scholarly journals A BRANCH AND BOUND ALGORITHM FOR WORKFLOW SCHEDULING

2018 ◽  
Vol 56 (2) ◽  
pp. 246
Author(s):  
Phan Thanh Toan ◽  
Nguyen The Loc
Keyword(s):  
Branch And Bound ◽  
Large Scale ◽  
Resource Constraints ◽  
Data Transfer ◽  
Scheduling Algorithm ◽  
Large Data ◽  
Scientific Workflow ◽  
Branch And Bound Algorithm ◽  
Workflow Scheduling ◽  
Tools And Techniques

Nowadays, people are connected to the Internet and use different Cloud solutions to store, process and deliver data. The Cloud consists of a collection of virtual servers that promise to provision on-demand computational and storage resources when needed. Workflow data is becoming an ubiquitous term in both science and technology and there is a strong need for new  tools and techniques to process and analyze large-scale complex datasets that are growing exponentially. scientific workflow is a sequence of connected tasks with large data transfer from parent task to children tasks. Workflow scheduling is the activity of assigning tasks to execution on servers and satisfying resource constraints and this is an NP-hard problem. In this paper, we propose a scheduling algorithm for workflow data that is derived from the Branch and Bound Algorithm.

SWSA: A Hybrid Scientific Workflow Scheduling Algorithm based on Metaheuristic Approach in Cloud Computing Environment

2021 ◽  
pp. 2150035
Author(s):  
Leyli Abbasi ◽  
Hossien Momeni ◽  
Mehdi Yaghoubi
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Scheduling Algorithm ◽  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Initial Population ◽  
Large Set ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Ranking Tasks

The cloud computing environment with a set of distributed computing resources is a suitable platform for the execution of large-scale applications. One of these applications is scientific workflow applications in which a large set of interrelated tasks are executed for a certain purpose. Scientific workflow scheduling is one of the main challenges in this area, which aims at the optimal assignment of tasks to computational resources. Given the heterogeneity of cloud computing resources, the scientific workflow scheduling is an NP-Complete problem that can be solved by heuristic methods. In this paper, an improved evolutionary algorithm called Scientific Workflow Scheduling Algorithm (SWSA) for scheduling scientific workflows in the cloud will be provided by ranking tasks and improving the initial population of tasks. The objective of this algorithm is to create a balance and an improvement in the parameters of the execution cost and workflow execution completion time. In this proposed approach, a heuristic algorithm is used to rank and generate the initial population, which increases the convergence rate. The experimental results show that SWSA is more efficient in terms of cost and execution time compared with other approaches.

scholarly journals Thuật toán MODE giải bài toán lập lịch luồng công việc

2017 ◽  
pp. 51
Author(s):  
Phan Thanh Toàn Phan Thanh Toàn
Keyword(s):  
Information And Communication Technology ◽  
Communication Technology ◽  
Large Scale ◽  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Challenging Problem ◽  
On Demand ◽  
Local Extrema ◽  
Information And Communication ◽  
And Storage

Cloud computing is a new trend of information and communication technology that enables resource distribution and sharing at a large scale. The Cloud consists of a collection of virtual machine that promise to provision on-demand computational and storage resources when needed. End-users can access these resources via the Internet and have to pay only for their usage. Scheduling of scientific workflow applications on the Cloud is a challenging problem that has been the focus of many researchers for many years. In this work, we propose a novel algorithm for workflow scheduling that is derived from the Opposition-based Differential Evolution method. This algorithm does not only ensure fast convergence but it also averts getting trapped into local extrema. Our CloudSim-based simulations show that our algorithm is superior to its predecessors. Moreover, the deviation of its solution from the optimal one is negligible.

scholarly journals Hybrid Balanced Task Clustering Algorithm for Scientific Workflows in Cloud Computing

2019 ◽  
Vol 20 (2) ◽  
pp. 237-258
Author(s):  
Avinash Kaur ◽  
Pooja Gupta ◽  
Manpreet Singh
Keyword(s):  
Impact Factor ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Data Transfer ◽  
Scientific Workflow ◽  
Scientific Workflows ◽  
Coarse Grained ◽  
Clustering Methods ◽  
The Impact ◽  
Task Clustering

Scientific Workflow is a composition of both coarse-grained and fine-grained computational tasks displaying varying execution requirements. Large-scale data transfer is involved in scientific workflows, so efficient techniques are required to reduce the makespan of the workflow. Task clustering is an efficient technique used in such a scenario that involves combining multiple tasks with shorter execution time into a single cluster to be executed on a resource. This leads to a reduction of scheduling overheads in scientific workflows and thus improvement of performance. However available task clustering methods involve clustering the tasks horizontally without the consideration of the structure of tasks in a workflow. We propose hybrid balanced task clustering algorithm that uses the parameter of impact factor of workflows along with the structure of workflow. According to this technique, tasks can be considered for clustering either vertically or horizontally based on the value of the impact factor. This minimizes the system overheads and the makespan for execution of a workflow. A simulation based evaluation is performed on real workflows that shows the proposed algorithm is efficient in recommending clusters. It shows improvement of 5-10\% in makespan time of workflow depending on the type of workflow used.

Cost Effective Heuristic workflow scheduling algorithm in Cloud under Deadline Constraint

2019 ◽  
Vol 12 ◽  
Author(s):  
Jasraj Meena ◽  
Manu Vardhan
Keyword(s):  
Cloud Computing ◽  
Scheduling Algorithm ◽  
Cost Effective ◽  
Scientific Workflow ◽  
Scientific Workflows ◽  
Workflow Scheduling ◽  
Performance Variation ◽  
Acquisition Delay ◽  
Very High ◽  
Deadline Constraint

Cloud computing is used to deliver IT resources over the internet. Due to the popularity of cloud computing, nowadays, most of the scientific workflows are shifted towards this environment. There are lots of algorithms has been proposed in the literature to schedule scientific workflows in the cloud, but their execution cost is very high as well as they are not meeting the user-defined deadline constraint. This paper focuses on satisfying the userdefined deadline of a scientific workflow while minimizing the total execution cost. So, to achieve this, we have proposed a Cost-Effective under Deadline (CEuD) constraint workflow scheduling algorithm. The proposed CEuD algorithm considers all the essential features of Cloud and resolves the major issues such as performance variation, and acquisition delay. We have compared the proposed CEuD algorithm with the existing literature algorithms for scientific workflows (i.e., Montage, Epigenomics, and CyberShake) and getting better results for minimizing the overall execution cost of the workflow while satisfying the user-defined deadline.

scholarly journals An IoT-based task scheduling optimization scheme considering the deadline and cost-aware scientific workflow for cloud computing

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xiaojin Ma ◽  
Honghao Gao ◽  
Huahu Xu ◽  
Minjie Bian
Keyword(s):  
Task Scheduling ◽  
Large Scale ◽  
Scheduling Algorithm ◽  
Scientific Workflow ◽  
Scheduling Method ◽  
Cloud Environments ◽  
Deadline Constraints ◽  
Minimum Completion Time ◽  
Acquisition Delay ◽  
Dynamic Acquisition

Abstract Large-scale applications of Internet of things (IoT), which require considerable computing tasks and storage resources, are increasingly deployed in cloud environments. Compared with the traditional computing model, characteristics of the cloud such as pay-as-you-go, unlimited expansion, and dynamic acquisition represent different conveniences for these applications using the IoT architecture. One of the major challenges is to satisfy the quality of service requirements while assigning resources to tasks. In this paper, we propose a deadline and cost-aware scheduling algorithm that minimizes the execution cost of a workflow under deadline constraints in the infrastructure as a service (IaaS) model. Considering the virtual machine (VM) performance variation and acquisition delay, we first divide tasks into different levels according to the topological structure so that no dependency exists between tasks at the same level. Three strings are used to code the genes in the proposed algorithm to better reflect the heterogeneous and resilient characteristics of cloud environments. Then, HEFT is used to generate individuals with the minimum completion time and cost. Novel schemes are developed for crossover and mutation to increase the diversity of the solutions. Based on this process, a task scheduling method that considers cost and deadlines is proposed. Experiments on workflows that simulate the structured tasks of the IoT demonstrate that our algorithm achieves a high success rate and performs well compared to state-of-the-art algorithms.

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