Photonic Interconnects for Data Centers

2014 ◽  
Vol 2014 (1) ◽  
pp. 000862-000865 ◽  
Author(s):  
Tolga Tekin ◽  
Andreas Hakansson ◽  
Nikos Pleros ◽  
Dimitris Apostolopoulos
Keyword(s):  
Power Consumption ◽  
Data Center ◽  
Optical Interconnects ◽  
Data Centers ◽  
Next Generation ◽  
Consumption Cost ◽  
Photonic Interconnects ◽  
And Performance

Power consumption, cost and performance appear as the main metrics in next-generation Data Center. PhoxTroT has been conceived to address optical interconnects at a holistic way among all hierarchy levels: chip-to-chip, board-to-board, rack-to-rack.

scholarly journals Power consumption prediction in cloud data center using machine learning

2020 ◽  
Vol 10 (2) ◽  
pp. 1524 ◽  
Author(s):  
Deepika T. ◽  
Prakash P.
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Power Consumption ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Providers ◽  
Business World ◽  
Cloud Data ◽  
Cloud Data Centers

The flourishing development of the cloud computing paradigm provides several services in the industrial business world. Power consumption by cloud data centers is one of the crucial issues for service providers in the domain of cloud computing. Pursuant to the rapid technology enhancements in cloud environments and data centers augmentations, power utilization in data centers is expected to grow unabated. A diverse set of numerous connected devices, engaged with the ubiquitous cloud, results in unprecedented power utilization by the data centers, accompanied by increased carbon footprints. Nearly a million physical machines (PM) are running all over the data centers, along with (5 – 6) million virtual machines (VM). In the next five years, the power needs of this domain are expected to spiral up to 5% of global power production. The virtual machine power consumption reduction impacts the diminishing of the PM’s power, however further changing in power consumption of data center year by year, to aid the cloud vendors using prediction methods. The sudden fluctuation in power utilization will cause power outage in the cloud data centers. This paper aims to forecast the VM power consumption with the help of regressive predictive analysis, one of the Machine Learning (ML) techniques. The potency of this approach to make better predictions of future value, using Multi-layer Perceptron (MLP) regressor which provides 91% of accuracy during the prediction process.

Data Center Enhancement by Server Resources Utilization

2021 ◽  
Vol 39 (1B) ◽  
pp. 203-208
Author(s):  
Haider A. Ghanem ◽  
Rana F. Ghani ◽  
Maha J. Abbas
Keyword(s):  
Cloud Computing ◽  
Power Consumption ◽  
Resource Utilization ◽  
Virtual Machine ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
The Internet ◽  
Memory Resources

Data centers are the main nerve of the Internet because of its hosting, storage, cloud computing and other services. All these services require a lot of work and resources, such as energy and cooling. The main problem is how to improve the work of data centers through increased resource utilization by using virtual host simulations and exploiting all server resources. In this paper, we have considered memory resources, where Virtual machines were distributed to hosts after comparing the virtual machines with the host from where the memory and putting the virtual machine on the appropriate host, this will reduce the host machines in the data centers and this will improve the performance of the data centers, in terms of power consumption and the number of servers used and cost.

scholarly journals Heat Sink Sunk

2017 ◽  
Vol 139 (01) ◽  
pp. 40-45
Author(s):  
Alan S. Brown
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Disk Drives ◽  
Energy Research ◽  
Electrical Grid ◽  
Site Energy ◽  
Network Servers ◽  
Power Construction ◽  
Computing Center ◽  
And Performance

This article explores various innovative ways that technology companies are working upon to deal with the large amount of heat of their big data centers. Microsoft’s research and development team is working on designing and building of an underwater data center. Researchers believe that underwater data centers might have power, construction, and performance advantages as well. Berkeley’s National Energy Research Scientific Computing Center (NERSC) recycles its chilled water. NERSC uses air to chill the data center and cooler-running components, such as disk drives, routers, and network servers. With the growing efficiency of data centers, better cooling might not be enough to make underwater operations worthwhile. But cooling is only one of the advantages that Microsoft sees in submerging its data centers. Submersibles also simplify deployment. Microsoft used an underwater cable connected to the electrical grid to power Leona Philpot, a submersible data center. In the future, it may reduce costs through renewable energy, combined with on-site energy storage and backup power from the grid.

scholarly journals CCD Controllers

1995 ◽  
Vol 167 ◽  
pp. 49-56
Author(s):  
Robert W. Leach
Keyword(s):  
Power Consumption ◽  
San Diego ◽  
Computer Interface ◽  
Next Generation ◽  
System Architectures ◽  
Consumption Cost ◽  
Video Processor ◽  
Readout Noise

The requirements of current and next generation CCD controllers in the areas of CCD device and system architectures, readout noise, number and speed of readouts are reviewed together with such operational requirements as system flexibility, power consumption, cost and weight. The basic components of a CCD controller are described, including the timing sequencer, clock drivers, video processor and computer interface. The capabilities and implementation of the CCD controller developed at San Diego State are reviewed. An upgraded controller is described to overcome limitations in the area of readout speed and efficient support of multiple readout capability.

Energy Optimization of Air-Cooled Data Centers

2010 ◽  
Vol 2 (4) ◽  
Author(s):  
H. E. Khalifa ◽  
D. W. Demetriou
Keyword(s):  
Power Consumption ◽  
Temperature Rise ◽  
Data Center ◽  
Data Centers ◽  
Optimal Operation ◽  
Effect Of Temperature ◽  
Cooling Power ◽  
Air Conditioners ◽  
Air Supply ◽  
Operating Strategies

The work presented in this paper describes a simplified thermodynamic model that can be used for exploring optimization possibilities in air-cooled data centers. The model has been used to identify optimal, energy-efficient designs, operating scenarios, and operating parameters such as flow rates and air supply temperatures. The results of this analysis highlight the important features that need to be considered when optimizing the operation of air-cooled data centers, especially the trade-off between low air supply temperature and increased air flow rate. The model was shown to be especially valuable in defining the optimal operating strategies for enclosed aisle configurations with fixed and variable server flows, and to elucidate the deleterious effect of temperature nonuniformity at the inlet of the racks on the data center cooling infrastructure power consumption. The analysis shows a potential for as much as an ∼58% savings in cooling infrastructure energy consumption by utilizing an optimized enclosed aisle configuration with bypass recirculation, instead of a traditional enclosed aisle, where all the data center exhaust is forced to flow through the computer room air conditioners. The analysis of open-aisle data centers shows that as the temperature at the inlet of the racks becomes more nonuniform, optimal operation tends toward lower recirculation and higher power consumption; again, stressing the importance of providing as uniform a temperature to the racks as possible. It is also revealed that servers with a modest temperature rise (∼10°C) have a wider latitude for cooling infrastructure optimization than those with a high temperature rise (≥20°C), which tend to consume less cooling power when the aisles are enclosed.

scholarly journals Open Source Monitoring System for IT Infrastructures Incorporating IoT-Based Sensors

Proceedings ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 56
Author(s):  
Alejandro Mosteiro Vázquez ◽  
Carlos Dafonte ◽  
Ángel Gómez
Keyword(s):  
Power Consumption ◽  
Open Source ◽  
Monitoring System ◽  
Data Center ◽  
Data Centers ◽  
Source Monitoring ◽  
State Of The Art ◽  
Memory Usage ◽  
Network Bandwidth ◽  
Status Data

This paper introduces the development of a data center monitoring system based on IoT technologies. The system is meant to work as an administrative tool for system administrators in any environment, but mainly focused on data centers, since it integrates sensor and server status data. We are developing a system that gives a broad view of a data center, integrating server data such as CPU and memory usage or network bandwidth with room health parameters such as temperature, humidity, and power consumption or the presence sensors that indicate if there were people inside the room at the time a certain event occurred. As this is a work in progress, in this paper, we present the state-of-the-art of this subject, as well as what we expect to obtain from this project.

scholarly journals Eeadoselfcloud: Energy Efficient Adaptive Depth Optimized Self Cloud Mechanism for VM Migration in Data Centers

2019 ◽  
Vol 8 (6) ◽  
pp. 475-482
Keyword(s):  
Power Consumption ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Algorithm Analysis ◽  
Virtual Machine Migration ◽  
Hop Count ◽  
Vm Migration ◽  
Bee Colony ◽  
Data Centres

Cloud computing is a paradigm where all resources like software, hardware and information are accessed over internet by using highly sophisticated virtual data centres. The cloud has a data center with a host of many features. Each machine is shared by many users, and virtual machines are used to use these machines. With a large number of data centers and data centers with a large number of physical hosts. Two important issues in cloud environment are Load balancing and power consumption which solved by virtual machine migration. In earlier learnings, Artificial Bee Colony (ABC)'s policy could lead to a compromise between productivity and energy consumption. There are, however, two ways in the ABC-based Abstract based approach: (1) How to find effective solutions across the globe. (2) how to reduce the time to decide to distribute BM.To overcome this issue, this project develop one novel VM migration scheme called eeadoSelfCloud. This proposed method introduces Bee Lion Optimization (BLO) for VM allocation. Data Center Utilization, Average Node Utilization, Request Rejection Ration, Number of Hop Count and Power Consumption are employed as parameters for the proposed algorithm analysis. The experimental results indicate that the proposed algorithm does better than the other available methods.

scholarly journals Randomized routing of virtual machines in IaaS data centers

2019 ◽  
Vol 5 ◽  
pp. e211
Author(s):  
Hadi Khani ◽  
Hamed Khanmirza
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Power Consumption ◽  
Analytical Model ◽  
Data Center ◽  
Large Scale ◽  
Data Centers ◽  
Virtual Machines ◽  
General Distribution

Cloud computing technology has been a game changer in recent years. Cloud computing providers promise cost-effective and on-demand resource computing for their users. Cloud computing providers are running the workloads of users as virtual machines (VMs) in a large-scale data center consisting a few thousands physical servers. Cloud data centers face highly dynamic workloads varying over time and many short tasks that demand quick resource management decisions. These data centers are large scale and the behavior of workload is unpredictable. The incoming VM must be assigned onto the proper physical machine (PM) in order to keep a balance between power consumption and quality of service. The scale and agility of cloud computing data centers are unprecedented so the previous approaches are fruitless. We suggest an analytical model for cloud computing data centers when the number of PMs in the data center is large. In particular, we focus on the assignment of VM onto PMs regardless of their current load. For exponential VM arrival with general distribution sojourn time, the mean power consumption is calculated. Then, we show the minimum power consumption under quality of service constraint will be achieved with randomize assignment of incoming VMs onto PMs. Extensive simulation supports the validity of our analytical model.

Sign in / Sign up

Export Citation Format

Share Document