Scientific workflow scheduling in non-dedicated heterogeneous multicluster with advance reservations

2015 ◽  
Vol 22 (3) ◽  
pp. 261-280 ◽  
Author(s):  
Jinghui Zhang ◽  
Junzhou Luo ◽  
Fang Dong
Keyword(s):  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Advance Reservations

scholarly journals A Hybrid Algorithm for Multi-Objective Scientific Workflow Scheduling in IaaS Cloud

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 125783-125795 ◽  
Author(s):  
Yongqiang Gao ◽  
Shuyun Zhang ◽  
Jiantao Zhou
Keyword(s):  
Hybrid Algorithm ◽  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Multi Objective

A Novel Approach to Location-Aware Scheduling of Workflows Over Edge Computing Resources

2020 ◽  
Vol 17 (3) ◽  
pp. 56-68
Author(s):  
Yin Li ◽  
Yuyin Ma ◽  
Ziyang Zeng
Keyword(s):  
Real World ◽  
Service Level Agreement ◽  
Service Level ◽  
Scientific Workflow ◽  
Edge Computing ◽  
Workflow Scheduling ◽  
Location Aware ◽  
Novel Approach ◽  
Negative Impacts ◽  
Multiple Metrics

Edge computing is pushing the frontier of computing applications, data, and services away from centralized nodes to the logical extremes of a network. A major technological challenge for workflow scheduling in the edge computing environment is cost reduction with service-level-agreement (SLA) constraints in terms of performance and quality-of-service requirements because real-world workflow applications are constantly subject to negative impacts (e.g., network congestions, unexpected long message delays, shrinking coverage, range of edge servers due to battery depletion. To address the above concern, we propose a novel approach to location-aware and proximity-constrained multi-workflow scheduling with edge computing resources). The proposed approach is capable of minimizing monetary costs with user-required workflow completion deadlines. It employs an evolutionary algorithm (i.e., the discrete firefly algorithm) for the generation of near-optimal scheduling decisions. For the validation purpose, the authors show that our proposed approach outperforms traditional peers in terms multiple metrics based on a real-world dataset of edge resource locations and multiple well-known scientific workflow templates.

Makespan–Cost–Reliability-Optimized Workflow Scheduling Using Evolutionary Techniques in Clouds

2019 ◽  
Vol 29 (10) ◽  
pp. 2050167
Author(s):  
Xiumin Zhou ◽  
Gongxuan Zhang ◽  
Tian Wang ◽  
Mingyue Zhang ◽  
Xiji Wang ◽  
...  
Keyword(s):  
Quality Of Service ◽  
Energy Consumption ◽  
System Reliability ◽  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Workflow Systems ◽  
Multi Objective ◽  
Qos Parameters ◽  
Crossover And Mutation

Most popular scientific workflow systems can now support the deployment of tasks to the cloud. The execution of workflow on cloud has become a multi-objective scheduling in order to meet the needs of users in many aspects. Cost and makespan are considered to be the two most important objects. In addition to these, there are some other Quality-of-Service (QoS) parameters including system reliability, energy consumption and so on. Here, we focus on three objectives: cost, makespan and system reliability. In this paper, we propose a Multi-objective Evolutionary Algorithm on the Cloud (MEAC). In the algorithm, we design some novel schemes including problem-specific encoding and also evolutionary operations, such as crossover and mutation. Simulations on real-world and random workflows are conducted and the results show that MEAC can get on average about 5% higher hypervolume value than some other workflow scheduling algorithms.

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.

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