The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Traffic Analyses and Measurements: Technological Dependability
|
|
Author(s): Rossitza Goleva (Technical University of Sofia, Bulgaria), Dimitar Atamian (Technical University of Sofia, Bulgaria), Seferin Mirtchev (Technical University of Sofia, Bulgaria), Desislava Dimitrova (ETH Zurich, Switzerland), Lubina Grigorova (Vivacom JSCO, Bulgaria), Rosen Rangelov (Lufthansa Technik-Sofia, Bulgaria)and Aneliya Ivanova (Technical University of Sofia, Bulgaria)
Copyright: 2015
Pages: 52
Source title:
Resource Management of Mobile Cloud Computing Networks and Environments
Source Author(s)/Editor(s): George Mastorakis (Technological Educational Institute of Crete, Greece), Constandinos X. Mavromoustakis (University of Nicosia, Cyprus)and Evangelos Pallis (Technological Educational Institute of Crete, Greece)
DOI: 10.4018/978-1-4666-8225-2.ch006
Purchase
|
Abstract
Resource management schemes in current data centers, including cloud environments, are not well equipped to handle the dynamic variation in traffic caused by the large diversity of traffic sources, source mobility patterns, and underlying network characteristics. Part of the problem is lacking knowledge on the traffic source behaviour and its proper representation for development and operation. Inaccurate, static traffic models lead to incorrect estimation of traffic characteristics, making resource allocation, migration, and release schemes inefficient, and limit scalability. The end result is unsatisfied customers (due to service degradation) and operators (due to costly inefficient infrastructure use). The authors argue that developing appropriate methods and tools for traffic predictability requires carefully conducted and analysed traffic experiments. This chapter presents their measurements and statistical analyses on various traffic sources for two network settings, namely local Area Network (LAN) and 3G mobile network. LAN traffic is organised in DiffServ categories supported by MPLS to ensure Quality of Service (QoS) provisioning. 3G measurements are taken from a live network upon entering the IP domain. Passive monitoring was used to collect the measurements in order to be non-obtrusive for the networks. The analyses indicate that the gamma distribution has general applicability to represent various traffic sources by proper setting of the parameters. The findings allow the construction of traffic models and simulation tools to be used in the development and evaluation of flexible resource management schemes that meet the real-time needs of the users.
Related Content
|
Dina Darwish.
© 2024.
43 pages.
|
|
Kassim Kalinaki, Musau Abdullatif, Sempala Abdul-Karim Nasser, Ronald Nsubuga, Julius Kugonza.
© 2024.
23 pages.
|
|
Yogita Yashveer Raghav, Ramesh Kait.
© 2024.
17 pages.
|
|
Renuka Devi Saravanan, Shyamala Loganathan, Saraswathi Shunmuganathan.
© 2024.
21 pages.
|
|
Veera Talukdar, Ardhariksa Zukhruf Kurniullah, Palak Keshwani, Huma Khan, Sabyasachi Pramanik, Ankur Gupta, Digvijay Pandey.
© 2024.
30 pages.
|
|
Dharmesh Dhabliya, Sukhvinder Singh Dari, Nitin N. Sakhare, Anish Kumar Dhablia, Digvijay Pandey, Balakumar Muniandi, A. Shaji George, A. Shahul Hameed, Pankaj Dadheech.
© 2024.
9 pages.
|
|
Avtar Singh, Shobhana Kashyap.
© 2024.
11 pages.
|
|
|