Energy-aware scheduling of virtual machines in heterogeneous cloud computing systems

2017 ◽  
Vol 74 ◽  
pp. 142-150 ◽  
Author(s):  
Hancong Duan ◽  
Chao Chen ◽  
Geyong Min ◽  
Yu Wu
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Energy Aware ◽  
Computing Systems ◽  
Energy Aware Scheduling ◽  
Heterogeneous Cloud

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.

A simplified reliability analysis method for cloud computing systems considering common-cause failures

2017 ◽  
Vol 231 (3) ◽  
pp. 324-333 ◽  
Author(s):  
Ruiying Li ◽  
Qiong Li ◽  
Ning Huang ◽  
Rui Kang
Keyword(s):  
Cloud Computing ◽  
State Space ◽  
Reliability Analysis ◽  
Virtual Machines ◽  
Single Server ◽  
Analysis Method ◽  
Computing Systems ◽  
Common Cause ◽  
Cloud Computing System ◽  
Common Cause Failures

Virtualization is one of the main features of cloud computing systems, which enables building multiple virtual machines on a single server. However, this feature brings new challenge in reliability modeling, as the failure of the server will make all its co-located virtual machines inoperable, which is a typical common-cause failure. To satisfy the demand of the cloud computing system, the reliability of the system is defined as the probability that at least a given number of virtual machines are operable. State-space enumeration is one method to calculate such reliability; however, due to the large number of combinations, it is time-consuming and impractical. To solve this problem, we propose a simplified reliability analysis method based on fault tree and state-space models. Two illustrative examples are studied to show the process and the effectiveness of our method. State enumeration and Monte Carlo simulation are also used to prove the correctness of our method as back-to-back verifications. Compared to the reliability analysis without considering common-cause failures, our results are quite different, which illustrates the necessity of considering common-cause failures in the reliability of cloud computing systems.

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