SLGP Header

Neural Network Training for Efficient Resource Sharing in Cloud

IJCSEC Front Page

Abstract
In cloud computing, collaborative cloud computing (CCC) is the emerging technology where globally-dispersed cloud resource belonging to different organization are collectively used in a cooperative manner to provide services. The harmony enables a node to locate its desired resources where the load factor is not calculated. In the proposed system resource utilization is based on optimal time.. In proposed system to reform resource utilization based on optimal time period to allocate resources to the neural network training and to load factor calculation the dynamic priority scheduling technique is used to assign the priority to the cloud users according to their load. The dynamic priority scheduling algorithm strikes the right balance between performance and power efficiency.
Keywords: Reputation management; Resource management; Collaboration cloud computing
I.Introduction
Cloud computing is cyberspace-based enumerate in which large groups of secret servers are associate to allow sharing of data converting tasks, classify in formation store and online access to mainframe check property. Cloud computing is the creativity perception of computing as an good organization. Anywhere users can as side effect storage information into the cloud so as to mind the on-order high condition operation and usefulness form a shared splash of configurable computing property. Cloud environment offers the four types of cloud.
 Public cloud
 Private cloud
 Hybrid cloud
 Community cloud
 Software as a service (SaaS)
 Platform as a service (PaaS)
 Infrastructure as a service (IaaS)
Resources management and Reputation management must be jointing addressed in harmony to insure the victory implementation of sharing the cloud computing. The optimal time period for neural network training, load factor calculation and dynamic priority scheduling. The challenges of implementation the harmony system for real world application which involves cooperation between clouds provide.
o Drop box currently have five million users, three times the number last year [8].
o Planet lab is a group of mainframe possible as a search platform for brain circulates and shared systems analysis [10].
o Amazon Web Services (AWS) is a collection of isolated measure supplies in order that together make up a cloud cipher platform, show over the in order by www.Amazon.com [7].
Globally – scattered distributed cloud resources belonging to different organization are collectively polled and used in a cooperative manner to provide services. Thus developments in cloud computing is inevitably leading to a promising future for collaborative cloud computing. In any kind of system, dynamic priority scheduling and performance measure are the major data storage issues in cloud to be disturbed. Resource operation based on best time period to allocate resources propose a neural network training and dynamic priority scheduling for the nodes based on which the Virtual Machines (VMs) are scheduled.

References:

  1. Haiying Shen, Senior Member, IEEE, and Guoxin Liu, Student Member, IEEE,” An Efficient and Trustworthy Resource Sharing Platform for Collaborative Cloud Computing” IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 25, NO. 4, APRIL 2014.
  2. R. Zhou and K. Hwang, “Power Trust: A Robust and Scalable Reputation System for Trusted Peer-toPeer Computing,” IEEE Trans. Parallel and Distributed Systems, vol. 18, no. 4, pp. 460- 473, 2008.
  3. R. Zhou and K. Hwang, “Gossip-Based Reputation Management for Unstructured Peer-to-Peer Networks,” IEEE Trans. Knowledge and Data Eng., 2007.
  4. Y. Chawathe, S. Ratnasamy, L. Breslau, N. Lanham, and S. Shenker, “Making Gnutella-Like P2P Systems Scalable,” Proc. ACM SIGCOMM, 2003.
  5. S. Son and K.M. Sim, “A Price- and-Time-Slot-Negotiation Mechanism for Cloud Service Reservations,” IEEE Trans. Systems, Man, and Cybernetics, vol. 42, no. 3, pp. 713-728, June 2012.
  6. S. Chaisiri, B.S. Lee, and D. Niyato, “Optimization of Resource Provisioning Cost in Cloud Computing,” IEEE Trans. Services Computing, vol. 5, no. 2, pp. 164-177, Apr.-June 2012.
  7. Amazon Elastic Compute Cloud (EC2), http://aws.amazon.com,2013.
  8. Dropbox, www.dropbox.com, 2013.
  9. Zol, http://www.zol.com.cn/, 2013.
  10. Planetlab, http://www.planet-lab.org/, 2013.
  11. H. Shen and G. Liu, “Harmony: Integrated Resource and Reputation Management for Large-Scale Distributed Systems,” Proc. 20th Int’l Conf. Computer Comm. and Networks (ICCCN), 2011.
  12. S. Di and C. Wang, “Dynamic Optimization of Multiattribute Resource Allocation in Self-Organizing Clouds,” IEEE Trans.Parallel and Distributed Systems, vol. 24, no. 3, pp. 464-478,Mar. 2013.
  13. K. Chard and K. Bubendorfer, “High Performance Resource Allocation Strategies for Computational Economies,” IEEE Trans. Parallel and Distributed Systems, vol. 24, no. 1, pp. 72-84, Jan. 2013.
  14. K. Hwang, S. Kulkarni, and Y. Hu, “Cloud Security with Virtualized Defense and Reputation-Based Trust Management,” Proc. IEEE Int’l Conf. Dependable, Autonomic and Secure Computing (DASC), 2009.
  15. H. Shen, Y. Zhu, and W. Li, “Efficient and Locality-Aware Resource Management in Wide-Area Distributed Systems,” Proc. Int’l Conf. Networking, Architecture, and Storage, 2008.