Multi-threading based Map Reduce tasks scheduling

Author(s):  
Qutaibah Althebyan ◽  
Omar ALQudah ◽  
Yaser Jararweh ◽  
Qussai Yaseen
Keyword(s):  
Author(s):  
Qussai Yaseen ◽  
Omar AlQudah ◽  
Yaser Jararweh ◽  
Qutaibah Althebyan
Keyword(s):  

2015 ◽  
Vol 27 (18) ◽  
pp. 5686-5699 ◽  
Author(s):  
Qutaibah Althebyan ◽  
Yaser Jararweh ◽  
Qussai Yaseen ◽  
Omar AlQudah ◽  
Mahmoud Al-Ayyoub

Author(s):  
Qutaibah Althebyan ◽  
Omar AlQudah ◽  
Yaser Jararweh ◽  
Qussai Yaseen
Keyword(s):  

Author(s):  
Shalin Eliabeth S. ◽  
Sarju S.

Big data privacy preservation is one of the most disturbed issues in current industry. Sometimes the data privacy problems never identified when input data is published on cloud environment. Data privacy preservation in hadoop deals in hiding and publishing input dataset to the distributed environment. In this paper investigate the problem of big data anonymization for privacy preservation from the perspectives of scalability and time factor etc. At present, many cloud applications with big data anonymization faces the same kind of problems. For recovering this kind of problems, here introduced a data anonymization algorithm called Two Phase Top-Down Specialization (TPTDS) algorithm that is implemented in hadoop. For the data anonymization-45,222 records of adults information with 15 attribute values was taken as the input big data. With the help of multidimensional anonymization in map reduce framework, here implemented proposed Two-Phase Top-Down Specialization anonymization algorithm in hadoop and it will increases the efficiency on the big data processing system. By conducting experiment in both one dimensional and multidimensional map reduce framework with Two Phase Top-Down Specialization algorithm on hadoop, the better result shown in multidimensional anonymization on input adult dataset. Data sets is generalized in a top-down manner and the better result was shown in multidimensional map reduce framework by the better IGPL values generated by the algorithm. The anonymization was performed with specialization operation on taxonomy tree. The experiment shows that the solutions improves the IGPL values, anonymity parameter and decreases the execution time of big data privacy preservation by compared to the existing algorithm. This experimental result will leads to great application to the distributed environment.


2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Roberto Rodriguez-Zurrunero ◽  
Ramiro Utrilla ◽  
Elena Romero ◽  
Alvaro Araujo

Wireless Sensor Networks (WSNs) are a growing research area as a large of number portable devices are being developed. This fact makes operating systems (OS) useful to homogenize the development of these devices, to reduce design times, and to provide tools for developing complex applications. This work presents an operating system scheduler for resource-constraint wireless devices, which adapts the tasks scheduling in changing environments. The proposed adaptive scheduler allows dynamically delaying the execution of low priority tasks while maintaining real-time capabilities on high priority ones. Therefore, the scheduler is useful in nodes with rechargeable batteries, as it reduces its energy consumption when battery level is low, by delaying the least critical tasks. The adaptive scheduler has been implemented and tested in real nodes, and the results show that the nodes lifetime could be increased up to 70% in some scenarios at the expense of increasing latency of low priority tasks.


Sign in / Sign up

Export Citation Format

Share Document