Energy-aware parameter tuning mechanism for workflow scheduling in the cloud environment

Autor: Danthuluri Sudha, Sanjay Chitnis
Rok vydání: 2021
Předmět:
Zdroj: Materials Today: Proceedings. 45:3137-3142
ISSN: 2214-7853
DOI: 10.1016/j.matpr.2020.12.219
Popis: Cloud Computing is one of the successful computing paradigms and it has contributed to the huge growth of the IT industry as well as individual usability of computing resource due to its ease of accessing resource properties. Moreover, cloud computing emergence has another advantage for deployment in a large scientific workflow. The scientific workflow defines a computation series which enables data analysis in a distributed and structured manner; since this workflow possesses a huge amount of task, energy consumption is a primary issue. In this research work, we have designed an efficient mechanism named EAPT (Energy-Aware Parameter tuning) to minimize energy consumption; in here two eminent parameter such as computation parameter, communication parameter and task size are tuned for optimizing these parameters resulting in minimization in energy. Further EAPT achieves the energy minimization through balancing the load dynamically, through tuning the above discussed parameter. EAPT tunes this in efficient way such that load are distributed based upon resources. EAPT is evaluated by considering the energy consumption as a parameter and varying the number of VM as 20, 40 and 60; moreover, a comparative analysis is carried out with the existing DVFS model. Comparative analysis shows that EAPT consumes marginally less energy than the existing model.
Databáze: OpenAIRE
Externí odkaz: