Toward a new computing model for an open distributed environment

1990 ◽  
pp. 261-268
Author(s):  
Mario Tokoro
Keyword(s):  
Distributed Environment ◽  
Computing Model

scholarly journals Enhanced Weighted Round Robin Load Balancing Algorithm in Cloud Computing

2019 ◽  
Vol 8 (9S2) ◽  
pp. 148-151
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Task Assignment ◽  
Round Robin ◽  
Utility Computing ◽  
Distributed Environment ◽  
Dynamic Task ◽  
Computing Model ◽  
Network Load ◽  
Load Balancing Algorithm

The cloud/utility computing model requires a dynamic task assignment to cloud sites with the goal that the performance and demand handling is done as effectively as would be prudent. Efficient load balancing and proper allocation of resources are vital systems to improve the execution of different services and make legitimate usage of existing assets in the cloud computing atmosphere. Consequently, the cloud-based infrastructure has numerous kinds of load concerns such as CPU load, server load, memory drain, network load, etc. Thus, an appropriate load balancing system helps in realizing failures, reducing backlog problems, adaptability, proper resource distribution, expanding dependability and client fulfillment and so forth in distributed environment. This thesis reviewed various popular load balancing algorithms. Modified round robin algorithms are popularly employed by various giant companies for scheduling issues and load balancing. An enhanced weighted round robin algorithm is discussed in this paper concentrating on efficient load balancing and effective task scheduling and resource management.

scholarly journals Comparison of Algorithms for W orkflow Applications in C loud C omputing

2021 ◽  
Vol 11 (1) ◽  
pp. 55-59
Author(s):  
Sriperambuduri Vinay Kumar ◽  
◽  
M. Nagaratna ◽  
Keyword(s):  
Cloud Computing ◽  
Web Applications ◽  
Service Providers ◽  
Research Trends ◽  
Workflow Scheduling ◽  
Distributed Environment ◽  
Iot Applications ◽  
Computing Model ◽  
Comprehensive Survey

Cloud computing model has evolved to deliver resources on pay per use model to businesses, service providers and end-users. Workflow scheduling has become one of the research trends in cloud computing as many applications in scientific, business, and big data processing can be expressed in the form of a workflow. The scheduling aims to execute scientific or synthetic workloads on the cloud by utilizing the resources by meeting QoS requirements, makespan, energy and cost. There has been extensive research in this area to schedule workflow applications in a distributed environment, to execute background tasks in IoT applications, event-driven and web applications. This paper focuses on the comprehensive survey and classification of workflow scheduling algorithms designed for the cloud.

Load Following Function of Fuel Cell Plant in Distributed Environment

2005 ◽  
Vol 1 (03) ◽  
pp. 285-290 ◽  
Author(s):  
F. González-Longatt ◽  
◽  
A. Hernandez ◽  
F. Guillen ◽  
C. Fortoul
Keyword(s):  
Fuel Cell ◽  
Distributed Environment ◽  
Load Following

Big Data Privacy Preservation Using Two Phase Top-Down Specialization Algorithm with Multidimensional Map Reduce Framework on Hadoop

2015 ◽  
Vol 3 (2) ◽  
Author(s):  
Shalin Eliabeth S. ◽  
Sarju S.
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Experimental Result ◽  
Map Reduce ◽  
Distributed Environment ◽  
Top Down ◽  
Two Phase ◽  
Data Anonymization ◽  
Big Data Privacy

Big data privacy preservation is one of the most disturbed issues in current industry. Sometimes the data privacy problems never identified when input data is published on cloud environment. Data privacy preservation in hadoop deals in hiding and publishing input dataset to the distributed environment. In this paper investigate the problem of big data anonymization for privacy preservation from the perspectives of scalability and time factor etc. At present, many cloud applications with big data anonymization faces the same kind of problems. For recovering this kind of problems, here introduced a data anonymization algorithm called Two Phase Top-Down Specialization (TPTDS) algorithm that is implemented in hadoop. For the data anonymization-45,222 records of adults information with 15 attribute values was taken as the input big data. With the help of multidimensional anonymization in map reduce framework, here implemented proposed Two-Phase Top-Down Specialization anonymization algorithm in hadoop and it will increases the efficiency on the big data processing system. By conducting experiment in both one dimensional and multidimensional map reduce framework with Two Phase Top-Down Specialization algorithm on hadoop, the better result shown in multidimensional anonymization on input adult dataset. Data sets is generalized in a top-down manner and the better result was shown in multidimensional map reduce framework by the better IGPL values generated by the algorithm. The anonymization was performed with specialization operation on taxonomy tree. The experiment shows that the solutions improves the IGPL values, anonymity parameter and decreases the execution time of big data privacy preservation by compared to the existing algorithm. This experimental result will leads to great application to the distributed environment.

Urban Railway Bridge Fast Evaluation Based on Cloud Computing

2011 ◽  
Vol 71-78 ◽  
pp. 4501-4505
Author(s):  
Ming Chen ◽  
Wan Zhou
Keyword(s):  
Neural Networks ◽  
Cloud Computing ◽  
Model Updating ◽  
Condition Assessment ◽  
Fe Model ◽  
Railway Bridge ◽  
Fast Evaluation ◽  
Railway Bridges ◽  
Computing Model ◽  
Bridge Condition Assessment

Although modern bridge are carefully designed and well constructed, damage may occur in them due to unexpected causes. Currently, many different techniques have been proposed and investigated in bridge condition assessment. However, evaluation efficiency of condition assessment has not been paid much attention by the researchers. A fast evaluation of the urban railway bridge condition based on the cloud computing is presented. In this paper dynamic FE model and Artificial neural networks technique is applied to model updating. The cloud computing model provides the basis for fast analyses. It was found that when applied to the actually railway bridges, the proposed method provided results similar to those obtained by experts, but can improve efficiency of bridge

Video Streaming Platform Using Distributed Environment In Cloud Platform

2021 ◽  
Author(s):  
Mythresh Korupolu ◽  
Srikanth Jannabhatla ◽  
Venkata Surendra Kommineni ◽  
Hemanth Kalyanam ◽  
Vijaykumar Vasantham
Keyword(s):  
Video Streaming ◽  
Cloud Platform ◽  
Distributed Environment
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