Optimal cloud storage problem in the distributed cloud data centers by the discrete PSO algorithm

2015 ◽  
Author(s):  
Jun Liu ◽  
Tianyun Shi ◽  
Ping Li
Keyword(s):  
Cloud Storage ◽  
Data Centers ◽  
Pso Algorithm ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Discrete Pso ◽  
Storage Problem ◽  
Distributed Cloud

scholarly journals On demand clock synchronization for live VM migration in distributed cloud data centers

2020 ◽  
Vol 138 ◽  
pp. 15-31 ◽  
Author(s):  
Yashwant Singh Patel ◽  
Aditi Page ◽  
Manvi Nagdev ◽  
Anurag Choubey ◽  
Rajiv Misra ◽  
...  
Keyword(s):  
Data Centers ◽  
Clock Synchronization ◽  
Vm Migration ◽  
Cloud Data ◽  
On Demand ◽  
Cloud Data Centers ◽  
Distributed Cloud

scholarly journals Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing

2019 ◽  
Vol 9 (17) ◽  
pp. 3550 ◽  
Author(s):  
A-Young Son ◽  
Eui-Nam Huh
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Response Time ◽  
Data Centers ◽  
Cloud Data ◽  
Multi Objective ◽  
Service Placement ◽  
Service Response Time ◽  
Cloud Data Centers ◽  
Distributed Cloud

With the rapid increase in the development of the cloud data centers, it is expected that massive data will be generated, which will decrease service response time for the cloud data centers. To improve the service response time, distributed cloud computing has been designed and researched for placement and migration from mobile devices close to edge servers that have secure resource computing. However, most of the related studies did not provide sufficient service efficiency for multi-objective factors such as energy efficiency, resource efficiency, and performance improvement. In addition, most of the existing approaches did not consider various metrics. Thus, to maximize energy efficiency, maximize performance, and reduce costs, we consider multi-metric factors by combining decision methods, according to user requirements. In order to satisfy the user’s requirements based on service, we propose an efficient service placement system named fuzzy- analytical hierarchical process and then analyze the metric that enables the decision and selection of a machine in the distributed cloud environment. Lastly, using different placement schemes, we demonstrate the performance of the proposed scheme.

scholarly journals Robustness of the Storage in Cloud Data Centers Based on Simple Swarm Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Alireza Chamkoori ◽  
Serajdean Katebi
Keyword(s):  
Data Centers ◽  
Spatial Information ◽  
Data Transfer ◽  
Data Communication ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Optimal Position ◽  
Cloud Data ◽  
Learning Automaton ◽  
Cloud Data Centers

Storing extensive data in cloud environments affects service quality, transmission speed, and access to information in systems, which is becoming a growing challenge. In storage improvement, reducing various costs and reducing the shortest path in the storage of distributed cloud data centers are among the important issues in the field of cloud computing. In this paper, particle swarm optimization (PSO) algorithm and learning automaton (LA) are used to minimize the cost of a data center, which includes communication, data transfer, and storage and optimization of communication between data centers. To improve storage in distributed data centers, a new model called LAPSO is proposed by combining LA and PSO, in which LA improves particle control by searching for particle speed and position. In this method, LA moves each particle in the direction where it has the best individual and group experiences. In multipeak problems, it does not fall into local optimums. Results of the experiments are shown on the dataset of spatial information and cadastre of country lands, which includes 13 data centers. The proposed method evaluates and improves the optimal position parameters, minimum route cost, distance, data transfer cost, storage cost, data communication cost, load balance, and access performance better than other methods.

Energy and Carbon Footprint-Aware Management of Geo-Distributed Cloud Data Centers

2017 ◽  
pp. 27-46 ◽  
Author(s):  
Atefeh Khosravi ◽  
Rajkumar Buyya
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Carbon Footprint ◽  
Data Centers ◽  
Target System ◽  
Cloud Data ◽  
Carbon Efficiency ◽  
Cloud Data Centers ◽  
Distributed Cloud ◽  
System Objective

Cloud computing provides on-demand access to computing resources for users across the world. It offers services on a pay-as-you-go model through data center sites that are scattered across diverse geographies. However, cloud data centers consume huge amount of electricity and leave high amount of carbon footprint in the ecosystem. This makes data centers responsible for 2% of the global CO2 emission. Therefore, having energy and carbon-efficient techniques for resource management in distributed cloud data centers is inevitable. This chapter presents a taxonomy and classifies the existing research works based on their target system, objective, and the technique they use for resource management in achieving a green cloud computing environment. Finally, it discusses how each work addresses the issue of energy and carbon-efficiency and also provides an insight into future directions.

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.

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