Energy-Aware Multi-Objective Placement of Virtual Machines in Cloud Data Centers

2018 ◽  
Author(s):  
Hanan Ali Al-shehri ◽  
Khaoula Hamdi
Keyword(s):  
Data Centers ◽  
Virtual Machines ◽  
Energy Aware ◽  
Cloud Data ◽  
Multi Objective ◽  
Cloud Data Centers

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 Enhanced Virtual Machine Placement in Cloud Data Centers: Combinations of Fuzzy Logic with Reinforcement Learning and Biogeography-Based Optimization (BBO) Algorithms

2022 ◽  
Author(s):  
Arezoo Ghasemi ◽  
Abolfazl Toroghi Haghighat ◽  
Amin Keshavarzi
Keyword(s):  
Reinforcement Learning ◽  
Load Balancing ◽  
Execution Time ◽  
Load Balance ◽  
Data Centers ◽  
Virtual Machines ◽  
Vertical Load ◽  
Cloud Data ◽  
Multi Objective ◽  
Cloud Data Centers

Abstract The process of mapping Virtual Machines (VMs) to Physical Ma- chines (PMs), which is defined as VM placement, affects Cloud Data Centers (DCs) performance. To enhance the performance, optimal placement of VMs regarding conflicting objectives has been proposed in some research, such as Multi-Objective VM reBalance (MOVMrB) and Reinforcement Learning VM reBalance (RLVMrB) in recent years. The MOVMrB algorithm is based on the BBO meta-heuristic algorithm and the RLVMrB algorithm inspired by reinforcement learning, which in both of them the non-dominance method is used to evaluate generated solutions. Although this approach reaches accept- able results, it fails to consider other solutions which are optimal regarding all objectives, when it meets the best solution based on one of these objectives. In this paper, we propose two enhanced multi-objective algorithms, Fuzzy- RLVMrB and Fuzzy-MOVMrB, that are able to consider all objectives when evaluating candidate solutions in solution space. All four algorithms aim to balance the load between VMs in terms of processor, bandwidth, and memory as well as horizontal and vertical load balance. We simulated all algorithms using the CloudSim simulator and compared them in terms of horizontal and vertical load balance and execution time. The simulation results show that Fuzzy-RLVMrB and Fuzzy-MOVMrB algorithms outperform RLVMrB and MOVMrB algorithms in terms of vertical load balancing and horizontal load balancing. Also, the RLVMrB and Fuzzy-RLVMrB algorithms are better in execution time than the MOVMrB and Fuzzy-MOVMrB algorithms.

scholarly journals An Enhanced Multi-Objective Gray Wolf Optimization for Virtual Machine Placement in Cloud Data Centers

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 218 ◽  
Author(s):  
Aisha Fatima ◽  
Nadeem Javaid ◽  
Ayesha Anjum Butt ◽  
Tanzeela Sultana ◽  
Waqar Hussain ◽  
...  
Keyword(s):  
Data Centers ◽  
Virtual Machines ◽  
Service Providers ◽  
Utilization Rate ◽  
Gray Wolf ◽  
Vm Placement ◽  
Cloud Data ◽  
Multi Objective ◽  
Cloud Data Centers ◽  
Better Than

Cloud computing offers various services. Numerous cloud data centers are used to provide these services to the users in the whole world. A cloud data center is a house of physical machines (PMs). Millions of virtual machines (VMs) are used to minimize the utilization rate of PMs. There is a chance of unbalanced network due to the rapid growth of Internet services. An intelligent mechanism is required to efficiently balance the network. Multiple techniques are used to solve the aforementioned issues optimally. VM placement is a great challenge for cloud service providers to fulfill the user requirements. In this paper, an enhanced levy based multi-objective gray wolf optimization (LMOGWO) algorithm is proposed to solve the VM placement problem efficiently. An archive is used to store and retrieve true Pareto front. A grid mechanism is used to improve the non-dominated VMs in the archive. A mechanism is also used for the maintenance of an archive. The proposed algorithm mimics the leadership and hunting behavior of gray wolves (GWs) in multi-objective search space. The proposed algorithm was tested on nine well-known bi-objective and tri-objective benchmark functions to verify the compatibility of the work done. LMOGWO was then compared with simple multi-objective gray wolf optimization (MOGWO) and multi-objective particle swarm optimization (MOPSO). Two scenarios were considered for simulations to check the adaptivity of the proposed algorithm. The proposed LMOGWO outperformed MOGWO and MOPSO for University of Florida 1 (UF1), UF5, UF7 and UF8 for Scenario 1. However, MOGWO and MOPSO performed better than LMOGWO for UF2. For Scenario 2, LMOGWO outperformed the other two algorithms for UF5, UF8 and UF9. However, MOGWO performed well for UF2 and UF4. The results of MOPSO were also better than the proposed algorithm for UF4. Moreover, the PM utilization rate (%) was minimized by 30% with LMOGWO, 11% with MOGWO and 10% with MOPSO.

scholarly journals Energy-aware virtual machines allocation by krill herd algorithm in cloud data centers

Heliyon ◽  
2019 ◽  
Vol 5 (7) ◽  
pp. e02066 ◽  
Author(s):  
Minoo Soltanshahi ◽  
Reza Asemi ◽  
Nazi Shafiei
Keyword(s):  
Data Centers ◽  
Virtual Machines ◽  
Energy Aware ◽  
Cloud Data ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Cloud Data Centers

An Energy-Efficient Consolidation of Virtual Machines in Cloud Data Centers

2017 ◽  
Vol 26 (1) ◽  
pp. 113-128
Author(s):  
Gamal Eldin I. Selim ◽  
Mohamed A. El-Rashidy ◽  
Nawal A. El-Fishawy
Keyword(s):  
Energy Efficient ◽  
Data Centers ◽  
Virtual Machines ◽  
Cloud Data ◽  
Cloud Data Centers

scholarly journals Energy aware dynamic virtual machine consolidation in cloud data centers

2018 ◽  
Vol 7 (2.8) ◽  
pp. 550 ◽  
Author(s):  
G Anusha ◽  
P Supraja
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Data Centers ◽  
Energy Aware ◽  
Large Power ◽  
Complex Tasks ◽  
Cloud Data ◽  
Network Bandwidth ◽  
Virtual Machine Consolidation ◽  
Cloud Data Centers

Cloud computing is a growing technology now-a-days, which provides various resources to perform complex tasks. These complex tasks can be performed with the help of datacenters. Data centers helps the incoming tasks by providing various resources like CPU, storage, network, bandwidth and memory, which has resulted in the increase of the total number of datacenters in the world. These data centers consume large volume of energy for performing the operations and which leads to high operation costs. Resources are the key cause for the power consumption in data centers along with the air and cooling systems. Energy consumption in data centers is comparative to the resource usage. Excessive amount of energy consumption by datacenters falls out in large power bills. There is a necessity to increase the energy efficiency of such data centers. We have proposed an Energy aware dynamic virtual machine consolidation (EADVMC) model which focuses on pm selection, vm selection, vm placement phases, which results in the reduced energy consumption and the Quality of service (QoS) to a considerable level.

Sign in / Sign up

Export Citation Format

Share Document