TY - GEN
T1 - Swarm Intelligence (SI) based profiling and scheduling of big data applications
AU - Somasundaram, Thamarai Selvi
AU - Govindarajan, Kannan
AU - Kumar, Vivekanandan Suresh
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016
Y1 - 2016
N2 - Personalization targets a user's software, hardware, and QoS requirements at any given moment in the cloud environment for the big data applications. However, the individualization aims to target the daily needs of an individual user in a dynamic manner. The proposed research work aims to design a system which will be able to optimize user's applications towards a specified target goal. Furthermore, it is integrated with a Particle Swarm Optimization (PSO) based application profiling and resource selection mechanism which comes from the family of Swarm Intelligence (SI). The proposed algorithms create an application profile template and preferred resource list for each submitted big data applications and select the cloud resources from the preferred resource list which is based on the application preferences and availability of cloud resources in an optimal manner. From the experimental results, it is evident that the proposed research work maximizes the application success ratio, scheduling success rate, utilization of cloud resources, and user satisfaction.
AB - Personalization targets a user's software, hardware, and QoS requirements at any given moment in the cloud environment for the big data applications. However, the individualization aims to target the daily needs of an individual user in a dynamic manner. The proposed research work aims to design a system which will be able to optimize user's applications towards a specified target goal. Furthermore, it is integrated with a Particle Swarm Optimization (PSO) based application profiling and resource selection mechanism which comes from the family of Swarm Intelligence (SI). The proposed algorithms create an application profile template and preferred resource list for each submitted big data applications and select the cloud resources from the preferred resource list which is based on the application preferences and availability of cloud resources in an optimal manner. From the experimental results, it is evident that the proposed research work maximizes the application success ratio, scheduling success rate, utilization of cloud resources, and user satisfaction.
KW - Application Profiling
KW - Big Data
KW - Cloud Computing
KW - Resource Selection
KW - Swarm Intelligence
UR - http://www.scopus.com/inward/record.url?scp=85015153093&partnerID=8YFLogxK
U2 - 10.1109/BigData.2016.7840806
DO - 10.1109/BigData.2016.7840806
M3 - Published Conference contribution
AN - SCOPUS:85015153093
T3 - Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
SP - 1875
EP - 1880
BT - Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
A2 - Ak, Ronay
A2 - Karypis, George
A2 - Xia, Yinglong
A2 - Hu, Xiaohua Tony
A2 - Yu, Philip S.
A2 - Joshi, James
A2 - Ungar, Lyle
A2 - Liu, Ling
A2 - Sato, Aki-Hiro
A2 - Suzumura, Toyotaro
A2 - Rachuri, Sudarsan
A2 - Govindaraju, Rama
A2 - Xu, Weijia
T2 - 4th IEEE International Conference on Big Data, Big Data 2016
Y2 - 5 December 2016 through 8 December 2016
ER -