scholarly journals Cost minimisation technique in geo-distributed data centres

2019 ◽  
Vol 11 (6) ◽  
pp. 669
Author(s):  
Ayesheh Ahrari Khalaf ◽  
Aisha Hassan Abdalla Hashim
Keyword(s):  
Distributed Data ◽  
Cost Minimisation ◽  
Data Centres

scholarly journals The cloud of geographically distributed data centers

2019 ◽  
Vol 214 ◽  
pp. 07007
Author(s):  
Petr Fedchenkov ◽  
Andrey Shevel ◽  
Sergey Khoruzhnikov ◽  
Oleg Sadov ◽  
Oleg Lazo ◽  
...  
Keyword(s):  
Data Storage ◽  
Data Centers ◽  
Virtual Machines ◽  
Data Transfer ◽  
Service Availability ◽  
Distributed Data ◽  
Virtual Storage ◽  
Distribution Services ◽  
Geographically Distributed ◽  
Data Centres

ITMO University (ifmo.ru) is developing the cloud of geographically distributed data centres. The geographically distributed means data centres (DC) located in different places far from each other by hundreds or thousands of kilometres. Usage of the geographically distributed data centres promises a number of advantages for end users such as opportunity to add additional DC and service availability through redundancy and geographical distribution. Services like data transfer, computing, and data storage are provided to users in the form of virtual objects including virtual machines, virtual storage, virtual data transfer link.

scholarly journals Serving data from the SCAR Southern Ocean Observing System (SOOS) using the SeaDataNet infrastructure

2010 ◽  
Vol 28 ◽  
pp. 5-9
Author(s):  
T. F. de Bruin
Keyword(s):  
Southern Ocean ◽  
Systems Approach ◽  
Scientific Committee ◽  
Direct Access ◽  
Data Delivery ◽  
Distributed Data ◽  
Antarctic Research ◽  
Oceanographic Data ◽  
Data Centres ◽  
Ocean Observing

Abstract. The Scientific Committee on Antarctic Research (SCAR) and the Scientific Committee on Oceanic Research (SCOR) jointly intend to build a Southern Ocean Observing System (SOOS). This paper addresses the required data flow infrastructure. SOOS will use a system of systems approach, using existing observation programmes and projects. Data should be submitted to professional data centres. The problem arises how to link all these data centres and get a central overview of the SOOS data as well as direct access to the data. The Netherlands National Oceanographic Data Committee (NL-NODC) has successfully built a national distributed oceanographic data acccess infrastructure, adopting and implementing technology developed by the European SeaDataNet project. The Dutch system has been operational since early 2009. The conclusion is that the SeaDataNet technology can be used to build an operational, distributed data delivery infrastructure, featuring all elements required by the Southern Ocean Observing System (SOOS).

scholarly journals Detection and Classification of Malicious Flows in Software-Defined Networks Using Data Mining Techniques

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2972
Author(s):  
Marek Amanowicz ◽  
Damian Jankowski
Keyword(s):  
Data Mining ◽  
Network Control ◽  
Flow Rule ◽  
Distributed Data ◽  
Software Defined Networks ◽  
Data Mining Techniques ◽  
Wireless Applications ◽  
Using Data ◽  
Data Centres

The increasing availability of mobile devices and applications, the progress in virtualisation technologies, and advances in the development of cloud-based distributed data centres have significantly stimulated the growing interest in the use of software-defined networks (SDNs) for both wired and wireless applications. Standards-based software abstraction between the network control plane and the underlying data forwarding plane, including both physical and virtual devices, provides an opportunity to significantly increase network security. In this paper, to secure SDNs against intruders’ actions, we propose a comprehensive system that exploits the advantages of SDNs’ native features and implements data mining to detect and classify malicious flows in the SDN data plane. The architecture of the system and its mechanisms are described, with an emphasis on flow rule generation and flow classification. The concept was verified in the SDN testbed environment that reflects typical SDN flows. The experiments confirmed that the system can be successfully implemented in SDNs to mitigate threats caused by different malicious activities of intruders. The results show that our combination of data mining techniques provides better detection and classification of malicious flows than other solutions.

scholarly journals A survey on Approaches Used for Efficient Workload Management in Geo-Distributed Data Centres

2018 ◽  
Vol 7 (3.34) ◽  
pp. 141
Author(s):  
D Ramya ◽  
J Deepa ◽  
P N.Karthikayan
Keyword(s):  
Operating Systems ◽  
Data Center ◽  
Data Centers ◽  
Distributed Data ◽  
Cloud Infrastructure ◽  
Workload Management ◽  
Software Applications ◽  
Geographically Distributed ◽  
Cloud Infrastructures ◽  
Data Centres

A geographically distributed Data center assures Globalization of data and also security for the organizations. The principles for Disaster recovery is also taken into consideration. The above aspects drive business opportunities to companies that own many sites and Cloud Infrastructures with multiple owners.  The data centers store very critical and confidential documents that multiple organizations share in the cloud infrastructure. Previously different servers with different Operating systems and software applications were used. As it was difficult to maintain, Servers are consolidated which allows sharing of resources at low of cost maintenance [7]. The availability of documents should be increased and down time should be reduced. Thus workload management becomes a challenging among the data centers distributed geographically. In this paper we focus on different approaches used for workload management in Geo-distributed data centers. The algorithms used and also the challenges involved in different approaches are discussed 

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