Skip to main content
 

 

 

Energy-Aware Task Allocation for Multi-Cloud Networks

Author name : AHMED MOSA H ALSAYAT
Publication Date : 2020-09-25
Journal Name : IEEE Access

Abstract

In recent years, the growth rate of Cloud computing technology is increasing exponentially, mainly for its extraordinary services with expanding computation power, the possibility of massive storage, and all other services with the maintained quality of services (QoSs). The task allocation is one of the best solutions to improve different performance parameters in the cloud, but when multiple heterogeneous clouds come into the picture, the allocation problem becomes more challenging. This research work proposed a resource-based task allocation algorithm. The same is implemented and analyzed to understand the improved performance of the heterogeneous multi-cloud network. The proposed task allocation algorithm (Energy-aware Task Allocation in Multi-Cloud Networks ( ETAMCN )) minimizes the overall energy consumption and also reduces the makespan. The results show that the makespan is approximately overlapped for different tasks and does not show a significant difference. However, the average energy consumption improved through ETAMCN is approximately 14%, 6.3%, and 2.8% in opposed to the random allocation algorithm, Cloud Z-Score Normalization ( CZSN ) algorithm, and multi-objective scheduling algorithm with Fuzzy resource utilization (FR-MOS), respectively. An observation of the average SLA-violation of ETAMCN for different scenarios is performed.

Keywords

Resource based; energy consumption; makespan; multi-cloud; task scheduling; cloud virtualization

Publication Link

https://doi.org/10.1109/ACCESS.2020.3026875

Block_researches_list_suggestions

Suggestions to read

“Synthesis and Characterization study of SnO2/α-Fe2O3, In2O3/α-Fe2O3 and ZnO/α-Fe2O3 thin films and its application as transparent conducting electrode in silicon heterojunction solar cell”
Asma Arfaoui
Oral cancer stem cells: A comprehensive review of key drivers of treatment resistance and tumor recurrence
DR KALADHAR REDDY AILENI
Modeling the Social Factors Affecting Students Satisfaction with Online Learning: A Structural Equation Modeling Approach
ABDULHAMEED RAKAN ALENEZI
Higher Knee Muscles Co-Contractions are Observed in Individuals Exhibiting Loading Asymmetry Early after ACL Reconstruction. The Combined Sections Meeting
ABDULMAJEED BARAKAT MUBARAK ALFAYYADH
Contact