scholarly journals Fuzzy Theory-Based Data Placement for Scientific Workflows in Hybrid Cloud Environments

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zheyi Chen ◽  
Xu Zhao ◽  
Bing Lin
Keyword(s):  
Data Transmission ◽  
Data Centers ◽  
Fuzzy Theory ◽  
Data Placement ◽  
Scientific Workflow ◽  
Scientific Workflows ◽  
Transmission Time ◽  
Fuzzy Data ◽  
Hybrid Cloud ◽  
Cloud Environments

In hybrid cloud environments, reasonable data placement strategies are critical to the efficient execution of scientific workflows. Due to various loads, bandwidth fluctuations, and network congestions between different data centers as well as the dynamics of hybrid cloud environments, the data transmission time is uncertain. Thus, it poses huge challenges to the efficient data placement for scientific workflows. However, most of the traditional solutions for data placement focus on deterministic cloud environments, which lead to the excessive data transmission time of scientific workflows. To address this problem, we propose an adaptive discrete particle swarm optimization algorithm based on the fuzzy theory and genetic algorithm operators (DPSO-FGA) to minimize the fuzzy data transmission time of scientific workflows. The DPSO-FGA can rationally place the scientific workflow data while meeting the requirements of data privacy and the capacity limitations of data centers. Simulation results show that the DPSO-FGA can effectively reduce the fuzzy data transmission time of scientific workflows in hybrid cloud environments.

A Data Placement Strategy for Scientific Workflow in Hybrid Cloud

2018 ◽  
Author(s):  
Zhanghui Liu ◽  
Tao Xiang ◽  
Bing Lin ◽  
Xinshu Ye ◽  
Haijiang Wang ◽  
...  
Keyword(s):  
Data Placement ◽  
Scientific Workflow ◽  
Hybrid Cloud

scholarly journals Bi-objective scheduling algorithm for scientific workflows on cloud computing platform with makespan and monetary cost minimization approach

2021 ◽  
Author(s):  
Mirsaeid Hosseini Shirvani ◽  
Reza Noorian Talouki
Keyword(s):  
Cost Minimization ◽  
Scheduling Algorithm ◽  
Scientific Workflow ◽  
Scientific Workflows ◽  
Discrete Problem ◽  
Hybrid Cloud ◽  
Monetary Cost ◽  
Computing Platform ◽  
Cloud Architecture ◽  
Private And Public

AbstractScheduling of scientific workflows on hybrid cloud architecture, which contains private and public clouds, is a challenging task because schedulers should be aware of task inter-dependencies, underlying heterogeneity, cost diversity, and virtual machine (VM) variable configurations during the scheduling process. On the one side, reaching a minimum total execution time or makespan is a favorable issue for users whereas the cost of utilizing quicker VMs may lead to conflict with their budget on the other side. Existing works in the literature scarcely consider VM’s monetary cost in the scheduling process but mainly focus on makespan. Therefore, in this paper, the problem of scientific workflow scheduling running on hybrid cloud architecture is formulated to a bi-objective optimization problem with makespan and monetary cost minimization viewpoint. To address this combinatorial discrete problem, this paper presents a hybrid bi-objective optimization based on simulated annealing and task duplication algorithms (BOSA-TDA) that exploits two important heuristics heterogeneous earliest finish time (HEFT) and duplication techniques to improve canonical SA. The extensive simulation results reported of running different well-known scientific workflows such as LIGO, SIPHT, Cybershake, Montage, and Epigenomics demonstrate that proposed BOSA-TDA has the amount of 12.5%, 14.5%, 17%, 13.5%, and 18.5% average improvement against other existing approaches in terms of makespan, monetary cost, speed up, SLR, and efficiency metrics, respectively.

scholarly journals Cost and Time Economical Planning Algorithm for Scientific Workflows in Cloud Computing

2021 ◽  
Vol 13 (10) ◽  
pp. 263
Author(s):  
Jabanjalin Hilda ◽  
Srimathi Chandrasekaran
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Critical Path ◽  
Heterogeneous Systems ◽  
Scientific Workflow ◽  
Scientific Workflows ◽  
Cloud Data ◽  
Finish Time ◽  
Efficient Planning ◽  
Planning Algorithm

A heterogeneous system can be portrayed as a variety of unlike resources that can be locally or geologically spread, which is exploited to implement data-intensive and computationally intensive applications. The competence of implementing the scientific workflow applications on heterogeneous systems is determined by the approaches utilized to allocate the tasks to the proper resources. Cost and time necessity are evolving as different vital concerns of cloud computing environments such as data centers. In the area of scientific workflows, the difficulties of increased cost and time are highly challenging, as they elicit rigorous computational tasks over the communication network. For example, it was discovered that the time to execute a task in an unsuited resource consumes more cost and time in the cloud data centers. In this paper, a new cost- and time-efficient planning algorithm for scientific workflow scheduling has been proposed for heterogeneous systems in the cloud based upon the Predict Optimistic Time and Cost (POTC). The proposed algorithm computes the rank based not only on the completion time of the current task but also on the successor node in the critical path. Under a tight deadline, the running time of the workflow and the transfer cost are reduced by using this technique. The proposed approach is evaluated using true cases of data-exhaustive workflows compared with other algorithms from written works. The test result shows that our proposed method can remarkably decrease the cost and time of the experimented workflows while ensuring a better mapping of the task to the resource. In terms of makespan, speedup, and efficiency, the proposed algorithm surpasses the current existing algorithms—such as Endpoint communication contention-aware List Scheduling Heuristic (ELSH)), Predict Earliest Finish Time (PEFT), Budget-and Deadline-constrained heuristic-based upon HEFT (BDHEFT), Minimal Optimistic Processing Time (MOPT) and Predict Earlier Finish Time (PEFT)—while holding the same time complexity.

scholarly journals Monitoring of Grid Scientific Workflows

2008 ◽  
Vol 16 (2-3) ◽  
pp. 205-216
Author(s):  
Bartosz Balis ◽  
Marian Bubak ◽  
Bartłomiej Łabno
Keyword(s):  
Real Life ◽  
Scientific Workflow ◽  
Point Of View ◽  
Scientific Workflows ◽  
Monitoring Data ◽  
Monitoring Point ◽  
Loosely Coupled ◽  
Provenance Data ◽  
On Line ◽  
Monitoring Architecture

Scientific workflows are a means of conducting in silico experiments in modern computing infrastructures for e-Science, often built on top of Grids. Monitoring of Grid scientific workflows is essential not only for performance analysis but also to collect provenance data and gather feedback useful in future decisions, e.g., related to optimization of resource usage. In this paper, basic problems related to monitoring of Grid scientific workflows are discussed. Being highly distributed, loosely coupled in space and time, heterogeneous, and heavily using legacy codes, workflows are exceptionally challenging from the monitoring point of view. We propose a Grid monitoring architecture for scientific workflows. Monitoring data correlation problem is described and an algorithm for on-line distributed collection of monitoring data is proposed. We demonstrate a prototype implementation of the proposed workflow monitoring architecture, the GEMINI monitoring system, and its use for monitoring of a real-life scientific workflow.

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