scholarly journals Massive Speech Recognition Resource Scheduling System based on Grid Computing

2022 ◽  
Vol 16 ◽  
pp. 181-190
Author(s):  
Shanshan Yang ◽  
Jinjin Chao
Keyword(s):  
Speech Recognition ◽  
Grid Computing ◽  
Large Scale ◽  
Resource Scheduling ◽  
Information Resources ◽  
Experimental Results ◽  
Scheduling System ◽  
Exchange Of Information ◽  
Information Scheduling ◽  
Speed And Accuracy

Nowadays, there are too many large-scale speech recognition resources, which makes it difficult to ensure the scheduling speed and accuracy. In order to improve the effect of large-scale speech recognition resource scheduling, a large-scale speech recognition resource scheduling system based on grid computing is designed in this paper. In the hardware part, microprocessor, Ethernet control chip, controller and acquisition card are designed. In the software part of the system, it mainly carries out the retrieval and exchange of information resources, so as to realize the information scheduling of the same type of large-scale speech recognition resources. The experimental results show that the information scheduling time of the designed system is short, up to 2.4min, and the scheduling accuracy is high, up to 90%, in order to provide some help to effectively improve the speed and accuracy of information scheduling.

scholarly journals Sim-DRS: a similarity-based dynamic resource scheduling algorithm for microservice-based web systems

2021 ◽  
Vol 7 ◽  
pp. e824
Author(s):  
Yiren Li ◽  
Tieke Li ◽  
Pei Shen ◽  
Liang Hao ◽  
Wenjing Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Web Applications ◽  
State Of The Art ◽  
Scheduling Algorithm ◽  
Resource Scheduling ◽  
Experimental Results ◽  
Heuristic Scheduling ◽  
Dynamic Resource ◽  
Web Systems

Microservice-based Web Systems (MWS), which provide a fundamental infrastructure for constructing large-scale cloud-based Web applications, are designed as a set of independent, small and modular microservices implementing individual tasks and communicating with messages. This microservice-based architecture offers great application scalability, but meanwhile incurs complex and reactive autoscaling actions that are performed dynamically and periodically based on current workloads. However, this problem has thus far remained largely unexplored. In this paper, we formulate a problem of Dynamic Resource Scheduling for Microservice-based Web Systems (DRS-MWS) and propose a similarity-based heuristic scheduling algorithm that aims to quickly find viable scheduling schemes by utilizing solutions to similar problems. The performance superiority of the proposed scheduling solution in comparison with three state-of-the-art algorithms is illustrated by experimental results generated through a well-known microservice benchmark on disparate computing nodes in public clouds.

Scheduling System for Cloud Federation across Multi-Data Center

2013 ◽  
Vol 457-458 ◽  
pp. 839-843
Author(s):  
Meng Di Yao ◽  
Dong Lin Chen ◽  
Xin Chen
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Large Scale ◽  
Resource Scheduling ◽  
Web Server ◽  
Scheduling Algorithms ◽  
Scheduling System ◽  
Key Technology ◽  
Major Function ◽  
Cloud Federation

In the cloud computing federation, the method of cloud computing federation resource scheduling has been introduced to allocate the users requested tasks reasonably to the providers. In the present, single cloud providers method and system of resource scheduling dont apply to the cloud federation environment. Therefore, the solution to the problem of resource scheduling in cloud federation cross data center has become a key technology. The system, mainly about the resource scheduling algorithms across data center, presents the framework and major function of the cloud federation environment cross the datacenter. Furthermore, through Cloudsim, a Web server based system platform was built. Finally, the system proved that it can meet the complicated large-scale demand of users as well as increase the efficiency and profit of resource scheduling among the providers in cloud federation.

Phoneme-to-Grapheme Conversion Based Large-Scale Pre-Training for End-to-End Automatic Speech Recognition

2020 ◽  
Author(s):  
Ryo Masumura ◽  
Naoki Makishima ◽  
Mana Ihori ◽  
Akihiko Takashima ◽  
Tomohiro Tanaka ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Large Scale ◽  
End To End

Computational fluid dynamics of rectangular external loop airlift reactor

2020 ◽  
Vol 18 (5-6) ◽  
Author(s):  
Shivanand M. Teli ◽  
Channamallikarjun S. Mathpati
Keyword(s):  
Large Scale ◽  
Lift Coefficient ◽  
Volume Ratio ◽  
Reynolds Stress Model ◽  
Airlift Reactor ◽  
Experimental Results ◽  
Turbulent Dispersion ◽  
Drag Forces ◽  
External Loop ◽  
Turbulent Models

AbstractThe novel design of a rectangular external loop airlift reactor is at present the most used large-scale reactor for microalgae culture. It has a unique future for a large surface to volume ratio for exposure of light radiation for photosynthesis reaction. The 3D simulations have been performed in rectangular EL-ALR. The Eulerian–Eulerian approach has been used with a dispersed gas phase for different turbulent models. The performance and applicability of different turbulent model’s i.e., K-epsilon standard, K-epsilon realizable, K-omega, and Reynolds stress model are used and compared with experimental results. All drag forces and non-drag forces (turbulent dispersion, virtual mass, and lift coefficient) are included in the model. The experimental values of overall gas hold-up and average liquid circulation velocity have been compared with simulation and literature results. It is seemed to give good agreements. For the different elevations in the downcomer section, liquid axial velocity, turbulent kinetic energy, and turbulent eddy dissipation experimental have been compared with different turbulent models. The K-epsilon Realizable model gives better prediction with experimental results.

scholarly journals Evaluation of recent advances in recommender systems on Arabic content

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mehdi Srifi ◽  
Ahmed Oussous ◽  
Ayoub Ait Lahcen ◽  
Salma Mouline
Keyword(s):  
Recommender Systems ◽  
High Performance ◽  
Large Scale ◽  
State Of The Art ◽  
Experimental Results ◽  
Recent Advances ◽  
Research Gap ◽  
Text Preprocessing

AbstractVarious recommender systems (RSs) have been developed over recent years, and many of them have concentrated on English content. Thus, the majority of RSs from the literature were compared on English content. However, the research investigations about RSs when using contents in other languages such as Arabic are minimal. The researchers still neglect the field of Arabic RSs. Therefore, we aim through this study to fill this research gap by leveraging the benefit of recent advances in the English RSs field. Our main goal is to investigate recent RSs in an Arabic context. For that, we firstly selected five state-of-the-art RSs devoted originally to English content, and then we empirically evaluated their performance on Arabic content. As a result of this work, we first build four publicly available large-scale Arabic datasets for recommendation purposes. Second, various text preprocessing techniques have been provided for preparing the constructed datasets. Third, our investigation derived well-argued conclusions about the usage of modern RSs in the Arabic context. The experimental results proved that these systems ensure high performance when applied to Arabic content.

Local Graph Edge Partitioning

2021 ◽  
Vol 12 (5) ◽  
pp. 1-25
Author(s):  
Shengwei Ji ◽  
Chenyang Bu ◽  
Lei Li ◽  
Xindong Wu
Keyword(s):  
Real World ◽  
Graph Partitioning ◽  
Large Scale ◽  
Complete Information ◽  
Local Information ◽  
Experimental Results ◽  
Two Stage ◽  
Graph Computation ◽  
Local Graph ◽  
Edge Partitioning

Graph edge partitioning, which is essential for the efficiency of distributed graph computation systems, divides a graph into several balanced partitions within a given size to minimize the number of vertices to be cut. Existing graph partitioning models can be classified into two categories: offline and streaming graph partitioning models. The former requires global graph information during the partitioning, which is expensive in terms of time and memory for large-scale graphs. The latter creates partitions based solely on the received graph information. However, the streaming model may result in a lower partitioning quality compared with the offline model. Therefore, this study introduces a Local Graph Edge Partitioning model, which considers only the local information (i.e., a portion of a graph instead of the entire graph) during the partitioning. Considering only the local graph information is meaningful because acquiring complete information for large-scale graphs is expensive. Based on the Local Graph Edge Partitioning model, two local graph edge partitioning algorithms—Two-stage Local Partitioning and Adaptive Local Partitioning—are given. Experimental results obtained on 14 real-world graphs demonstrate that the proposed algorithms outperform rival algorithms in most tested cases. Furthermore, the proposed algorithms are proven to significantly improve the efficiency of the real graph computation system GraphX.

scholarly journals PeerFlow: Secure Load Balancing in Tor

2017 ◽  
Vol 2017 (2) ◽  
pp. 74-94 ◽  
Author(s):  
Aaron Johnson ◽  
Rob Jansen ◽  
Nicholas Hopper ◽  
Aaron Segal ◽  
Paul Syverson
Keyword(s):  
Load Balancing ◽  
Load Balance ◽  
Large Scale ◽  
Scanning System ◽  
Large Scale Network ◽  
Improve Accuracy ◽  
Scale Network ◽  
Network Simulations ◽  
Improved Design ◽  
Speed And Accuracy

Abstract We present PeerFlow, a system to securely load balance client traffic in Tor. Security in Tor requires that no adversary handle too much traffic. However, Tor relays are run by volunteers who cannot be trusted to report the relay bandwidths, which Tor clients use for load balancing. We show that existing methods to determine the bandwidths of Tor relays allow an adversary with little bandwidth to attack large amounts of client traffic. These methods include Tor’s current bandwidth-scanning system, TorFlow, and the peer-measurement system EigenSpeed. We present an improved design called PeerFlow that uses a peer-measurement process both to limit an adversary’s ability to increase his measured bandwidth and to improve accuracy. We show our system to be secure, fast, and efficient. We implement PeerFlow in Tor and demonstrate its speed and accuracy in large-scale network simulations.

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