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Computing
Erschienen in: Computing 7/2016

01.07.2016

A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems

verfasst von: Abdul Hameed, Alireza Khoshkbarforoushha, Rajiv Ranjan, Prem Prakash Jayaraman, Joanna Kolodziej, Pavan Balaji, Sherali Zeadally, Qutaibah Marwan Malluhi, Nikos Tziritas, Abhinav Vishnu, Samee U. Khan, Albert Zomaya

Erschienen in: Computing | Ausgabe 7/2016

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Abstract

In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources (servers, storage disks, and networks, and the like) is a complex problem due to the presence of heterogeneous application (e.g., content delivery networks, MapReduce, web applications, and the like) workloads having contentious allocation requirements in terms of ICT resource capacities (e.g., network bandwidth, processing speed, response time, etc.). Several recent papers have tried to address the issue of improving energy efficiency in allocating cloud resources to applications with varying degree of success. However, to the best of our knowledge there is no published literature on this subject that clearly articulates the research problem and provides research taxonomy for succinct classification of existing techniques. Hence, the main aim of this paper is to identify open challenges associated with energy efficient resource allocation. In this regard, the study, first, outlines the problem and existing hardware and software-based techniques available for this purpose. Furthermore, available techniques already presented in the literature are summarized based on the energy-efficient research dimension taxonomy. The advantages and disadvantages of the existing techniques are comprehensively analyzed against the proposed research dimension taxonomy namely: resource adaption policy, objective function, allocation method, allocation operation, and interoperability.
Vorheriger Artikel Managing the complex data center environment: an Integrated Energy-aware Framework

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Literatur
1.
Mell P, Grance T (2009) Definition of cloud computing. Technical report SP 800–145, National Institute of Standard and Technology (NIST), Gaithersburg, MD
2.
Wang L, Kunze M, Tao J, Laszewski G (2011) Towards building a cloud for scientific applications. Adv Eng Softw 42(9):714–722CrossRef
3.
Wang L, Laszewski G, Younge AJ, He X, Kune M, Tao J, Fu C (2010) Cloud computing: a perspective study. New Gener Comput 28(2):137–146CrossRefMATH
4.
5.
Wang L, Chen D, Zhao J, Tao J (2012) Resource management of distributed virtual machines. IJAHUC 10(2):96–111CrossRef
6.
Wang L, Chen D, Huang F (2011) Virtual workflow system for distributed collaborative scientific applications on Grids. Comput Electr Eng 37(3):300–310CrossRef
7.
Wang L, Laszewski L, Chen D, Tao J, Kunze M (2010) Provide virtual machine information for grid computing. IEEE Trans Syst Man Cybern Part A 40(6):1362–1374CrossRef
8.
Nathuji R, Kansal A, Ghaffarkhah A (2010) Q-Clouds: managing performance interference effects for QoS-aware clouds. In: 5th European conference on computer system (EuroSys’10), pp 237–250
9.
10.
Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M (2010) A view of cloud computing. Commun ACM 53(4):50–58CrossRef
11.
Sadashiv N, Kumar S (2011) Cluster, grid and cloud computing: a detailed comparison. In: 6th international conference on computer science and education (ICCSE 2011), pp 477–482
12.
Jansen W (2011) Cloud hooks: security and privacy issues in cloud computing. In: 44th Hawaii international conference on systems science (HICSS), pp 1–10
13.
Barroso LA, Hölzle U (2009) The datacenter as a computer: an introduction to the design of warehouse-scale machines, 1st edn. In: Hill MD (ed) Morgan and Claypool Publishers, University of Wisconsin, Madison
14.
Berl A, Gelenbe E, Girolamo MD, Giuliani G, Meer HD, Dang MQ, Pentikousis K (2010) Energy-efficient cloud computing. Comput J 53(7):1045–1051CrossRef
15.
Srikantaiah S, Kansal A, Zhao F (2008) Energy aware consolidation for cloud computing. In: Conference on power aware computer and systems (HotPower ’08)
16.
17.
18.
Paradiso JA, Starner T (2005) Energy scavenging for mobile and wireless electronics. Pervasive Comput 4(1):18–27CrossRef
19.
Elnozahy M, Kistler M, Rajamony R (2002) Energy-efficient server clusters. Power aware computer systems, vol 2325. Springer, Berlin, pp 179–197
20.
Sharma V, Thomas A, Abdelzaher T, Skadron K (2003) Power-aware QoS management in web servers. In: Real-time systems symposium (RTSS 2003), pp 63–72
21.
Horvath T, Abdelzaher T, Skadron K, Liu X (2007) Dynamic voltage scaling in multitier web servers with end-to-end delay control. IEEE Trans Comput 56(4):444–458MathSciNetCrossRef
22.
Liu X, Shenoy P, Gong W (2004) A time series-based approach for power management in mobile processors and disks. In: 14th international workshop on network and operating systems support for digital audio and video (NOSSDAV ’04), pp 74–79
23.
Steere DC, Goel A, Gruenberg J, Mcnamee D, Pu C, Walpole J (1999) A feedback-driven proportion allocator for real-rate scheduling. In: Third symposium on operating system design and implementation (OSDI), pp 145–158
24.
Fujiwara I, Aida K, Ono I (2009) Market based resource allocation for distributed computing. IPSJ SIG Technical Report 1, 34
25.
Wei G, Vasilakos A, Zheng Y, Xiong N (2009) A game-theoretic method of fair resource allocation for cloud computing services. J Supercomput 54(2):252–269CrossRef
26.
Shu W (2007) Optimal resource allocation on grid computing using a quantum chromosomes genetic algorithm. In: IET conference on wireless, mobile and sensor networks (CCWMSN07), pp 1059–1062
27.
Ismail L, Mills B, Hennebelle A (2008) A formal model of dynamic resource allocation in grid computing environment. In: 9th ACIS international conference on software engineering, artificial intelligence, networking, and parallel/distributed computing (SNPD ’08), pp 685–693
28.
Huang Y, Chao B (2001) A priority-based resource allocation strategy in distributed computing networks. J Syst Softw 58(3):221–233CrossRef
29.
Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener Comput Syst 28:755–768CrossRef
30.
Beloglazov A, Buyya R, Lee YC, Zomaya AY (2011) A taxonomy and survey of energy-efficient data centers and cloud computing systems. Adv Compt 82:47–111
31.
Buyya R, Broberg J, Goscinski A (2011) Cloud computing principles and paradigms. Wiley, HobokenCrossRef
32.
Malet B, Pietzuch P (2010) Resource allocation across multiple cloud data centres. In: 8th international workshop on middleware for grids, clouds and e-science (MGC ’10), pp 1–6
33.
Demchenko Y, Ham J, Strijkers R, Ghijsen M, Ngo C, Cristea M (2011) Generic architecture for cloud infrastructure as a service (IaaS) provisioning model, Technical report SNE-UVA-2011-03, System and Network Engineering Group, University van Amsterdam
34.
35.
Gupta M, Singh S (2003) Greening of the internet. In: Applications technology of architecture, protocols and computer communication, pp 19–26
36.
37.
Singh T, Vara P (2009) Smart metering the clouds. In: 18th IEEE international workshops on enabling technologies: infrastructures for collaborative enterprises, pp 66–71
38.
Baliga J, Ayre R, Hinton K, Sorin W, Tucker R (2009) Energy consumption in optical IP networks. J Lightweight Technol 27(13):2391–2403CrossRef
39.
Tamm O, Hermsmeyer C, Rush A (2010) Eco-sustainable system and network architectures for future transport networks. Bell Labs Tech J 14(4):311–327CrossRef
40.
Vukovic A (2005) Datacenters: network power density challenges. J ASHRAE 47:55–59
41.
Liu J, Zhao F, Liu X, He W (2009) Challenges towards elastic power management in internet datacenters. In: IEEE international conference on distributed systems, pp 65–72
42.
Chase J, Anderson D, Thakur P, Vahdat A (2001) Managing energy and server resources in hosting centers. In: 18th symposium on operating systems principles (SOSP ’01), pp 103–116
43.
Hermenier F, Loriant N, Menaud J (2006) Power management in grid computing with Xen. Lecture notes in computer science. Springer, Berlin
44.
Cook G, Horn J (2011) How dirty is your data. GreenPeace International, Amsterdam
45.
Ranjan R, Benatallah B (2012) Programming cloud resource orchestration framework: operations and research challenges. CoRR abs/1204.2204
46.
Duy TVT, Duy S, Inoguchi Y (2010) Performance evaluation of a green scheduling algorithm for energy savings in cloud computing. In: 2010 IEEE International Symposium on Parallel and distributed processing, workshops and PhD forum (IPDPSW), pp 1–8, 19–23
47.
Mezmaz M-S, Kessaci Y, Lee YC, Melab N, Talbi E-G, Zomaya AY, Tuyttens D (2010) A parallel island-based hybrid genetic algorithm for precedence-constrained applications to minimize energy consumption and makespan. In: GRID, pp 274–281
48.
Hussin M, Lee YC, Zomaya AY (2011) Efficient energy management using adaptive reinforcement learning-based scheduling in large-scale distributed systems. In: ICPP, pp 385–393
49.
Kalyvianaki E (2009) Resource provisioning for virtualized server applications. Technical Report UCAM-CL-TR-762, Computer Laboratory, University of Cambridge
50.
Chen Y, Gmach D, Arlitt M, Marwah M, Gandhi A (2011) Minimizing data center SLA violations and power consumption via hybrid resource provisioning. In: Second international green computing conference (IGCC 2011), pp 1–8
51.
Tan CH, Luo M, Zhao YZ (2010) Multi-agent approach for dynamic resource allocation. SIMTech technical reports (STR_V11_N3_03_MEC), vol 11, No. 3
52.
Xie T, Wilamowski B (2011) Recent advances in power aware design. In: 37th annual conference on IEEE industrial electronics society IECON 2011, pp 4632–4635
53.
Fu S (2005) Service migration in distributed virtual machines for adaptive computing. In: International conference on parallel processing (ICPP 2005), pp 358–365
54.
Singh R, Sharma U, Cecchet E, Shenoy P (2010) Autonomic mix-aware provisioning for non-stationary data center workloads. In: Proceedings of the 7th IEEE international conference on autonomic computing and communication (ICAC ’10)
55.
Moreno IS, Xu J (2011) Energy-efficiency in cloud computing environments: towards energy savings without performance degradation. Int J Cloud Appl Comput 1(1):17–33
56.
Poladian V, Garlan D, Shaw M, Satyanarayanan M, Schmerl B, Sousa J (2007) Leveraging resource prediction for anticipatory dynamic configuration. In: Proceedings of the first international conference on self-adaptive and self-organizing systems (SASO ’07)
57.
Tang Q, Gupta S, Varsamopoulos G (2008) Energy-efficient, thermal-aware task scheduling for homogeneous, high performance computing data centers: a cyber-physical approach. IEEE Trans Parallel Distrib Syst 19(11):1458–1472CrossRef
58.
Goldman C, Reid M, Levy R, Silverstein A (2010) Coordination of energy efficiency and demand response. Environmental Energy Technologies Division, Berkeley National Laboratory
59.
Khargharia B, Hariri S, Yousif MS (2008) Autonomic power and performance management for computing systems. Clust Comput 11(2):167–181CrossRef
60.
Hung W-L, Xie Y, Vijaykrishnan N, Kandemir M, Irwin MJ (2005) Thermal-aware task allocation and scheduling for embedded systems. In: Proceedings of the conference on design, automation and test in Europe (DATE ’05), vol 2, pp 898–899
61.
Vasic N, Scherer T, Schott W (2010) Thermal-aware workload scheduling for energy efficient data centers. In: 7th international conference on autonomic computing (ICAC ’10), pp 169–174
62.
Cai C, Wang L, Khan SU, Jie T (2011) Energy-aware high performance computing—a taxonomy study. In: 17th international conference on parallel and distributed systems (ICPADS), pp 953–958
63.
Verma A, Ahuja P, Neogi A (2008) pMapper: power and migration cost aware application placement in virtualized systems. In: 9th ACM/IFIP/USENIX international conference on middleware (Middleware ’08), pp 243–264
64.
Nathuji R, Schwan K (2007) VirtualPower: coordinated power management in virtualized enterprise systems. In: 21st ACM SIGOPS symposium on operating systems principles (SOSP’07), pp 265–278
65.
Lee Y, Zomaya A (2010) Energy efficient utilization of resources in cloud computing systems. J Supercomput 1(13):1–13CrossRef
66.
Torres J, Carrera D, Hogan K, Gavaldà R, Beltran V, Poggi N (2008) Reducing wasted resources to help achieve green data centers. In: IEEE international symposium on parallel and distributed proceedings (IPDPS 2008), pp 1–8
67.
Subrata R, Zomaya AY, Landfeldt B (2010) Cooperative power-aware scheduling in grid computing environments. J Parallel Distrib Comput 70(2):84–91CrossRefMATH
68.
Mazzucco M, Dyachuk D, Deters R (2010) Maximizing cloud providers’ revenues via energy aware allocation policies. In: 3rd international conference on cloud computing (CLOUD), pp 131–138
69.
Raghavendra R, Ranganathan P, Talwar V, Wang Z, Zhu X (2008) No “power” struggles: coordinated multi-level power management for the data center. SIGARCH Comput Archit News 36(1):48–59CrossRef
70.
Kusic D, Kephart JO, Hanson JE, Kandasamy N, Jiang G (2009) Power and performance management of virtualized computing environments via look ahead control. Cluster Comput 12(1):1–15CrossRef
71.
Cardosa M, Korupolu M, Singh A (2009) Shares and utilities based power consolidation in virtualized server environments. In: 11th IFIP/IEEE integrated network management (IM 2009), pp 327–334
72.
Gandhi A, Harchol-Balter M, Das R, Lefurgy C (2009) Optimal power allocation in server farms. In: 11th international joint conference on measurement and modeling of computer systems (SIGMETRICS ’09), pp 157–168
73.
Gong J, Xu C-Z (2010) A gray-box feedback control approach for system-level peak power management. In: 39th international conference on parallel proceedings (ICPP’10), San Diego, CA
74.
Csorba MJ, Meling H, Heegaard PE (2010) Ant system for service deployment in private and public clouds. In: 2nd workshop on bio-inspired algorithms for distributed systems (BADS ’10), pp 19–28
75.
Heegaard P, Helvik B, Wittner O (2008) The cross entropy ant system for network path management. Telektronikk 104(1):19–40
76.
Grewal M, Andrews A (2010) Applications of Kalman filtering in aerospace 1960 to the present. Control Syst 30(3):69–78MathSciNetCrossRef
Metadaten
Titel
A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems
verfasst von
Abdul Hameed
Alireza Khoshkbarforoushha
Rajiv Ranjan
Prem Prakash Jayaraman
Joanna Kolodziej
Pavan Balaji
Sherali Zeadally
Qutaibah Marwan Malluhi
Nikos Tziritas
Abhinav Vishnu
Samee U. Khan
Albert Zomaya
Publikationsdatum
01.07.2016
Verlag
Springer Vienna
Erschienen in
Computing / Ausgabe 7/2016
Print ISSN: 0010-485X
Elektronische ISSN: 1436-5057
DOI
https://doi.org/10.1007/s00607-014-0407-8