Efficient virtual data center request embedding based on row-epitaxial and batched greedy algorithms

Efficient virtual data center request embedding based on row-epitaxial and batched greedy algorithms

Data centers are becoming the main backbone of and centralized repository for all cloud-accessible servicesin on-demand cloud computing environments. In particular, virtual data centers (VDCs) facilitate the virtualization of all data center resources such as computing, memory, storage, and networking equipment as a single unit. It is necessary to use the data center efficiently to improve its profitability. The essential factor that significantly influences efficiencyis the average number of VDC requests serviced by the infrastructure provider, and the optimal allocation of requests improves the acceptance rate. In existing VDC request embedding algorithms, data center performance factors such as resource utilization rate and energy consumption are not taken into consideration. This motivated us to design a strategy for improving the resource utilization rate without increasing the energy consumption. We propose novel VDC embedding methods based on row-epitaxial and batched greedy algorithms inspired by bioinformatics. These algorithms embed new requests into the VDC while reembedding previously allocated requests. Reembedding is done to consolidate the available resources in the VDC resource pool. The experimental testbed results show that our algorithms boost the data center objectives of high resource utilization (by improving the request acceptance rate), low energy consumption, and short VDC request scheduling delay, leading to an appreciable return on investment.

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  • [1] Yang Y, Chang X, Liu J, Li L. Towards robust green virtual cloud data center provisioning. IEEE T Cloud Comput 2017; 5: 168-181.
  • [2] Wen X, Han Y, Yuan H, Zhou X, Xu Z. An efficient resource embedding algorithm in software defined virtualized data center. In: IEEE Global Communications Conference (GLOBECOM); 6–10 December 2015; San Diego, CA, USA.
  • [3] Zhani MF, Zhang Q, Simona G, Boutaba R. VDC Planner: dynamic migration-aware Virtual Data Center embedding for clouds. In: IFIP/IEEE International Symposium on Integrated Network Management (IM 2013); 27–31 May 2013; Ghent, Belgium. pp. 18-25.
  • [4] Zhang Q, Zhani MF, Jabri M, Boutaba R. Venice: Reliable virtual data center embedding in clouds. In: IEEE INFOCOM 2014 - IEEE Conference on Computer Communications; 27 April–2 May 2014; Toronto, Canada. pp.289- 297.
  • [5] Zuo C, Yu H, Anand V. Reliability-aware virtual data center embedding. In: 6th International Workshop on Reliable Networks Design and Modeling (RNDM); 17–19 November 2014; Barcelona, Spain. pp. 151-157.
  • [6] Yang Y, Chang X, Liu J, Li L. Towards robust green virtual cloud data center provisioning. IEEE T Cloud Comput 2017; 5: 168-181.
  • [7] Shi L, Katramatos D, Yu D. Virtual data center allocation with dynamic clustering in clouds. In: IEEE 33rd International Performance Computing and Communications Conference (IPCCC); 5–7 December 2014; Austin, TX, USA. pp. 1-10.
  • [8] Amokrane A, Zhani MF, Langar R, Boutaba R, Pujolle G. Greenhead: virtual data center embedding across distributed infrastructures. IEEE T Cloud Comput 2013; 1: 36-49.
  • [9] Dayarathna M, Wen Y, Fan R. Data center energy consumption modeling: a survey. IEEE Commun Surv Tutorials 2016; 18: 732-794.
  • [10] Kahng AB, Măndoiu II, Pevzner PA, Reda S, Zelikovsky AZ. Scalable heuristics for design of DNA probe arrays. J Comput Biol 2004; 11: 429-447.
  • [11] Kahng AB, Mandoiu II, Zelikovsky AZ. Highly scalable algorithms for rectilinear and octilinear Steiner trees. In: Proceedings of the ASP-DAC Asia and South Pacific Design Automation Conference; 21–24 January 2003; Kitakyushu, Japan. pp. 827-833
  • [12] Sun X, Su S, Xu P, Jiang L. Optimizing multi-dimensional resource utilization in virtual data center. In: 4th IEEE International Conference on Broadband Network and Multimedia Technology; 28–30 October 2011; Shenzhen, China. pp. 395-400.
  • [13] Li M, Bi J, Li Z. Virtual-switching-aware VM consolidation in virtualized data centers. In: IEEE 6th International Conference on Cloud Computing Technology and Science; 15–18 December 2014; Singapore. pp. 817-822.
  • [14] Fuerst C, Schmid S, Feldmann A. Virtual network embedding with collocation: benefits and limitations of preclustering. In: IEEE 2nd International Conference on Cloud Networking (CloudNet); 11–13 November 2013; San Francisco, CA, USA. pp. 91-98.
  • [15] Guo C, Lu G, Wang H, Yang S, Kong C, Sun P, Wu W, Zhang Y. Secondnet: A data center network virtualization architecture with bandwidth guarantees. In: Proceedings of the 6th International Conference ACM CoNEXT; 30 November–3 December 2010; Philadelphia, PA, USA.
  • [16] Roohitavaf M, Entezari-Maleki R, Movaghar A. Availability modeling and evaluation of cloud virtual data centers. In: International Conference on Parallel and Distributed Systems; 15–18 December 2013; Seoul, South Korea. pp. 675-680.
  • [17] Xu L, Li C, Li L, Liu Y, Yang Z, Liu Y. A virtual data center deployment model based on the green cloud computing. In: IEEE/ACIS 13th International Conference on Computer and Information Science (ICIS); 4–6 June 2014; Taiyuan, China. pp. 235-240.
  • [18] Kliazovich D, Bouvry P, Khan SU. GreenCloud: A packet-level simulator of energy-aware cloud computing data centers. J Supercomput 2012; 62: 1263-1283.
  • [19] Zou J, Yan F, Lee TT, Hu W. An add/drop algorithm for virtual data center embedding. In: Asia Communications and Photonics Conference; 11–14 November 2014; Shanghai, China.
  • [20] Kansal NJ, Chana I. Artificial bee colony based energy-aware resource utilization technique for cloud computing. Concurr Comput Pract Exper 2015; 27: 1207-1225.
  • [21] Correa ES, Fletscher LA, Botero JF. Virtual data center embedding: a survey. IEEE Lat Am Trans 2015; 13: 1661-1670.
  • [22] Sivaranjani B, Vijayakumar P. A technical survey on various VDC request embedding techniques in virtual data center. In: National Conference on Parallel Computing Technologies (PARCOMPTECH); 19–20 February 2015; Bangalore, India.
  • [23] Sun G, Liao D, Bu S, Yu H, Sun Z, Chang V. The efficient framework and algorithm for provisioning evolving VDC in federated data centers. Future Gener Comp Sy 2017; 73: 79-89.
  • [24] Leiserson CE. Fat-trees: Universal networks for hardware-efficient supercomputing. IEEE T Comput 1985; C34: 892-901.