
Teaching about Madrid: A Collaborative Agents-Based Distributed Learning Course
Interactive art courses require a huge amount of computational resources to be running on real time. These computational resources are even bigger if the course has been designed as a Virtual Environment with which students can interact. In this paper, we present an initiative that has been develop in a close collaboration between two Spanish Universities: Universidad Politécnica de Madrid and Universidad Rey Juan Carlos with the aim of join two previous research project: a Collaborative Awareness Model for Task-Balancing-Delivery (CAMT) in clusters and the “Teaching about Madrid” course, which provides a cultural interactive background of the capital of Spain
A customizable multi-agent system for distributed data mining
We present a general Multi-Agent System framework for
distributed data mining based on a Peer-to-Peer model. Agent
protocols are implemented through message-based asynchronous
communication. The framework adopts a dynamic load balancing
policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances
A Hybrid Optimization Algorithm for Efficient Virtual Machine Migration and Task Scheduling Using a Cloud-Based Adaptive Multi-Agent Deep Deterministic Policy Gradient Technique
This To achieve optimal system performance in the quickly developing field of cloud computing, efficient resource management—which includes accurate job scheduling and optimized Virtual Machine (VM) migration—is essential. The Adaptive Multi-Agent System with Deep Deterministic Policy Gradient (AMS-DDPG) Algorithm is used in this study to propose a cutting-edge hybrid optimization algorithm for effective virtual machine migration and task scheduling. An sophisticated combination of the War Strategy Optimization (WSO) and Rat Swarm Optimizer (RSO) algorithms, the Iterative Concept of War and Rat Swarm (ICWRS) algorithm is the foundation of this technique. Notably, ICWRS optimizes the system with an amazing 93% accuracy, especially for load balancing, job scheduling, and virtual machine migration. The VM migration and task scheduling flexibility and efficiency are greatly improved by the AMS-DDPG technology, which uses a powerful combination of deterministic policy gradient and deep reinforcement learning. By assuring the best possible resource allocation, the Adaptive Multi-Agent System method enhances decision-making even more. Performance in cloud-based virtualized systems is significantly enhanced by our hybrid method, which combines deep learning and multi-agent coordination. Extensive tests that include a detailed comparison with conventional techniques verify the effectiveness of the suggested strategy. As a consequence, our hybrid optimization approach is successful. The findings show significant improvements in system efficiency, shorter job completion times, and optimum resource utilization. Cloud-based systems have unrealized potential for synergistic optimization, as shown by the integration of ICWRS inside the AMS-DDPG framework. Enabling a high-performing and sustainable cloud computing infrastructure that can adapt to the changing needs of modern computing paradigms is made possible by this strategic resource allocation, which is attained via careful computational utilization
A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing
The emergence of cloud computing based on virtualization technologies brings
huge opportunities to host virtual resource at low cost without the need of
owning any infrastructure. Virtualization technologies enable users to acquire,
configure and be charged on pay-per-use basis. However, Cloud data centers
mostly comprise heterogeneous commodity servers hosting multiple virtual
machines (VMs) with potential various specifications and fluctuating resource
usages, which may cause imbalanced resource utilization within servers that may
lead to performance degradation and service level agreements (SLAs) violations.
To achieve efficient scheduling, these challenges should be addressed and
solved by using load balancing strategies, which have been proved to be NP-hard
problem. From multiple perspectives, this work identifies the challenges and
analyzes existing algorithms for allocating VMs to PMs in infrastructure
Clouds, especially focuses on load balancing. A detailed classification
targeting load balancing algorithms for VM placement in cloud data centers is
investigated and the surveyed algorithms are classified according to the
classification. The goal of this paper is to provide a comprehensive and
comparative understanding of existing literature and aid researchers by
providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres
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