E2DR: Energy Efficient Data Replication in Data Grid
محورهای موضوعی : Cloud, Cluster, Grid and P2P ComputingKobra Bagheri 1 , Mehran Mohsenzadeh 2
1 - Department of Computer, Science and Research Branch Islamic Azad University, Tehran , Iran.
2 - Department of Computer, Science and Research Branch Islamic Azad University, Tehran, Iran.
کلید واژه: Data Replication, data grid, cloudsim, energy efficient,
چکیده مقاله :
Abstract— Data grids are an important branch of gird computing which provide mechanisms for the management of large volumes of distributed data. Energy efficiency has recently emerged as a hot topic in large distributed systems. The development of computing systems is traditionally focused on performance improvements driven by the demand of client's applications in scientific and business domains. High energy consumption in computer systems leads to their limited performance because of the increased consumption of carbon dioxide and amount of electricity bills. Thus, the goal of design of computer systems has been shifted to power and energy efficiency. Data grids can solve large scale applications that require a large amount of data. Data replication is a common solution to improve availability and file access time in such environments. This solution replicates the data file in many different sites. In this paper, a new data replication method is proposed that is not only data aware, but also is energy efficient. Simulation results with CLOUDSIM show that the proposed method gives better energy consumption, average response time, and network usage than other algorithms and prevents the unnecessary creation of replica, which leads to efficient storage usage.
[1] susan v. Vrbsky, ming lei, karl smith and jeff byrd ,data replication and power consumption in Data grids, 2nd ieee international conference on cloud computing technology and science,ieee(2010).
[2] Tarek Hamrouni,Sarra Slimani ,A critical survey of data grid replication strategies based on data mining techniques ,ICCS 2015 International Conference on Computational Sience volume 55, (2015), 2779 – 2788.
[3] somayeh abdi and somayeh mohamadi, two level job scheduling and data Replication in data grid, international journal of grid computing & applications (ijgca) vol.1, no.1(2010).
[4] ming tang, bu-sung lee, xueyan tang, chai-kiat yeo , The impact of data replication on Job scheduling performance in the data grid, future generation computer systems, volume 22, issue 3 ( 2006), 254-268.
[5] najme mansouri, gholam hosein dastghaibyfard, A dynamic replica management strategy in data grid, journal of network and computer applications 35 (2012),siencedirect,( 2012), 1297–1303.
[6] JEMAL ABAWAJY ,DATA REPLICATION APPROACH WITH CONSISTENCY GUARANTEE FOR DATA GRID, IEEE TRANSACTION ON COMPUTERS , (2015), 1-17.
[7] Alireza Souri , Amir Masoud Rahmani, Survey for replica placement techniques in data grid environment , I.J.Modern Education and computer science,2014, (2014), 46-51.
[8] anton beloglazov, rajkumar buyya, young choon lee, albert zomaya,”a taxonomy and survey of Energy-efficient data centers And cloud computing systems,elsevier , (2011), 47-111.
[9] x. Fan, w.d. weber, l.a. barroso, power provisioning for a warehouse-sized computer, In: proceedings of the 34th annual international symposium on computer architecture (isca2007), acm new york, ny, usa, (2007), 13–23.
[10] m. Allalouf, y. Arbitman, m. Factor, r. I. Kat, k. Meth, and D. Naor. Storage modeling for power estimation. In systor ’09: proceedings of systor 2009: the israeli experimental Systems conference, new york, ny, usa.Acm, (2009), 1-10.
[11] c. Patel, r. Sharma, c. Bash, and s. Graupner, Energy aware Grid: global workload placement based on energy efficiency. Technical report, hp laboratories,(2002).
[12] j. Torres, d. Carrera, k. Hogan, r. Gavalda, v. Beltran, and N. Poggi, Reducing wasted resources to help achieve green Data centers. In international symposium on parallel and Distributed processing (ipdps 2008)Ieee, (2008), 1-8.
[13] s. Srikantaiah, a. Kansal, and f. Zhao. Energy aware consolidation for cloud computing. In proceedings of hotpower ’08 Workshop on power aware computing and systems(2008).
[14] a.-c. Orgerie and l. Laurent. When clouds become green: The green open cloud architecture. In international conference On parallel computing (parco 2009), lyon, france(2009).
[15] Junaid Shuja, Kashif Bilal, Sajjad A. Madani, Mazliza Othman,Rajiv Ranjan, Pavan Balaji, and Samee U. Khan, Survey of Techniques and Architectures for Designing Energy-Efficient Data Centers, IEEE SYSTEMS JOURNAL, ( 2014), 1-13.
[16] c. Gunaratne, k. Christensen, and b. Nordman. Managing Energy consumption costs in desktop pcs and lan switches with Proxying, split tcp connections, and scaling of link speed. Int. J. Netw. Manag., 15(5), (2005), 297–310.
[17] d. C. Snowdon, s. Ruocco, and g. Heiser. Power management and dynamic voltage scaling: myths and facts. In Proceedings of the 2005 workshop on power aware real-time Computing, new jersey, usa(2005).
[18] h. Dietz and w. Dieter. Compiler and runtime support For predictive control of power and cooling. Parallel and Distributed processing symposium, international, (2006), 0-345.
[19] x. Fan, w.-d. Weber, and l. A. Barroso. Power provisioning For a warehouse-sized computer. In isca ’07: proceedings Of the 34th annual international symposium on computer Architecture, new york, ny, usa.Acm (2007), 13–23.
[20] f. Bellosa, s. Kellner, m. Waitz, and a. Weissel. Event-driven Energy accounting for dynamic thermal management. In Proceedings of the workshop on compilers and operating Systems for low power (colp’03), (2003), 1–10.
[21] a. Merkel and f. Bellosa. Balancing power consumption in Multiprocessor systems. In sigops operating systems review, 40(4), (2006), 403–414.
[22] j. S. Chase, d. C. Anderson, p. N. Thakar, a. M. Vahdat, and R. P. Doyle. Managing energy and server resources in hosting Centers. In sosp ’01: 18th acm symposium on operating Systems principles, new york, ny, usa,. Acm. , (2001), 103- 116.
[23] r. Jejurikar and r. Gupta. Energy aware task scheduling With task synchronization for embedded real-time systems. In Computer-aided design of integrated circuits and systems, Ieee transactions on. Ieee, (2006), 1024– 1037.
[24] g. Von laszewski, l. Wang, a. Younge, and x. He. Poweraware scheduling of virtual machines in dvfs-enabled clusters. In ieee international conference on cluster computing and Workshops (cluster ’09),( 2009), 1–10.
[25] Dejene Boru· Dzmitry Kliazovich· Fabrizio Granelli· Pascal Bouvry· Albert Y. Zomaya , “Energy-efficient data replication in cloud computing datacenters” , springer , (2015), 1-18.
[26] Jemal Abawajy , Data Replication Approach With Consistency Guarantee for Data Grid”, IEEE TRANSACTIONS ON COMPUTERS DECEMBER 2014, (2014), 1-17.
[27] ali elghirani, riky subrata, albert y. Zomaya, and ali al mazari., performance enhancement Through hybrid replication and genetic algorithm co-scheduling in data grids, advanced Networks research group, school of information technologies, university of sydney, nsw Australia(2006).
[28] sang-min park, jai-hoon kim, young-bae ko: dynamicgrid replication strategy based on Internet hierarchy, book series lecture notes in computer science, grid and cooperative Omputing book,publisher springer, august 2005, volume 3033/2004, (2005), 838-846.
[29] k. Ranganathan and i. Foster, identifying dynamic replication strategies for a high Performance data grid. In proceedings of the international grid computing workshop, Denver, colorado, usa(2001).
[30] i. Foster, k. Ranganathan, design and evaluation of dynamic replication strategies for high Performance data grids, in: proceedings of international conference on computing in high Energy and nuclear physics, beijing, china, (2001).
[31] p. K. Suri, manpreet singh, js2dr2 : an effective two-level job scheduling algorithm and Two-phase dynamic replication strategy for data grid, 2009 international conference on advances in computing, control, and telecommunication technologies,ieee, (2009), 232-237.
[32] k. Sashi, a.s. thanamani, a new dynamic replication algorithm for European Data grid, in: proceedings of the third annual acm bangalore conference, 17(2010).
[33] Leyli mohammad khanli , ayaz isazadeh , tahmuras n. Shishavan, phfs: a dynamic replication method, to decrease access latency in the multi-tier Data grid, future generation computer systems 27,(2011), 233–244.
[34] m. Tang, b.-s. Lee, c.-k. Yeo, and x. Tang, dynamic Replication algorithms for the multi-tier data grid, future Generation computer systems, vol. 21, (2005), 775-790.
[35] Mohammad Shorfuzzaman, Peter Graham, Rasit Eskicioglu Distributed Popularity Based Replica Placement in Data Grid Environments, International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2010
[36] T.A.Abdurrab , FIRE: A File Reunion Based Data Replication Strategy for Data Grids.(2010).
