2,818 research outputs found
A Taxonomy of Workflow Management Systems for Grid Computing
With the advent of Grid and application technologies, scientists and
engineers are building more and more complex applications to manage and process
large data sets, and execute scientific experiments on distributed resources.
Such application scenarios require means for composing and executing complex
workflows. Therefore, many efforts have been made towards the development of
workflow management systems for Grid computing. In this paper, we propose a
taxonomy that characterizes and classifies various approaches for building and
executing workflows on Grids. We also survey several representative Grid
workflow systems developed by various projects world-wide to demonstrate the
comprehensiveness of the taxonomy. The taxonomy not only highlights the design
and engineering similarities and differences of state-of-the-art in Grid
workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure
A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments
In recent years, due to the unnecessary wastage of electrical energy in
residential buildings, the requirement of energy optimization and user comfort
has gained vital importance. In the literature, various techniques have been
proposed addressing the energy optimization problem. The goal of each technique
was to maintain a balance between user comfort and energy requirements such
that the user can achieve the desired comfort level with the minimum amount of
energy consumption. Researchers have addressed the issue with the help of
different optimization algorithms and variations in the parameters to reduce
energy consumption. To the best of our knowledge, this problem is not solved
yet due to its challenging nature. The gap in the literature is due to the
advancements in the technology and drawbacks of the optimization algorithms and
the introduction of different new optimization algorithms. Further, many newly
proposed optimization algorithms which have produced better accuracy on the
benchmark instances but have not been applied yet for the optimization of
energy consumption in smart homes. In this paper, we have carried out a
detailed literature review of the techniques used for the optimization of
energy consumption and scheduling in smart homes. The detailed discussion has
been carried out on different factors contributing towards thermal comfort,
visual comfort, and air quality comfort. We have also reviewed the fog and edge
computing techniques used in smart homes
Scheduling in Grid Computing Environment
Scheduling in Grid computing has been active area of research since its
beginning. However, beginners find very difficult to understand related
concepts due to a large learning curve of Grid computing. Thus, there is a need
of concise understanding of scheduling in Grid computing area. This paper
strives to present concise understanding of scheduling and related
understanding of Grid computing system. The paper describes overall picture of
Grid computing and discusses important sub-systems that enable Grid computing
possible. Moreover, the paper also discusses concepts of resource scheduling
and application scheduling and also presents classification of scheduling
algorithms. Furthermore, the paper also presents methodology used for
evaluating scheduling algorithms including both real system and simulation
based approaches. The presented work on scheduling in Grid containing concise
understandings of scheduling system, scheduling algorithm, and scheduling
methodology would be very useful to users and researchersComment: Fourth International Conference on Advanced Computing & Communication
Technologies (ACCT), 201
Parallel memetic algorithms for independent job scheduling in computational grids
In this chapter we present parallel implementations of Memetic Algorithms (MAs) for the problem of scheduling independent jobs in computational grids. The problem of scheduling in computational grids is known for its high demanding computational time. In this work we exploit the intrinsic parallel nature of MAs as well as the fact that computational grids offer large amount of resources, a part of which could be used to compute the efficient allocation of jobs to grid resources.
The parallel models exploited in this work for MAs include both fine-grained and coarse-grained parallelization and their hybridization. The resulting schedulers have been tested through different grid scenarios generated by a grid simulator to match different possible configurations of computational grids in terms of size (number of jobs and resources) and computational characteristics of resources. All in all, the result of this work showed that Parallel MAs are very good alternatives in order to match different performance requirement on fast scheduling of jobs to grid resources.Peer ReviewedPostprint (author's final draft
Modelling of user requirements and behaviors in computational grids
In traditional distributed computing systems a few user types are found having ratherPeer ReviewedPostprint (published version
Performance Evaluation of Dynamic Scheduling for Grid Systems
Schedulers are applications responsible for job management including resource allocation for a specific job, splitting them to ensure parallel task execution, data management, event correlation, and service-level management capabilities. When Grids allotted a number of jobs, such applications have to consider the overhead time, cost regarding to and from Grid resources, job transmission and at job processing, Grid resources for allocation of the jobs. In this paper, it is proposed to investigate the performance of dynamic scheduling algorithm of schedulers for executing different number of tasks is evaluated
Genetic Algorithm Approach for Implementation of Job Scheduling Problem
A job scheduling maps and schedules the virtual machine (VM) resources to physical machines (VM) for getting the finest mapping result to achieve the proper system load balance. Job scheduling system tries to find the best suitable schedule in a system for VMs and PMs, by considering various on time restrictions into concern. The ultimate goal of job scheduling is to schedule adaptable virtual machines to physical machines, getting a suitable order in order to enhance resource utility. This research paper proposes an approach in order to discuss a Job Scheduling problem to progress resource utility with the help of Genetic Algorithm (GA).
DOI: 10.17762/ijritcc2321-8169.15067
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