Resource Selection in Large-Scale Distributed System Using Dynamic Task Sharing

2010 ◽  
Author(s):  
Dae Gun Kim ◽  
Zhong Yuan Li ◽  
Sung Soo Moon ◽  
Hee Yong Youn
Keyword(s):  
Distributed System ◽  
Resource Selection ◽  
Large Scale ◽  
Task Sharing ◽  
Dynamic Task

scholarly journals Optimization of Resource Control for Transitions in Complex Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Florin Pop
Keyword(s):  
Complex Systems ◽  
Distributed System ◽  
Resource Selection ◽  
Large Scale ◽  
Optimization Problem ◽  
Fitness Function ◽  
Industrial Process ◽  
Optimal Solution ◽  
Resource Control ◽  
Open Issue

In complex systems like Large-Scale Distributed Systems (LSDSs) the optimization of resource control is an open issue. The large number of resources and multicriteria optimization requirements make the optimization problem a complex one. The importance of resource control increases with the need of use for industrial process and manufacturing, being a key solution for QoS assuring. This paper presents different solutions for multiobjective decentralized control models for tasks assignment in LSDS. The transaction in real-time complex system is modeled in simulation by tasks which will be scheduled and executed in a distributed system, so a set of specifications and requirements are known. The paper presents a critical analysis of existing solutions and focuses on a genetic-based algorithm for optimization. The contribution of the algorithm is the fitness function that includes multiobjective criteria for optimization in different way. Several experimental scenarios, modeled using simulation, were considered to offer a support for analysis of near-optimal solution for resource selection.

scholarly journals Leveraging Edge Intelligence for Video Analytics in Smart City Applications

Information ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 14
Author(s):  
Aluizio Rocha Neto ◽  
Thiago P. Silva ◽  
Thais Batista ◽  
Flávia C. Delicato ◽  
Paulo F. Pires ◽  
...  
Keyword(s):  
Distributed System ◽  
Smart City ◽  
Large Scale ◽  
Facial Recognition ◽  
Public Spaces ◽  
Hardware Accelerators ◽  
Video Streams ◽  
Video Analytics ◽  
Learning Tasks ◽  
Naive Approach

In smart city scenarios, the huge proliferation of monitoring cameras scattered in public spaces has posed many challenges to network and processing infrastructure. A few dozen cameras are enough to saturate the city’s backbone. In addition, most smart city applications require a real-time response from the system in charge of processing such large-scale video streams. Finding a missing person using facial recognition technology is one of these applications that require immediate action on the place where that person is. In this paper, we tackle these challenges presenting a distributed system for video analytics designed to leverage edge computing capabilities. Our approach encompasses architecture, methods, and algorithms for: (i) dividing the burdensome processing of large-scale video streams into various machine learning tasks; and (ii) deploying these tasks as a workflow of data processing in edge devices equipped with hardware accelerators for neural networks. We also propose the reuse of nodes running tasks shared by multiple applications, e.g., facial recognition, thus improving the system’s processing throughput. Simulations showed that, with our algorithm to distribute the workload, the time to process a workflow is about 33% faster than a naive approach.

Discovering Complex Relationships of Drugs over Distributed Knowledgebases

2016 ◽  
pp. 1572-1591
Author(s):  
Juan Li ◽  
Ranjana Sharma ◽  
Yan Bai
Keyword(s):  
Drug Discovery ◽  
Semantic Web ◽  
Distributed System ◽  
Large Scale ◽  
The Internet ◽  
Full Potential ◽  
Web Data ◽  
Semantic Relationships ◽  
Complex Relationships

Drug discovery is a lengthy, expensive and difficult process. Indentifying and understanding the hidden relationships among drugs, genes, proteins, and diseases will expedite the process of drug discovery. In this paper, we propose an effective methodology to discover drug-related semantic relationships over large-scale distributed web data in medicine, pharmacology and biotechnology. By utilizing semantic web and distributed system technologies, we developed a novel hierarchical knowledge abstraction and an efficient relation discovery protocol. Our approach effectively facilitates the realization of the full potential of harnessing the collective power and utilization of the drug-related knowledge scattered over the Internet.

Synchronizing Execution of Big Data in Distributed and Parallelized Environments

Big Data ◽  
2016 ◽  
pp. 1555-1581
Author(s):  
Gueyoung Jung ◽  
Tridib Mukherjee
Keyword(s):  
Big Data ◽  
Distributed System ◽  
Data Analytics ◽  
High Performance ◽  
Large Scale ◽  
Big Data Analytics ◽  
Loosely Coupled ◽  
Current Trends ◽  
Distributed Computing Infrastructures ◽  
Performance Computing

In the modern information era, the amount of data has exploded. Current trends further indicate exponential growth of data in the future. This prevalent humungous amount of data—referred to as big data—has given rise to the problem of finding the “needle in the haystack” (i.e., extracting meaningful information from big data). Many researchers and practitioners are focusing on big data analytics to address the problem. One of the major issues in this regard is the computation requirement of big data analytics. In recent years, the proliferation of many loosely coupled distributed computing infrastructures (e.g., modern public, private, and hybrid clouds, high performance computing clusters, and grids) have enabled high computing capability to be offered for large-scale computation. This has allowed the execution of the big data analytics to gather pace in recent years across organizations and enterprises. However, even with the high computing capability, it is a big challenge to efficiently extract valuable information from vast astronomical data. Hence, we require unforeseen scalability of performance to deal with the execution of big data analytics. A big question in this regard is how to maximally leverage the high computing capabilities from the aforementioned loosely coupled distributed infrastructure to ensure fast and accurate execution of big data analytics. In this regard, this chapter focuses on synchronous parallelization of big data analytics over a distributed system environment to optimize performance.

Distribution of date elements and its relationship to the types of digital libraries: Analysing aggregated metadata of a Polish large-scale distributed system

2017 ◽  
Vol 51 (3) ◽  
pp. 710-720
Author(s):  
Veslava Osinska ◽  
Krystyna K. Matusiak ◽  
Malgorzata Kowalska ◽  
Bozena Bednarek-Michalska ◽  
Piotr Malak
Keyword(s):  
Distributed System ◽  
Digital Libraries ◽  
Large Scale ◽  
Big Data Analysis ◽  
Resource Discovery ◽  
Visual Methods ◽  
Systematic Classification ◽  
Knowledge Growth ◽  
Digital Knowledge

Large-scale distributed digital library systems with aggregated metadata provide platforms for resource discovery and retrieval. For researchers, aggregated metadata offers a potential for big data analysis and exploration of digital knowledge growth. The paper reports the findings of the study that investigated the distribution of the date elements in the metadata aggregated in the Polish Federation of Digital Libraries and related it to the types of libraries. The purpose of this study was to address the gap in research about heterogeneous digital libraries and explore the dynamics of their growth. The authors included timeline characteristics of the development of Polish digital libraries and proposed a new dynamics parameter – resource release interval. They used histograms, which have been grouped according to the organizational and thematic criteria, developed for this study. All charts are characterized by two similar maximum points. Their shapes and ratio have been analysed by both statistical and visual methods. The shape of resource release interval charts revealed characteristic differences for libraries types. The proposed approach, based on time characteristics, is an important step in the development of systematic classification of digital libraries and digitizing institutions. It can be also considered as a new tool in monitoring the dynamics of digital knowledge growth.

Resource selection in a high-altitude rangeland equid, the kiang (Equus kiang): influence of forage abundance and quality at multiple spatial scales

2014 ◽  
Vol 92 (3) ◽  
pp. 239-249 ◽  
Author(s):  
Antoine St-Louis ◽  
Steeve D. Côté
Keyword(s):  
High Altitude ◽  
Resource Selection ◽  
Large Scale ◽  
Plant Biomass ◽  
Spatial Scales ◽  
Plant Quality ◽  
The Tibetan Plateau ◽  
Higher Plant ◽  
Feeding Site ◽  
Spatial And Temporal Scales

Herbivores foraging in arid and seasonal environments often face choices between plant patches varying in abundance and nutritional quality at several spatial and temporal scales. Because of their noncompartmented digestive system, equids typically rely on abundant forage to meet their nutrient requirements. In forage-limited environments, therefore, scarcity of food resources represents a challenge for wild equids. We investigated hierarchical resource-selection patterns of kiangs (Equus kiang Moorcroft, 1841), a wild equid inhabiting the high-altitude steppes of the Tibetan Plateau, hypothesizing that vegetation abundance would be the main factor driving resource selection at a large scale and that plant quality would influence resource selection at finer scales. We investigated resource-selection patterns at three spatial levels (habitat, feeding site, and plant (vegetation groups, i.e., grasses, sedges, forbs, and shrubs)) during summer and fall. At the habitat level, kiangs selected both mesic and xeric habitats in summer and only xeric habitats (plains) during fall. At the feeding-site level, feeding sites had higher plant biomass and percentage of green foliage than random sites in the same habitats. At the plant level, grasses were selected over forbs and shrubs, and sedges were used in proportion to their availability during all seasons. Our results indicate that resource-selection patterns in kiangs vary across scales and that both forage abundance and quality play a role in resource selection. Plant quality appeared more important than hypothesized, possibly to increase daily nutrient intake in forage-limited and highly seasonal high-altitude rangelands.

A distributed system for large-scale n-gram language models at Tencent

2019 ◽  
Vol 12 (12) ◽  
pp. 2206-2217
Author(s):  
Qiang Long ◽  
Wei Wang ◽  
Jinfu Deng ◽  
Song Liu ◽  
Wenhao Huang ◽  
...  
Keyword(s):  
Distributed System ◽  
Large Scale ◽  
Language Models ◽  
N Gram
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