SWATHY R
@mitindia.edu
Teaching Fellow, Computer Centre
Madras Institute of Technology, Anna Unviersity
RESEARCH INTERESTS
Cloud Computing, Big Data
Scopus Publications
Scopus Publications
P. Chinnasamy, S. Padmavathi, R. Swathy, and S. Rakesh
Springer Singapore
Services are distributed among all servers and between the users and individuals in the cloud environment. Cloud providers have trouble guaranteeing file protection as security is the biggest issue in data handling and transfer as it can be accessed, misused and destroyed the original data form. Cloud security is a big concern in the cloud computing environment. To safeguard the cloud environment, many research works are being proposed. To overcome the security issue and achieve the CIA property (confidentiality, integrity and availability) the cryptography is used. Cryptography is the most useful technique to ensure a high level of data transfer and storage security. In traditional symmetric and asymmetric has some limitations. To solve this we are going to introducing a new hybrid technique to achieve high data security and confidentiality. In this article, we are combing ECC and Blowfish to implement a hybrid algorithm. The performance of the hybrid system is compared with the existing hybrid method and shows that the proposed method provides high security and confidentiality of patient data. The hybrid cryptography is used to defeat the inconveniences of both symmetric and asymmetric.
R. Swathy, B. Vinayagasundaram, G. Rajesh, Anand Nayyar, Mohamed Abouhawwash, and Mohamed Abu Elsoud
Public Library of Science (PLoS)
On-demand cloud computing is one of the rapidly evolving technologies that is being widely used in the industries now. With the increase in IoT devices and real-time business analytics requirements, enterprises that ought to scale up and scale down their services have started coming towards on-demand cloud computing service providers. In a cloud data center, a high volume of continuous incoming task requests to physical hosts makes an imbalance in the cloud data center load. Most existing works balance the load by optimizing the algorithm in selecting the optimal host and achieves instantaneous load balancing but with execution inefficiency for tasks when carried out in the long run. Considering the long-term perspective of load balancing, the research paper proposes Stackelberg (leader-follower) game-theoretical model reinforced with the satisfaction factor for selecting the optimal physical host for deploying the tasks arriving at the data center in a balanced way. Stackelberg Game Theoretical Model for Load Balancing (SGMLB) algorithm deploys the tasks on the host in the data center by considering the utilization factor of every individual host, which helps in achieving high resource utilization on an average of 60%. Experimental results show that the Stackelberg equilibrium incorporated with a satisfaction index has been very useful in balancing the loading across the cluster by choosing the optimal hosts. The results show better execution efficiency in terms of the reduced number of task failures by 47%, decreased ‘makespan’ value by 17%, increased throughput by 6%, and a decreased front-end error rate as compared to the traditional random allocation algorithms and flow-shop scheduling algorithm.
B. Vinayagasundaram and R Swathy
IEEE
Cloud Computing, one of the latest standard of large scale and parallel computing, attracts more users requiring utility computing with better and fast service. Load balancing is one of the key parameters that determine a cloud data center's performance. One of the main problems with respect to load balancing is that all the physical hosts in a data center are not efficiently used resulting in an imbalance and suboptimal performance. This paper has focused on how the physical hosts for deploying requested tasks is selected based on the requirement of the request by using Stackelberg's approach. Most of the works that has been done previously in this area utilize a series of algorithms that selects the optimal host in the data center based on the intelligence that is confined to the algorithm alone and doesn't have a holistic approach that considers the unutilized resources amongst all hosts within a data center. The proposed model in this work makes a decision of which physical host must be allocated to a requestbased on the requirements of the incoming task, current load on the data center hosts, available or unused resources in the data center. The model uses First-in-First-out allocation strategy for task assignments. Simulation results compared with the existing works show that the proposed approach has decreased the failure number of task deployment events obviously, and reduced the makespan.