Authors:
Meriem Azaiez
1
;
Walid Chainbi
1
and
Hanen Chihi
2
Affiliations:
1
University of Sousse, Tunisia
;
2
Institute of Computer Sciences of Ariana, Tunisia
Keyword(s):
Cloud Computing, Optimization, Scheduling, Green Computing, CloudSim, Genetic Algorithm.
Related
Ontology
Subjects/Areas/Topics:
Cloud Computing
;
Cloud Computing Enabling Technology
;
Cloud Delivery Models
;
Cloud Optimization and Automation
;
Cloud Workload Profiling and Deployment Control
;
Fundamentals
Abstract:
The evolution of network technologies and their reliability on the one hand, and the spread of virtualization techniques on the other hand, have motivated the use of execution and storage resources allocated by distant providers. These resources may progress on demand. Cloud computing deals with such aspects. However, these resources are greedy in energy because they consume huge amounts of electrical energy, which affects the invoicing of Cloud services which depends on run-time and used resources. The environment is affected too due to the emission of greenhouse gas. Therefore, we need Green Cloud computing solutions that reduce the environmental impact. To overcome this Challenge, we study in this paper the relationship between Cloud infrastructure and energy consumption. Then, we present a genetic algorithm based solution that schedules Cloud resources and optimizes the energy consumption and CO_2 emissions of Cloud computing infrastructure based on geographical features of data
centers. Unlike previous work, we propose to optimize the use of Cloud resources by scheduling dynamically the customer’s applications and therefore reduce energy consumption as well as the emission of CO_2. The optimal solution of scheduling is found using multi-objective genetic algorithm. In order to test our model, we extended the CloudSim simulator with a module implementing the dynamic scheduling of customer’s applications. The experiments show promising results related to the adoption of our model.
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