scholarly journals A Bit String Content Aware Chunking Strategy for Reduced CPU Energy on Cloud Storage

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Bin Zhou ◽  
ShuDao Zhang ◽  
Ying Zhang ◽  
JiaHao Tan
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
System Performance ◽  
Cloud Storage ◽  
High Performance ◽  
Factors Affecting ◽  
Redundant Data ◽  
Energy Factors ◽  
The Cost ◽  
Content Aware

In order to achieve energy saving and reduce the total cost of ownership, green storage has become the first priority for data center. Detecting and deleting the redundant data are the key factors to the reduction of the energy consumption of CPU, while high performance stable chunking strategy provides the groundwork for detecting redundant data. The existing chunking algorithm greatly reduces the system performance when confronted with big data and it wastes a lot of energy. Factors affecting the chunking performance are analyzed and discussed in the paper and a new fingerprint signature calculation is implemented. Furthermore, a Bit String Content Aware Chunking Strategy (BCCS) is put forward. This strategy reduces the cost of signature computation in chunking process to improve the system performance and cuts down the energy consumption of the cloud storage data center. On the basis of relevant test scenarios and test data of this paper, the advantages of the chunking strategy are verified.

Optimization of modular data center air conditioning system

2020 ◽  
Vol 34 (14n16) ◽  
pp. 2040123
Author(s):  
Yong-Liang Chen ◽  
Zi-Qiang Qin ◽  
Yao Li ◽  
Hai-Bo Wang ◽  
Sheryar Muhammad ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Data Center ◽  
Geometric Model ◽  
Side Wall ◽  
Pressure Jump ◽  
Air Conditioning System ◽  
Air Conditioners ◽  
Factors Affecting ◽  
Face To Face ◽  
Modular Data

In high-density data center, energy consumption is increasing dramatically. For reducing the energy consumption, CFD software, Fluent 15.0, is used to simulate the flow and temperature field distribution with [Formula: see text] turbulence model and fluid–solid coupling method. Fans on the back of racks are simplified as walls with a certain pressure jump. Severs are treated as solid heat sources and porous media. Simulation results reveal that the temperature distribution on the back of racks is not uniform when air conditioners are arranged face-to-face, and local high temperature points emerge near the side wall of air conditioners. Factors affecting cooling efficiency, such as location of air conditioners, speed of inlets, distance of racks, etc., need to be improved. Geometric model is optimized by using a diagonal rack arrangement and drilling holes on the side wall. Based on this, four different cases with various hot aisle distance are proposed. Single and double modular data center are both simulated. Results of new model are better than those of baseline model.

scholarly journals Custom Memory Design for Logic-in-Memory: Drawbacks and Improvements over Conventional Memories

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2291
Author(s):  
Fabrizio Ottati ◽  
Giovanna Turvani ◽  
Guido Masera ◽  
Marco Vacca
Keyword(s):  
Energy Consumption ◽  
High Performance ◽  
Data Exchange ◽  
Memory Cell ◽  
Cmos Process ◽  
Memory Array ◽  
Promising Solution ◽  
The Cost ◽  
Array Performance ◽  
Maximum Minimum

The speed of modern digital systems is severely limited by memory latency (the “Memory Wall” problem). Data exchange between Logic and Memory is also responsible for a large part of the system energy consumption. Logic-in-Memory (LiM) represents an attractive solution to this problem. By performing part of the computations directly inside the memory the system speed can be improved while reducing its energy consumption. LiM solutions that offer the major boost in performance are based on the modification of the memory cell. However, what is the cost of such modifications? How do these impact the memory array performance? In this work, this question is addressed by analysing a LiM memory array implementing an algorithm for the maximum/minimum value computation. The memory array is designed at physical level using the FreePDK 45nm CMOS process, with three memory cell variants, and its performance is compared to SRAM and CAM memories. Results highlight that read and write operations performance is worsened but in-memory operations result to be very efficient: a 55.26% reduction in the energy-delay product is measured for the AND operation with respect to the SRAM read one. Therefore, the LiM approach represents a very promising solution for low-density and high-performance memories.

scholarly journals Range smart cluster monitor based guesstimate approach for resource scheduling in small size clusters

2018 ◽  
Vol 7 (2) ◽  
pp. 837
Author(s):  
S Gokuldev ◽  
Jathin R
Keyword(s):  
Energy Consumption ◽  
Resource Sharing ◽  
System Performance ◽  
High Performance ◽  
Performance Metrics ◽  
Resource Scheduling ◽  
Turnaround Time ◽  
Experimental Result ◽  
Battery Life ◽  
Power Efficient

Performing scheduling of tasks with low energy consumption with high performance is one of the major concerns in distributed computing. Most of the existing systems have achieved improved energy efficiency but compromised with QoS metrics such as makespan and resource utilization. A resource scheduling strategy for wireless clusters is proposed by making careful considerations on decisions that would im-prove the battery life of nodes. The proposed strategy also incorporates monitoring system with in the clusters for optimizing the system performance as well as energy consumption. The system ensures “Any case zero loss" performance wherein each cluster will be monitored by at least one cluster monitor. This is implemented by using predictive calculation at each cluster monitor to communicate only if absolutely essential, during assigning jobs to resources, selecting optimal resources by assigning the jobs to the most power efficient resource among the available idle resources within the cluster. The experimental result ensures improved system performance with low power consumption in homogeneous computing environment. The resource sharing strategy is experimentally analyzed, considering the important performance metrics such as starvation deadline, turnaround time, miss hit count through simulations. Significant results were observed with improved efficiency.  

scholarly journals Intra- and Inter-Server Smart Task Scheduling for Profit and Energy Optimization of HPC Data Centers

2020 ◽  
Vol 10 (4) ◽  
pp. 32
Author(s):  
Sayed Ashraf Mamun ◽  
Alexander Gilday ◽  
Amit Kumar Singh ◽  
Amlan Ganguly ◽  
Geoff V. Merrett ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Data Center ◽  
High Performance ◽  
Data Centers ◽  
Virtual Machines ◽  
For Profit ◽  
Dynamic Voltage ◽  
High Power Consumption ◽  
Performance Computing

Servers in a data center are underutilized due to over-provisioning, which contributes heavily toward the high-power consumption of the data centers. Recent research in optimizing the energy consumption of High Performance Computing (HPC) data centers mostly focuses on consolidation of Virtual Machines (VMs) and using dynamic voltage and frequency scaling (DVFS). These approaches are inherently hardware-based, are frequently unique to individual systems, and often use simulation due to lack of access to HPC data centers. Other approaches require profiling information on the jobs in the HPC system to be available before run-time. In this paper, we propose a reinforcement learning based approach, which jointly optimizes profit and energy in the allocation of jobs to available resources, without the need for such prior information. The approach is implemented in a software scheduler used to allocate real applications from the Princeton Application Repository for Shared-Memory Computers (PARSEC) benchmark suite to a number of hardware nodes realized with Odroid-XU3 boards. Experiments show that the proposed approach increases the profit earned by 40% while simultaneously reducing energy consumption by 20% when compared to a heuristic-based approach. We also present a network-aware server consolidation algorithm called Bandwidth-Constrained Consolidation (BCC), for HPC data centers which can address the under-utilization problem of the servers. Our experiments show that the BCC consolidation technique can reduce the power consumption of a data center by up-to 37%.

scholarly journals Hybrid Optical Switching for Data Center Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Matteo Fiorani ◽  
Slavisa Aleksic ◽  
Maurizio Casoni
Keyword(s):  
Energy Consumption ◽  
Wavelength Division Multiplexing ◽  
Data Center ◽  
High Performance ◽  
Optical Switching ◽  
Transmission Efficiency ◽  
Optical Switches ◽  
Current Data ◽  
Service Differentiation ◽  
Data Center Network

Current data centers networks rely on electronic switching and point-to-point interconnects. When considering future data center requirements, these solutions will raise issues in terms of flexibility, scalability, performance, and energy consumption. For this reason several optical switched interconnects, which make use of optical switches and wavelength division multiplexing (WDM), have been recently proposed. However, the solutions proposed so far suffer from low flexibility and are not able to provide service differentiation. In this paper we introduce a novel data center network based on hybrid optical switching (HOS). HOS combines optical circuit, burst, and packet switching on the same network. In this way different data center applications can be mapped to the optical transport mechanism that best suits their traffic characteristics. Furthermore, the proposed HOS network achieves high transmission efficiency and reduced energy consumption by using two parallel optical switches. We consider the architectures of both a traditional data center network and the proposed HOS network and present a combined analytical and simulation approach for their performance and energy consumption evaluation. We demonstrate that the proposed HOS data center network achieves high performance and flexibility while considerably reducing the energy consumption of current solutions.

scholarly journals Energy-Efficient Reliability-Aware Scheduling Algorithm on Heterogeneous Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaoyong Tang ◽  
Weizhen Tan
Keyword(s):  
Energy Consumption ◽  
System Performance ◽  
High Performance ◽  
Scheduling Algorithm ◽  
Heterogeneous Systems ◽  
High Energy ◽  
Parallel Applications ◽  
Energy Aware ◽  
Simulation Performance ◽  
Energy Aware Scheduling

The amount of energy needed to operate high-performance computing systems increases regularly since some years at a high pace, and the energy consumption has attracted a great deal of attention. Moreover, high energy consumption inevitably contains failures and reduces system reliability. However, there has been considerably less work of simultaneous management of system performance, reliability, and energy consumption on heterogeneous systems. In this paper, we first build the precedence-constrained parallel applications and energy consumption model. Then, we deduce the relation between reliability and processor frequencies and get their parameters approximation value by least squares curve fitting method. Thirdly, we establish a task execution reliability model and formulate this reliability and energy aware scheduling problem as a linear programming. Lastly, we propose a heuristic Reliability-Energy Aware Scheduling (REAS) algorithm to solve this problem, which can get good tradeoff among system performance, reliability, and energy consumption with lower complexity. Our extensive simulation performance evaluation study clearly demonstrates the tradeoff performance of our proposed heuristic algorithm.

scholarly journals Regeneration of Hard Alloy Wastes by Low Energy Consumption and Environmentally Friendly Hydrothermal Technology

2015 ◽  
Vol 16 (1) ◽  
pp. 176-180
Author(s):  
S. F. Korablov
Keyword(s):  
Energy Consumption ◽  
High Performance ◽  
Waste Treatment ◽  
Practical Importance ◽  
Hydrothermal Solution ◽  
Low Energy ◽  
Hard Alloys ◽  
Core Technology ◽  
Low Energy Consumption ◽  
The Cost

Hard alloys are indispensable material for many branches of modern industry. However, even with the base composition (WC-Co) they are quite expensive due to the limited natural resources of cobalt and the complexity of their production from the minerals. Therefore, the collection and recycling of hard alloys waste have not only scientific but practical importance, taking into account that the cost of production of 1 ton of alloy from recovered waste comes to 20% cheaper than in the core technology. Existing methods of hard alloys waste treatment have several disadvantages, the main of which are high power consumption and big load on the environment. As a result of this research a high-performance, low-energy consumption, eco-friendly way for recycling of hard alloys waste has been proposed. According to this technology, in a first step the WC powder, and the solution containing cobalt salts were obtained by autoclaving at 230 °C in a mixture of HCl-H3PO4-HNO3 acids, and followed then metal cobalt  recovery from hydrothermal solution at temperatures of 110 – 160 °C.

scholarly journals Sensor Communication Model Using Cyber-Physical System Approach for Green Data Center

2019 ◽  
Vol 13 (10) ◽  
pp. 188
Author(s):  
Masnida Hussin ◽  
Raja Azlina Raja Mahmood ◽  
Mas Rina Mustaffa
Keyword(s):  
Energy Consumption ◽  
Resource Utilization ◽  
Data Center ◽  
High Performance ◽  
Room Temperature ◽  
Distributed Processing ◽  
Computing System ◽  
Communication Model ◽  
Computational Performance ◽  
Green Data Center

Energy consumption in distributed computing system gains a lot of attention recently after its processing capacity becomes significant for better business and economic operations. Comprehensive analysis of energy efficiency in high-performance data center for distributed processing requires ability to monitor a proportion of resource utilization versus energy consumption. In order to gain green data center while sustaining computational performance, a model of energy efficient cyber-physical communication is proposed. A real-time sensor communication is used to monitor heat emitted by processors and room temperature. Specifically, our cyber-physical communication model dynamically identifies processing states in data center while implying a suitable air-conditioning temperature level. The information is then used by administration to fine-tune the room temperature according to the current processing activities. Our automated triggering approach aims to improve edge computing performance with cost-effective energy consumption. Simulation experiments show that our cyber-physical communication achieves better energy consumption and resource utilization compared with other cooling model.

scholarly journals Estimation of operating costs and energy consumption in beneficiation of alluvial gold deposits

2020 ◽  
Vol 192 ◽  
pp. 01007
Author(s):  
Ruslan Seryi ◽  
Vladimir Alekseev
Keyword(s):  
Energy Consumption ◽  
Gold Deposits ◽  
Operating Costs ◽  
Liquid Ratio ◽  
Screening Process ◽  
Alluvial Deposits ◽  
Factors Affecting ◽  
Alluvial Gold ◽  
Processing Plants ◽  
The Cost

There are many scientific and practical works related to the identification and assessment of factors affecting the efficiency of beneficiation of placer sands at the sluice box, while the energy consumption of the beneficiation process, as well as assessing the efficiency of the sand screening process and the cost of maintaining the devices, is given little attention. Studies of the energy consumption of sand washing, carried out at several alluvial deposits, made it possible to identify the most energyconsuming devices, as well as to compare the energy expended for transporting rock through processing plants and to provide solid to liquid ratio during beneficiation at sluice boxes.

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