scholarly journals Reducing the Operational Cost of Cloud Data Centers through Renewable Energy

Algorithms ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 145 ◽  
Author(s):  
Demetrio Laganà ◽  
Carlo Mastroianni ◽  
Michela Meo ◽  
Daniela Renga
Keyword(s):  
Renewable Energy ◽  
Data Centers ◽  
Virtual Machines ◽  
Renewable Energy Sources ◽  
Management Strategies ◽  
Production Performance ◽  
Load Management ◽  
Cloud Data ◽  
Operational Costs ◽  
Allocation Process

The success of cloud computing services has led to big computing infrastructures that are complex to manage and very costly to operate. In particular, power supply dominates the operational costs of big infrastructures, and several solutions have to be put in place to alleviate these operational costs and make the whole infrastructure more sustainable. In this paper, we investigate the case of a complex infrastructure composed of data centers (DCs) located in different geographical areas in which renewable energy generators are installed, co-located with the data centers, to reduce the amount of energy that must be purchased by the power grid. Since renewable energy generators are intermittent, the load management strategies of the infrastructure have to be adapted to the intermittent nature of the sources. In particular, we consider EcoMultiCloud , a load management strategy already proposed in the literature for multi-objective load management strategies, and we adapt it to the presence of renewable energy sources. Hence, cost reduction is achieved in the load allocation process, when virtual machines (VMs) are assigned to a data center of the considered infrastructure, by considering both energy cost variations and the presence of renewable energy production. Performance is analyzed for a specific infrastructure composed of four data centers. Results show that, despite being intermittent and highly variable, renewable energy can be effectively exploited in geographical data centers when a smart load allocation strategy is implemented. In addition, the results confirm that EcoMultiCloud is very flexible and is suited to the considered scenario.

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 Virtual Machine Placement via Bin Packing in Cloud Data Centers

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 389 ◽  
Author(s):  
Aisha Fatima ◽  
Nadeem Javaid ◽  
Tanzeela Sultana ◽  
Waqar Hussain ◽  
Muhammad Bilal ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Data Centers ◽  
Bin Packing ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Vm Placement ◽  
Swarm Optimization ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Vm Consolidation

With the increasing size of cloud data centers, the number of users and virtual machines (VMs) increases rapidly. The requests of users are entertained by VMs residing on physical servers. The dramatic growth of internet services results in unbalanced network resources. Resource management is an important factor for the performance of a cloud. Various techniques are used to manage the resources of a cloud efficiently. VM-consolidation is an intelligent and efficient strategy to balance the load of cloud data centers. VM-placement is an important subproblem of the VM-consolidation problem that needs to be resolved. The basic objective of VM-placement is to minimize the utilization rate of physical machines (PMs). VM-placement is used to save energy and cost. An enhanced levy-based particle swarm optimization algorithm with variable sized bin packing (PSOLBP) is proposed for solving the VM-placement problem. Moreover, the best-fit strategy is also used with the variable sized bin packing problem (VSBPP). Simulations are done to authenticate the adaptivity of the proposed algorithm. Three algorithms are implemented in Matlab. The given algorithm is compared with simple particle swarm optimization (PSO) and a hybrid of levy flight and particle swarm optimization (LFPSO). The proposed algorithm efficiently minimized the number of running PMs. VM-consolidation is an NP-hard problem, however, the proposed algorithm outperformed the other two algorithms.

Multidimensional Resource Scheduling in Cloud Data Centers

2014 ◽  
Vol 1008-1009 ◽  
pp. 1513-1516
Author(s):  
Hai Na Song ◽  
Xiao Qing Zhang ◽  
Zhong Tang He
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Bin Packing ◽  
Virtual Machines ◽  
Three Dimensional ◽  
Resource Scheduling ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Cloud Data ◽  
Cloud Data Centers

Cloud computing environment is regarded as a kind of multi-tenant computing mode. With virtulization as a support technology, cloud computing realizes the integration of multiple workloads in one server through the package and seperation of virtual machines. Aiming at the contradiction between the heterogeneous applications and uniform shared resource pool, using the idea of bin packing, the multidimensional resource scheduling problem is analyzed in this paper. We carry out some example analysis in one-dimensional resource scheduling, two-dimensional resource schduling and three-dimensional resource scheduling. The results shows that the resource utilization of cloud data centers will be improved greatly when the resource sheduling is conducted after reorganizing rationally the heterogeneous demands.

Optimized Renewable Energy Use in Green Cloud Data Centers

2019 ◽  
pp. 314-330
Author(s):  
Minxian Xu ◽  
Adel N. Toosi ◽  
Behrooz Bahrani ◽  
Reza Razzaghi ◽  
Martin Singh
Keyword(s):  
Renewable Energy ◽  
Energy Use ◽  
Data Centers ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Green Cloud

Valuing feedback mechanisms between water and energy systems in hydropower networks

2021 ◽  
Author(s):  
Rachel Koh ◽  
Jordan Kern ◽  
AFM Kamal Chowdhury ◽  
Stefano Galelli
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
Renewable Energy Sources ◽  
Management Strategies ◽  
Energy System ◽  
Water Systems ◽  
Feedback Mechanism ◽  
Production Costs ◽  
Feedback Mechanisms ◽  
Modelling Framework

<p>Multi-sector modelling frameworks are fundamental platforms for exploring the complex interactions between the water and energy sectors. While acknowledging the pivotal role of hydropower within the energy system, it is essential to understand the feedback mechanisms between power and water systems to guide the design of hydropower operations and enhance water-energy management strategies. With this in mind, we developed a modelling framework hinged on a bidirectional coupling between water and power system models. We simulate the constraints imposed by water availability on grid operations as well as the feedback between the state of the energy and water systems. For example, the framework explicitly accounts for conditions of hydropower oversupply, during which part of the water could be stored in reservoirs or allocated to other sectors. The flexibility added to the system gives operators control over desired reservoirs, and allows the system to exploit the benefits warranted by a more efficient use of renewable energy. We evaluate the framework on a real-world case study based on the Cambodian grid, which relies on hydro, solar, and thermoelectric resources. In our analysis, we demonstrate that managing hydropower reservoirs with the feedback mechanism in mind allows us to improve system’s performance—evaluated in terms of power production costs and CO<sub>2</sub> emissions. Overall, our work contributes a novel modelling tool for climate-water-energy nexus studies, working towards an optimal integration of hydropower and other renewable energy sources into power systems.</p>

Bandwidth allocation for communicating virtual machines in cloud data centers

2020 ◽  
Vol 76 (9) ◽  
pp. 7268-7289
Author(s):  
Kamalesh Karmakar ◽  
Rajib K. Das ◽  
Sunirmal Khatua
Keyword(s):  
Bandwidth Allocation ◽  
Data Centers ◽  
Virtual Machines ◽  
Cloud Data ◽  
Cloud Data Centers

scholarly journals Virtual Machine Consolidation with Minimization of Migration Thrashing for Cloud Data Centers

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xialin Liu ◽  
Junsheng Wu ◽  
Gang Sha ◽  
Shuqin Liu
Keyword(s):  
Virtual Machine ◽  
Carbon Dioxide Emissions ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
High Capacity ◽  
Electrical Energy ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Vm Consolidation

Cloud data centers consume huge amount of electrical energy bringing about in high operating costs and carbon dioxide emissions. Virtual machine (VM) consolidation utilizes live migration of virtual machines (VMs) to transfer a VM among physical servers in order to improve the utilization of resources and energy efficiency in cloud data centers. Most of the current VM consolidation approaches tend to aggressive-migrate for some types of applications such as large capacity application such as speech recognition, image processing, and decision support systems. These approaches generate a high migration thrashing because VMs are consolidated to servers according to VM’s instant resource usage without considering their overall and long-term utilization. The proposed approach, dynamic consolidation with minimization of migration thrashing (DCMMT) which prioritizes VM with high capacity, significantly reduces migration thrashing and the number of migrations to ensure service-level agreement (SLA) since it keeps VMs likely to suffer from migration thrashing in the same physical servers instead of migrating. We have performed experiments using real workload traces compared to existing aggressive-migration-based solutions; through simulations, we show that our approach improves migration thrashing metric by about 28%, number of migrations metric by about 21%, and SLAV metric by about 19%.

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