Publication CSE

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At CGU,Odisha the faculty are encouraged to publish their findings in reputed journals and present papers at conferences. Support is given to participate in national and international conferences and thereby network with experts from India and abroad. During the last few years, CGU has seen a healthy growth in the amount of funding received from different   government and private sectors.

The Department of Computer Science & IT as one of the oldest department of CGU, Odisha also motivates its students and staff to involve in R&D activities of the department for their overall development. This department to its pride has a good number of publications and research projects

A neuron-based active queue management scheme for internet congestion control.To deal with nonlinear and complex problems of internet congestion control, an intelligent scheme is required, which can learn the traffic pattern of the network. In this paper, we design a robust AQM scheme called neuron-based AQM (N-AQM) to efficiently control the complex network congestion problem and achieve QoS. In N-AQM, a neural network is used to predict the future value of current queue length and estimate the differential queue length error and use it to define the packet drop probability. Our simulation result demonstrates that N-AQM is stable, robust and outperforms other AQM schemes. From the result section, it is observed that N-AQM is more efficient in stabilising the queue length around the target with faster settling time and incurs lower oscillation than others.
A novel load balancing technique for cloud computing platform based on PSO.In cloud computing environment tasks are allocated among virtual machines (VMs) having different length, starting time and execution time. Therefore, balancing these loads among VM is a key factor. Load balancing has to be carried out in such a manner that all VMs should have balanced to achieve optimal utilization of its capabilities and improve the system performance. In this paper, we proposed a load balancing technique by using modified PSO task scheduling (LBMPSO) to schedule tasks over the available cloud resources that minimizes the makespan and maximizes resource utilization. This is achieved by having proper information among the tasks and resources within the datacenter. Our proposed scheduling algorithm is implemented by using CloudSim simulator. Simulation results clearly shows that proposed scheduling algorithm performs better in reducing makespan and increases the resource utilization than other existing techniques.
A Self-Tuning Congestion Tracking Control for TCP/AQM Network for Single and Multiple Bottleneck Topology.In this work a self-tuning rate and queue based proportional and integral controller called SRQ-PI is proposed to efficiently control the queue length with small overshoot and faster settling time. SRQ-PI proposes a new control tracking function that maps level of congestion to the packet drop probability dynamically. In SRQ-PI, the incoming traffic rate is estimated and used with the proportional and integral controller. The SRQ-PI tunes itself and stabilizes the system with internal feedback without requiring any external feedback. Furthermore, the stability of the SRQ-PI is analyzed using control theory and presents systematic guidelines to select the control gain parameters. NS2 is used to carry out the simulation work. The simulation result demonstrates that SRQ-PI is stable and gets faster transient response due to lower average delay jitter and robust against dynamic network parameters. The SRQ-PI outperforms proportional integral (PI), Intelligent adaptive PI (IAPI) and Random exponential marking (REM) algorithm.
A survey on PSO based meta-heuristic scheduling mechanism in cloud computing environment.With the increasing of the scale of task or request and dynamic nature of cloud resources, it gives significant issues of load balancing, resource utilization, task allocation, and system performance and so on. To solve those problems many researchers have applied different types of scheduling techniques. But meta-heuristic scheduling is the most accomplish preferred outcomes over conventional heuristics and hybrid scheduling. Among various meta-heuristics algorithms, PSO is a famous metaheuristic technique to solved optimization issue. PSO is appropriate for dynamic task scheduling, workflow scheduling and load balancing. PSO has a strong worldwide searching capability toward the start of the run and a nearby pursuit close to the furthest limit of the run. Therefore, it has been generally utilized in different applications and has made incredible progress. In this paper a systematically reviews is done on different types of particle swarm optimization (PSO) based scheduling strategy with set of challenges and future direction.