Seven Pillars to Achieve Energy Efficiency in High-Performance Computing Data Centers

Hussain, Sardar Mehboob; Wahid, Abdul; Shah, Munam Ali; Akhunzada, Adnan; Khan, Faheem; Amin, Noor Ul; Arshad, Saba; Ali, Ihsan

doi:10.1007/978-3-319-99966-1_9

Cited by 22 publications

(17 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors of [40,41] focused their study on the description of some evolutionary changes in HPC hardware, and how recent hardware trends pose challenges associated with Exascale computing hardware development. Reference [42] examined energy management problems, challenges, and potential solutions for the period 2010-2016 by concentrating on the energy usage of data centers and HPC systems. The EE issues currently affecting data centers were highlighted, potential threats identified, as well as several short-term predictions.…”

Section: Comparative Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence: An Energy Efficiency Tool for Enhanced High performance computing

et al. 2020

View full text Add to dashboard Cite

Power-consuming entities such as high performance computing (HPC) sites and large data centers are growing with the advance in information technology. In business, HPC is used to enhance the product delivery time, reduce the production cost, and decrease the time it takes to develop a new product. Today’s high level of computing power from supercomputers comes at the expense of consuming large amounts of electric power. It is necessary to consider reducing the energy required by the computing systems and the resources needed to operate these computing systems to minimize the energy utilized by HPC entities. The database could improve system energy efficiency by sampling all the components’ power consumption at regular intervals and the information contained in a database. The information stored in the database will serve as input data for energy-efficiency optimization. More so, device workload information and different usage metrics are stored in the database. There has been strong momentum in the area of artificial intelligence (AI) as a tool for optimizing and processing automation by leveraging on already existing information. This paper discusses ideas for improving energy efficiency for HPC using AI.

show abstract

Section: Comparative Studiesmentioning

confidence: 99%

“…Hussain, Wahid, Shah, Akhunzada, and Arshad, 2018, [42] • This paper analyzed the problems, challenges, and solutions proposed for the period 2010-2016 by focusing on data center and HPC energy use. They classified existing energy management issues currently faced by data centers.…”

Section: Reference Objectivesmentioning

confidence: 99%

Artificial Intelligence: An Energy Efficiency Tool for Enhanced High performance computing

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Honeycomb and Hexagonal networks [1] have been widely studied in various research domains, such as wireless sensor networks, wireless networks [2][3][4][5][6] and cellular networks, in order to study and analyze various issues like routing, [7][8][9] location management and target tracking [10][11][12], energy conservation [13][14][15], and interference estimation [16]. These networks and application domains are applicable to the IoT as well [17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Edge Metric Dimension of Honeycomb and Hexagonal Networks for IoT

Abbas¹,

Raza²,

Siddiqui³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

Wireless Sensor Network (WSN) is considered to be one of the fundamental technologies employed in the Internet of things (IoT); hence, enabling diverse applications for carrying out real-time observations. Robot navigation in such networks was the main motivation for the introduction of the concept of landmarks. A robot can identify its own location by sending signals to obtain the distances between itself and the landmarks. Considering networks to be a type of graph, this concept was redefined as metric dimension of a graph which is the minimum number of nodes needed to identify all the nodes of the graph. This idea was extended to the concept of edge metric dimension of a graph G, which is the minimum number of nodes needed in a graph to uniquely identify each edge of the network. Regular plane networks can be easily constructed by repeating regular polygons. This design is of extreme importance as it yields high overall performance; hence, it can be used in various networking and IoT domains. The honeycomb and the hexagonal networks are two such popular mesh-derived parallel networks. In this paper, it is proved that the minimum landmarks required for the honeycomb network HC(n), and the hexagonal network HX (n) are 3 and 6 respectively. The bounds for the landmarks required for the hex-derived network HDN1(n) are also proposed.

show abstract

“…In WSN [4], data are communicated through multicasting, unicasting, broadcasting, any casting, and flooding, but the discussion is limited to multicasting, unicasting and flooding in this paper. In multicasting and unicasting, reliability decreases due to regular changes in the topology because only a particular route exists between the receiver and a source but at the cost of less energy consumption [5][6][7]. On the other hand, flooding distributes the data in the group immediately, as the data are received by any mesh member, which gives reliability at the cost of higher overhead to the group and the network [8,9].…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Reliable Algorithm for Wireless Sensor Network

Khan

Ahmad

Guruler

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

In wireless sensor networks (WSN), flooding increases the reliability in terms of successful transmission of a packet with higher overhead. The flooding consumes the resources of the network quickly, especially in sensor networks, mobile ad-hoc networks, and vehicular ad-hoc networks in terms of the lifetime of the node, lifetime of the network, and battery lifetime, etc. This paper aims to develop an efficient and reliable protocol by using multicasting and unicasting to overcome the issue of higher overhead due to flooding. Unicasting is used when the desired destination is at a minimum distance to avoid an extra overhead and increases the efficiency of the network in terms of overhead and energy because unicasting is favorable where the distance is minimum. Similarly, multicasting is used when the desired destination is at maximum distance and increases the network’s reliability in terms of throughput. The results are implemented in the Department of Computer Science, Bacha Khan University Charsadda (BKUC), Pakistan, as well as in the Network Simulator-2 (NS-2). The results are compared with benchmark schemes such as PUMA and ERASCA, and based on the results, the performance of the proposed approach is improved in terms of overhead, throughput, and packet delivery fraction by avoiding flooding.

show abstract

Seven Pillars to Achieve Energy Efficiency in High-Performance Computing Data Centers

Cited by 22 publications

References 21 publications

Artificial Intelligence: An Energy Efficiency Tool for Enhanced High performance computing

Artificial Intelligence: An Energy Efficiency Tool for Enhanced High performance computing

Edge Metric Dimension of Honeycomb and Hexagonal Networks for IoT

An Efficient and Reliable Algorithm for Wireless Sensor Network

Contact Info

Product

Resources

About