scholarly journals Cost Effective Genetic Algorithm for Workflow Scheduling in Cloud Under Deadline Constraint

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 5065-5082 ◽  
Author(s):  
Jasraj Meena ◽  
Malay Kumar ◽  
Manu Vardhan
Keyword(s):  
Genetic Algorithm ◽  
Cost Effective ◽  
Workflow Scheduling ◽  
Deadline Constraint

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.

Cost-Effective Algorithm for Workflow Scheduling in Cloud Computing Under Deadline Constraint

2018 ◽  
Vol 44 (4) ◽  
pp. 3765-3780 ◽  
Author(s):  
Aida A. Nasr ◽  
Nirmeen A. El-Bahnasawy ◽  
Gamal Attiya ◽  
Ayman El-Sayed
Keyword(s):  
Cloud Computing ◽  
Cost Effective ◽  
Workflow Scheduling ◽  
Effective Algorithm ◽  
Deadline Constraint

Deadline-Constrained Cost-Effective Load-Balanced Improved Genetic Algorithm for Workflow Scheduling

2021 ◽  
Vol 16 (4) ◽  
pp. 1-34
Author(s):  
Sandeep Kumar Bothra ◽  
Sunita Singhal ◽  
Hemlata Goyal
Keyword(s):  
Genetic Algorithm ◽  
Virtual Machines ◽  
Cost Effective ◽  
Critical Issue ◽  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Improved Genetic Algorithm ◽  
Greedy Strategy ◽  
Mutation Operators ◽  
Load Balanced

Resource scheduling in a cloud computing environment is noteworthy for scientific workflow execution under a cost-effective deadline constraint. Although various researchers have proposed to resolve this critical issue by applying various meta-heuristic and heuristic approaches, no one is able to meet the strict deadline conditions with load-balanced among machines. This article has proposed an improved genetic algorithm that initializes the population with a greedy strategy. Greedy strategy assigns the task to a virtual machine that is under loaded instead of assigning the tasks randomly to a machine. In general workflow scheduling, task dependency is tested after each crossover and mutation operators of genetic algorithm, but here the authors perform after the mutation operation only which yield better results. The proposed model also considered booting time and performance variation of virtual machines. The authors compared the algorithm with previously developed heuristics and metaheuristics both and found it increases hit rate and load balance. It also reduces execution time and cost.

Rapid Preliminary Design of Floating Offshore Structures Using a Modified Genetic Algorithm

2007 ◽  
Author(s):  
Michael J. Perry ◽  
John E. Halkyard ◽  
C. G. Koh
Keyword(s):  
Genetic Algorithm ◽  
Cost Effective ◽  
Offshore Structures ◽  
Preliminary Design ◽  
Optimization Strategy ◽  
Empirical Estimates ◽  
Wind Speed Maximum ◽  
Design Wind Speed ◽  
Truss Spar ◽  
Weight Design

Preliminary design of floating offshore structures involves determining structural dimensions able to provide sufficient buoyancy to carry the required topside, at the lowest possible cost, while satisfying various stability, strength, installation, and response requirements. A novel optimization strategy, capable of carrying out the preliminary design of floating offshore structures, is presented in this paper. The genetic algorithm based strategy searches within prescribed parameter limits for the most cost effective design, while ensuring the design conforms to the constraints given. The design of a truss spar is used to illustrate how the strategy can be applied. The topside weight, design wind speed, maximum wave height, etc are input along with constraints such as, maximum draft at floatoff, maximum heel angle, allowable stress in the truss and limits on pitch and heave period and response. Using empirical estimates for hull weights and simplified response calculations, the strategy is then able to rapidly determine parameters such as hull diameter, hard tank depth, length of keel tank, total length and truss leg diameter such that the total cost of the structure is minimized. The strategy allows for the preliminary design phase to be completed in only a few seconds, while providing initial weight and cost estimates.

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