Enhancing the Cloud Computing Performance by Labeling the Free Node Services as Ready-To-Execute Tasks

The huge bandwidth and hardware capacity form a high combination together which leads to a vigorous development in the Internet. On the other hand, different problems will come up during the use of the networks such as delay and node tasks load. These problems lead to degrade the network performance and then affect network service for users. In cloud computing, users are looking to be provided with a high level of services from the service provider. In addition, cloud computing service facilitates the execution of complicated tasks that needed high-storage scale for the computation. In this paper, we have implemented a new technique to retain the service and assign tasks to the best and free available node already labeled by the manager node. The Cloud Computing Alarm (CCA) technique is working to provide all information about the services node and which one is ready to receive the task from users. According to the simulation results, the CCA technique is making good enhancements on the QoS which will increase the number of users to use the service. Additionally, the results showed that the CCA technique improved the services without any degrading of network performance by completing each task in less time.

[1]  Abdullah Gani,et al.  A Study on Strategic Provisioning of Cloud Computing Services , 2014, TheScientificWorldJournal.

[2]  Tuncay Ercan,et al.  Effective use of cloud computing in educational institutions , 2010 .

[3]  Ernesto Damiani,et al.  Network and Storage Latency Attacks to Online Trading Protocols in the Cloud , 2014, OTM Workshops.

[4]  Mladen A. Vouk,et al.  Clouds in Higher Education , 2015 .

[5]  Filip De Turck,et al.  Shared resource network-aware impact determination algorithms for service workflow deployment with partial cloud offloading , 2015, J. Netw. Comput. Appl..

[6]  Eduardo Gómez-Sánchez,et al.  Cloud computing and education: A state-of-the-art survey , 2015, Comput. Educ..

[7]  Hema Date,et al.  Understanding determinants of cloud computing adoption using an integrated TAM-TOE model , 2015, J. Enterp. Inf. Manag..

[8]  Chen-Nee Chuah,et al.  Analysis of link failures in an IP backbone , 2002, IMW '02.

[9]  Antony I. T. Rowstron,et al.  Pastry: Scalable, Decentralized Object Location, and Routing for Large-Scale Peer-to-Peer Systems , 2001, Middleware.

[10]  Sudip Misra,et al.  Security in Vehicular Ad Hoc Networks , 2016 .

[11]  U. Brandes A faster algorithm for betweenness centrality , 2001 .

[12]  Mladen A. Vouk,et al.  Cloud Computing – Issues, Research and Implementations , 2008, CIT 2008.

[13]  Athina Markopoulou,et al.  Characterization of failures in an IP backbone , 2004, IEEE INFOCOM 2004.

[14]  R. F. Freund,et al.  Scheduling resources in multi-user, heterogeneous, computing environments with SmartNet , 1998, Proceedings Seventh Heterogeneous Computing Workshop (HCW'98).

[15]  Guillaume Urvoy-Keller,et al.  Hierarchical Peer-To-Peer Systems , 2003, Parallel Process. Lett..

[16]  Jørn Braa,et al.  Models for Online Computing in Developing Countries: Issues and Deliberations , 2015, Inf. Technol. Dev..

[17]  Lizhe Wang,et al.  Scientific Cloud Computing: Early Definition and Experience , 2008, 2008 10th IEEE International Conference on High Performance Computing and Communications.

[18]  Sai Peck Lee,et al.  Cost-aware challenges for workflow scheduling approaches in cloud computing environments: Taxonomy and opportunities , 2015, Future Gener. Comput. Syst..

[19]  Ladislau Bölöni,et al.  A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems , 2001, J. Parallel Distributed Comput..

[20]  Miguel Rio,et al.  Handling Transient Link Failures Using Alternate Next Hop Counters , 2010, Networking.

[21]  Elizabeth Chang,et al.  Cloud service selection: State-of-the-art and future research directions , 2014, J. Netw. Comput. Appl..

[22]  Debra A. Hensgen,et al.  The relative performance of various mapping algorithms is independent of sizable variances in run-time predictions , 1998, Proceedings Seventh Heterogeneous Computing Workshop (HCW'98).

[23]  Rajkumar Buyya,et al.  InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services , 2010, ICA3PP.

[24]  Krishna P. Gummadi,et al.  Towards Trusted Cloud Computing , 2009, HotCloud.

[25]  Shu-Chin Wang,et al.  A hybrid load balancing policy underlying grid computing environment , 2007, Comput. Stand. Interfaces.