scholarly journals EATS: Energy-Aware Tasks Scheduling in Cloud Computing Systems

2016 ◽  
Vol 83 ◽  
pp. 870-877 ◽  
Author(s):  
Leila Ismail ◽  
Abbas Fardoun
Keyword(s):  
Cloud Computing ◽  
Energy Aware ◽  
Computing Systems ◽  
Tasks Scheduling

scholarly journals A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems

2011 ◽  
Vol 71 (11) ◽  
pp. 1497-1508 ◽  
Author(s):  
M. Mezmaz ◽  
N. Melab ◽  
Y. Kessaci ◽  
Y.C. Lee ◽  
E.-G. Talbi ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Energy Aware ◽  
Computing Systems ◽  
Hybrid Metaheuristic ◽  
Energy Aware Scheduling

scholarly journals Heuristic-based Approach for Dynamic Consolidation of Software Licenses in Cloud Data Centers

2021 ◽  
Vol 13 (6) ◽  
pp. 1-12
Author(s):  
Leila Helali ◽  
◽  
Mohamed Nazih Omri
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Virtual Machines ◽  
Heuristic Algorithms ◽  
Energy Aware ◽  
Computing Systems ◽  
Cloud Data ◽  
Software Licenses ◽  
Cloud Data Centers ◽  
Distributed Cloud

Since its emergence, cloud computing has continued to evolve thanks to its ability to present computing as consumable services paid by use, and the possibilities of resource scaling that it offers according to client’s needs. Models and appropriate schemes for resource scaling through consolidation service have been considerably investigated,mainly, at the infrastructure level to optimize costs and energy consumption. Consolidation efforts at the SaaS level remain very restrained mostly when proprietary software are in hand. In order to fill this gap and provide software licenses elastically regarding the economic and energy-aware considerations in the context of distributed cloud computing systems, this work deals with dynamic software consolidation in commercial cloud data centers 𝑫𝑺𝟑𝑪. Our solution is based on heuristic algorithms and allows reallocating software licenses at runtime by determining the optimal amount of resources required for their execution and freed unused machines. Simulation results showed the efficiency of our solution in terms of energy by 68.85% savings and costs by 80.01% savings. It allowed to free up to 75% physical machines and 76.5% virtual machines and proved its scalability in terms of average execution time while varying the number of software and the number of licenses alternately.

scholarly journals An Efficient Energy-Aware Tasks Scheduling with Deadline-Constrained in Cloud Computing

Computers ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 46 ◽  
Author(s):  
Said BEN ALLA ◽  
Hicham BEN ALLA ◽  
Abdellah TOUHAFI ◽  
Abdellah EZZATI
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Data Centers ◽  
High Stress ◽  
Electricity Consumption ◽  
New Paradigm ◽  
Energy Aware ◽  
Carbon Footprints ◽  
Efficient Energy ◽  
Tasks Scheduling

Nowadays, Cloud Computing (CC) has emerged as a new paradigm for hosting and delivering services over the Internet. However, the wider deployment of Cloud and the rapid increase in the capacity, as well as the size of data centers, induces a tremendous rise in electricity consumption, escalating data center ownership costs and increasing carbon footprints. This expanding scale of data centers has made energy consumption an imperative issue. Besides, users’ requirements regarding execution time, deadline, QoS have become more sophisticated and demanding. These requirements often conflict with the objectives of cloud providers, especially in a high-stress environment in which the tasks have very critical deadlines. To address these issues, this paper proposes an efficient Energy-Aware Tasks Scheduling with Deadline-constrained in Cloud Computing (EATSD). The main goal of the proposed solution is to reduce the energy consumption of the cloud resources, consider different users’ priorities and optimize the makespan under the deadlines constraints. Further, the proposed algorithm has been simulated using the CloudSim simulator. The experimental results validate that the proposed approach can effectively achieve good performance by minimizing the makespan, reducing energy consumption and improving resource utilization while meeting deadline constraints.

Secured Data Sharing and Access Control for Cloud Computing Systems using CP-ABE Algorithm with Verifiable Delegation

NCICCNDA ◽  
2018 ◽  
Author(s):  
Kavya P ◽  
Manjula S ◽  
Neethu Kuwar ◽  
Nikitha R ◽  
Gururaj KS
Keyword(s):  
Cloud Computing ◽  
Access Control ◽  
Data Sharing ◽  
Computing Systems ◽  
Secured Data
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