scholarly journals MODELS FOR PREDICTING THE EXECUTION TIME OF PROBLEM-SOLVING SCHEMES IN A HETEROGENEOUS DISTRIBUTED COMPUTING ENVIRONMENT

2019 ◽  
pp. 102-108
Author(s):  
A.G. Feoktistov ◽  
O.Yu. Basharina
Keyword(s):  
Problem Solving ◽  
Distributed Computing ◽  
Execution Time ◽  
Computing Environment ◽  
Heterogeneous Distributed Computing

scholarly journals Predicting runtime of computational jobs in distributed computing environment

2020 ◽  
Author(s):  
A.G. Feoktistov ◽  
O.Yu. Basharina
Keyword(s):  
Problem Solving ◽  
Distributed Computing ◽  
Large Scale ◽  
System Software ◽  
Computing Environment ◽  
Use Of Resources ◽  
Software Packages ◽  
Applied Software ◽  
Hardware Architectures ◽  
Heterogeneous Distributed Computing

The paper addresses a relevant problem of predicting the runtime of jobs for executing problem-solving schemes of large-scale applications in a heterogeneous distributed computing environment. Such an environment includes nodes that have various hardware architectures, different system software, and diverse computational possibilities. We believe that increasing the accuracy in predicting the runtime of jobs can significantly improve the efficiency of problem-solving and rational use of resources in the heterogeneous environment. To this end, we propose new models that make it possible to take into account various estimations of the module runtime for all modules included in the problem-solving scheme. These models were developed using the special computational model of distributed applied software packages (large-scale scientific applications). In addition, we compare the prediction results (jobs runtime and their errors) using different estimations. Among them are the estimations obtained through the modules testing, users estimations, and estimations based on computational history. These results were obtained in continuous integration, delivery, and deployment of applied and system software of a package for solving warehouse logistics problems. They show that the largest accuracy is achieved by the modules testing.

scholarly journals A hybrid Method to augment the efficiency of Distributed Computing System by DAG -Using Finest Task Allocation with Dual Mode Processors

2019 ◽  
Vol 8 (11) ◽  
pp. 4275-4284
Keyword(s):  
Distributed Computing ◽  
Task Allocation ◽  
Scheduling Algorithm ◽  
Computing System ◽  
Service Rate ◽  
Additional Parameter ◽  
Computing Environment ◽  
Dual Mode ◽  
New Approach ◽  
Heterogeneous Distributed Computing

Distributed computing system creates or provides a platform having multiple computing nodes linked in a specified manner. On the basis of literature review of last few decades it has been noticed that most of distributed computing researchers have shown their effort to maintain load balancing between processors ,effective task scheduling and optimizing different parameters affecting execution cost and throughput .With these above scenario an additional parameter “Self reconfiguration of CPU” is also a countable parameter to augment the efficiency of distributed computing system .Through this research paper we want to present new approach of adaptive scheduling algorithm which is the mix output of effective task allocation to processor involved in computing and self-reconfiguration of those processors as per need of computing. By this proposed method we will optimize the execution cost, service rate and maximize the throughput as an outcome of organized processors consist in heterogeneous distributed computing system, resulting provide the considerable enhancement in the performance of Distributed computing environment.

scholarly journals Integration of Web Processing Services with Workflow-Based Scientific Applications for Solving Environmental Monitoring Problems

2021 ◽  
Vol 11 (1) ◽  
pp. 8
Author(s):  
Alexander Feoktistov ◽  
Sergey Gorsky ◽  
Roman Kostromin ◽  
Roman Fedorov ◽  
Igor Bychkov
Keyword(s):  
Distributed Computing ◽  
High Performance Computing ◽  
High Performance ◽  
Computing Environment ◽  
Scientific Application ◽  
Computational Load ◽  
Multidimensional Time Series ◽  
Critical Problems ◽  
Heterogeneous Distributed Computing ◽  
Performance Computing

Nowadays, developing and applying advanced digital technologies for monitoring protected natural territories are critical problems. Collecting, digitalizing, storing, and analyzing spatiotemporal data on various aspects of the life cycle of such territories play a significant role in monitoring. Often, data processing requires the utilization of high-performance computing. To this end, the paper addresses a new approach to automation of implementing resource-intensive computational operations of web processing services in a heterogeneous distributed computing environment. To implement such an operation, we develop a workflow-based scientific application executed under the control of a multi-agent system. Agents represent heterogeneous resources of the environment and distribute the computational load among themselves. Software development is realized in the Orlando Tools framework, which we apply to creating and operating problem-oriented applications. The advantages of the proposed approach are in integrating geographic information services and high-performance computing tools, as well as in increasing computation speedup, balancing computational load, and improving the efficiency of resource use in the heterogeneous distributed computing environment. These advantages are shown in analyzing multidimensional time series.

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