Loading...
Search for:
reshadi--m
0.01 seconds
Thickness optimization of polyurethane floor insulation based on analysis of the heat transfer in a multi-layer
, Article ASME 2014 12th Biennial Conference on Engineering Systems Design and Analysis, ESDA 2014 ; Vol. 3, issue , 2014 ; Saidi, M. H ; Reshadi, M ; Sharif University of Technology
Abstract
During the year, due to weather conditions, the temperature fluctuations at surface level cause problems in underground pipes as a result of freezing water. One of the best prevention strategies is the use of polyurethane floor insulation for keeping the temperature of clay above zero degrees Celsius. In this study to calculate the minimum thickness of polyurethane insulation layer, the differential equation of energy is solved based on principle of separation of variables using imaginary eigenvalues for consistency with the temperature distribution in multi-layer consist of asphalt, gravel and polyurethane with finite thickness and clay as a semiinfinite medium with periodic thermal...
HybSMRP: a hybrid scheduling algorithm in Hadoop MapReduce framework
, Article Journal of Big Data ; Volume 6, Issue 1 , 2019 ; 21961115 (ISSN) ; Reshadi, M ; Movaghar, A ; Khademzadeh, A ; Sharif University of Technology
Springer
2019
Abstract
Due to the advent of new technologies, devices, and communication tools such as social networking sites, the amount of data produced by mankind is growing rapidly every year. Big data is a collection of large datasets that cannot be processed using traditional computing techniques. MapReduce has been introduced to solve large-data computational problems. It is specifically designed to run on commodity hardware, and it depends on dividing and conquering principles. Nowadays, the focus of researchers has shifted towards Hadoop MapReduce. One of the most outstanding characteristics of MapReduce is data locality-aware scheduling. Data locality-aware scheduler is a further efficient solution to...
Designing a MapReduce performance model in distributed heterogeneous platforms based on benchmarking approach
, Article Journal of Supercomputing ; Volume 76, Issue 9 , 2020 , Pages 7177-7203 ; Movaghar, A ; Reshadi, M ; Khademzadeh, A ; Sharif University of Technology
Springer
2020
Abstract
MapReduce framework is an effective method for big data parallel processing. Enhancing the performance of MapReduce clusters, along with reducing their job execution time, is a fundamental challenge to this approach. In fact, one is faced with two challenges here: how to maximize the execution overlap between jobs and how to create an optimum job scheduling. Accordingly, one of the most critical challenges to achieving these goals is developing a precise model to estimate the job execution time due to the large number and high volume of the submitted jobs, limited consumable resources, and the need for proper Hadoop configuration. This paper presents a model based on MapReduce phases for...