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Adaptive Scheduling on Power-Aware Managed Data-Centers Using Machine Learning

Josep Ll. Berral, Ricard Gavalda, Jordi Torres
2011 2011 IEEE/ACM 12th International Conference on Grid Computing  
Energy-related costs have become one of the major economic factors in IT data-centers, and companies and the research community are currently working on new efficient poweraware resource management strategies  ...  The machine learning is used to estimate the initially unknown parameters of the mathematical model.  ...  ACKNOWLEDGMENT We would like to thank toÍñigo Goiri, Ferran Julià, Ramon Nou, J.Oriol Fitó and Jordi Guitart from UPC-BSC for lending us their testbed workbench in order to evaluate our methods.  ... 
doi:10.1109/grid.2011.18 dblp:conf/grid/BerralGT11 fatcat:mwb4opxhaveythjaae7vg4l3ve

Toward Energy-Aware Scheduling Using Machine Learning [chapter]

Josep Ll. Berral, Iñigo Goiri, Ramon Nou, Ferran Julià, Josep O. Fitó, Jordi Guitart, Ricard Gavaldá, Jordi Torres
2012 Energy-Efficient Distributed Computing Systems  
CONCLUSIONS ON INTELLIGENT POWER-AWARE SELF-MANAGEMENT As discussed in this chapter, data-center power-aware management techniques are mainly focused on the autonomic computing field, so power optimization  ...  EXPERIENCES OF APPLYING ML ON POWER-AWARE SELF-MANAGEMENT After looking at all the works and publications referring to the new techniques improving power-aware self-managed systems using data mining and  ... 
doi:10.1002/9781118342015.ch8 fatcat:mtzomhmp5rcyxol4q6zwipvt7a

Towards energy-aware scheduling in data centers using machine learning

Josep Ll. Berral, Íñigo Goiri, Ramón Nou, Ferran Julià, Jordi Guitart, Ricard Gavaldà, Jordi Torres
2010 Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking - e-Energy '10  
As energy-related costs have become a major economical factor for IT infrastructures and data-centers, companies and the research community are being challenged to find better and more efficient power-aware  ...  resource management strategies.  ...  ENERGY-AWARE MANAGEMENT Our approach uses two different mechanisms in order to reduce the power consumption of a data center while respecting the different SLAs.  ... 
doi:10.1145/1791314.1791349 dblp:conf/eenergy/BerralGNJGGT10 fatcat:wb65jkzg4fhejh3ljqcpcvmj4u

workload forecasting and resource management models based on machine learning for cloud computing environments [article]

Deepika Saxena, Ashutosh Kumar Singh
2021 arXiv   pre-print
Thereafter, a thorough survey of existing state-of-the-art contributions empowering machine learning based approaches in the field of cloud workload prediction and resource management are rendered.  ...  A conceptual framework for workload forecasting and resource management, categorization of existing machine learning based resources allocation techniques, and major challenges of inefficient distribution  ...  traffic pattern [15] . • Task Scheduling At cloud data centers, virtual machine allocation is done prior to the jobs arrival.  ... 
arXiv:2106.15112v1 fatcat:cngak4jdvzhjlpfa4jibfne23m

Energy-Aware High Performance Computing: A Taxonomy Study

Chang Cai, Lizhe Wang, Samee U. Khan, Jie Tao
2011 2011 IEEE 17th International Conference on Parallel and Distributed Systems  
This paper is devoted to categorize energy-aware computing methods for the high-end computing infrastructures, such as servers, clusters, data centers, and Grids/Clouds.  ...  Based on a taxonomy of methods and system scales, this paper reviews the current status of energy-aware HPC research and summarizes open questions and research directions of software architecture for future  ...  Power-aware methods Some work focuses on the energy aware management in a cluster, server farms or a data center [4] , [6] , [8] , [9] , [14] , [15] , [39] .  ... 
doi:10.1109/icpads.2011.59 dblp:conf/icpads/CaiWKT11 fatcat:2frg66nlyrf6djsbnmnfggs57u

2021 Index IEEE Transactions on Parallel and Distributed Systems Vol. 32

2022 IEEE Transactions on Parallel and Distributed Systems  
GML: Efficiently Auto-Tuning Flink's Configurations Via Guided Machine Learning. Guo, Y., +, TPDS Dec. 2021 2921-2935 Network-Aware Locality Scheduling for Distributed Data Operators in Data Centers.  ...  ., +, TPDS July 2021 1578-1590 Network-Aware Locality Scheduling for Distributed Data Operators in Data Centers.  ...  Graph coloring Feluca: A Two-Stage Graph Coloring Algorithm With Color-Centric Paradigm on GPU. Zheng, Z., +,  ... 
doi:10.1109/tpds.2021.3107121 fatcat:e7bh2xssazdrjcpgn64mqh4hb4

Using of Machine Learning into Cloud Environment (A Survey): Managing and Scheduling of Resources in Cloud Systems

Elham Hormozi, Hadi Hormozi, Mohammad Kazem Akbari, Morteza Sargolzai Javan
2012 2012 Seventh International Conference on P2P, Parallel, Grid, Cloud and Internet Computing  
In this survey, we investigate the effects using the concepts of machine learning on cloud environments, e.g. automated resource allocation mechanism, intelligently managing and allocating resources with  ...  This technology holds a vast scope of using the various aspects of machine learning for increased performance and solving some of the challenges in front of the research community.  ...  Further to this, we gratefully acknowledge those in the Cloud Computing Research Center at the Computer Engineering and Information Technology department, AmirKabir University, IRAN.  ... 
doi:10.1109/3pgcic.2012.69 dblp:conf/3pgcic/HormoziHAJ12 fatcat:j5zh7osjybge7gng27wsbgzvn4

2019 Index IEEE Transactions on Network and Service Management Vol. 16

2019 IEEE Transactions on Network and Service Management  
., +, T-NSM Sept. 2019 1059-1070 Ismael: Using Machine Learning to Predict Acceptance of Virtual Clusters in Data Centers.  ...  ., +, T-NSM Sept. 2019 1170-1183 Ismael: Using Machine Learning to Predict Acceptance of Virtual Clusters in Data Centers.  ...  Stochastic programming Tiered Cloud Storage via Two-Stage, Latency-Aware Bidding. Zhang  ... 
doi:10.1109/tnsm.2019.2960621 fatcat:zdo7i4plobaqxp54v7624wkrxi

Hybrid Approach for Resource Scheduling in Green Clouds

Keffy Goyal, Supriya Kinger
2013 International Journal of Computer Applications  
To save the power, temperature aware resource scheduler is used.  ...  The proposed work is on a hybrid approach for both temperature and power aware resource scheduling.  ...  Lathe Wang [8] proposed a method to predict a workload's thermal effect on a data center, which will be suitable for realtime scenarios and used machine learning techniques, such as artificial neural  ... 
doi:10.5120/12979-0201 fatcat:uy4elvg6pzfyxj3qcux35u7e6u

2019 Index IEEE Transactions on Cloud Computing Vol. 7

2020 IEEE Transactions on Cloud Computing  
-Dec. 2019 1139-1151 Computer centers Adia: Achieving High Link Utilization with Coflow-Aware Scheduling in Data Center Networks.  ...  ., +, TCC April -June 2019 403-414 Telecommunication scheduling Adia: Achieving High Link Utilization with Coflow-Aware Scheduling in Data Center Networks.  ... 
doi:10.1109/tcc.2020.2969066 fatcat:uxhqc6ryenen7brk6qnejnciaa

Survey on Energy Efficiency in Cloud Computing

Backialakshmi M, Hemavathi N
2016 Journal of Information Technology & Software Engineering  
Techniques for energy efficiency comprises virtualization, energy-aware routing in DCNs, dynamic voltage/frequency scaling, rate adaptation, dynamicpower management (DPM), energy-aware scheduling methodsand  ...  Energy-credit scheduler: Energy-aware virtual machine scheduler for cloud systems This model estimates the energy consumption of a virtual machine based on in-processor events generated by the virtual  ... 
doi:10.4172/2165-7866.1000164 fatcat:4b5texop6nab5ldql6zjpderdm

Infrastructure and Energy Conservation in Big Data Computing: A Survey

Ewa Niewiadomska-Szynkiewicz, Michał P. Karpowicz
2019 Journal of Telecommunications and Information Technology  
Development of software frameworks, include smart calculation, communication management, data decomposition and allocation algorithms is clearly one of the major technological challenges we are faced with  ...  Progress in life, physical sciences and technology depends on efficient data-mining and modern computing technologies.  ...  The main Apache Spark use cases include the following: streaming data, machine learning, fog computing, etc.  ... 
doi:10.26636/jtit.2019.132419 fatcat:oeokamnhxbdmbkoytyrpqgilfy

2020 Index IEEE Transactions on Parallel and Distributed Systems Vol. 31

2021 IEEE Transactions on Parallel and Distributed Systems  
., +, TPDS Jan. 2019 63-78 Distributed Bottleneck-Aware Coflow Scheduling in Data Centers.  ...  Cai, H., +, TPDS July 2019 1449-1463 Dependency-Aware Network Adaptive Scheduling of Data-Intensive Paral-Efficient Data Placement and Replication for QoS-Aware Approximate Query Evaluation of Big Data  ... 
doi:10.1109/tpds.2020.3033655 fatcat:cpeatdjlpzhqdersvsk5nmzjkm

A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning

Stanly Jayaprakash, Manikanda Devarajan Nagarajan, Rocío Pérez de Prado, Sugumaran Subramanian, Parameshachari Bidare Divakarachari
2021 Energies  
A key aspect for cloud data centers is to achieve management methods to reduce energy consumption, increasing the profit and reducing the environmental impact, which is critical in the deployment of leading-edge  ...  In this review, various clustering, optimization, and machine learning methods used in cloud resource allocation to increase the energy efficiency and performance are analyzed, compared and classified.  ...  Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/en14175322 fatcat:ivxj4gyobbdvlafyzbjcace76y

An adaptive power management framework for autonomic resource configuration in cloud computing infrastructures

Ziming Zhang, Qiang Guan, Song Fu
2012 2012 IEEE 31st International Performance Computing and Communications Conference (IPCCC)  
Based on these techniques, we are able to test various job scheduling strategies and develop resource management approaches to enhance the systems' power efficiency.  ...  between power usage and machine configurations.  ...  The data center owner aims to make the best use of the power budget to generate useful An overall power budget is allocated to the data center and each member cluster is allocated with an adjustable power  ... 
doi:10.1109/pccc.2012.6407738 dblp:conf/ipccc/ZhangGF12 fatcat:5d3gpczvknbjjb2ilk6zrys7si
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