Bikrampal Kaur

Hybrid filter with wavelet denoising and anisotropic diffusion filter for image despeckling

SAR imaging is an efficient tool for retrieval of useful information about the surfaces of earth and various other planets as well. The synthetic aperture radar (SAR) generates severely degraded images due to a form of multiplicative noise called speckle. It is produced by the random interference of the electromagnetic waves which are backscattered because of the roughness of imaged surface. Numerous filters were developed which helped in enhancing the image quality to some extent. However, recent filtering techniques which were based on non local image modelling technique, proved to build way better filters which resulted in despeckling the images without tampering with its edges or oversmoothing the images. This paper introduces one such filtering technique which is based on the wavelet transformation followed by anisotropic filtering. The results are found and compared for Peak Signal to Noise Ratio, Coefficient of Correlation and Equivalent No of looks.

An Effective Technique to Decline Energy Expenditure in Cloud Platforms

The cloud computing is the rapidly growing technology in the IT world. A vital aim of the cloud is to provide the services or resources where they are needed. From the user’s prospective convenient computing resources are limitless thatswhy the client does not worry that how many numbers of servers positioned at one site so it is the liability of the cloud service holder to have large number of resources. In cloud data-centers, huge bulk of power exhausted by different computing devices.Energy conservancy is a major concern in the cloud computing systems. From the last several years, the different number of techniques was implemented to minimize that problem but the expected results are not achieved. Now, in the proposed research work, a technique called Enhanced ACO that is developed to achieve better offloading decisions among virtual machines when the reliability and proper utilization of resources will also be considered and will use ACO algorithm to balance load and energy consumption in cloud environment. The proposed technique also minimizes energy consumption and cost of computing resources that are used by different processes for execution in cloud. The earliest finish time and fault tolerance is evaluated to achieve the objectives of proposed work. The experimental outcomes show the better achievement of prospective model with comparison of existing one. Meanwhile, energy-awake scheduling approach with Ant colony optimization method is an assuring method to accomplish that objective.

Comparative Analysis of Metaheuristics Based Load Balancing Optimization in Cloud Environment

Cloud Computing Technology provides computing resources as a utility service. The objective is to achieve maximum resource utilization with minimum service delivery time and cost. The main challenge is to balance the virtual machines (VM) load in cloud environment and it requires distributing the load between many virtual machines while avoiding underflow and overflow conditions, which depend on capacity of VMs. In this paper, load balancing of VMs have been done based on Ant Colony Optimization (ACO) and Bat algorithm for underflow and overflow VM identifications respectively. As cloud applications involve huge computations and are highly dynamic in nature, so Directed Acyclic Graph (DAG) files of various scientific workflows have been used as input data during implementation of the proposed methodology. Workflows used for experiments are Cybershake, Genome, Ligo, Montage, Sipht and VMs vary from 2 to 20 on a single host configuration. Initially, the workflows are parsed through Predict earliest Finish time (PEFT) heuristic which initializes the metaheuristics rather than using random initialization. Thus, metaheuristics are providing optimal initial parameters which further optimize the VM utilization by balancing their load. The performance of metaheuristics on the basis of makespan and cost metrics has been evaluate, analyzed and compared with the Particle Swarm Optimization (PSO) approach used for load balancing.

Load balancing in tasks using honey bee behavior algorithm in cloud computing

Proper scheduling of tasks on cloud is a crucial optimization concern. Load balancing between deterrent dependent tasks on virtual machines (VMs) in cloud datacenters is a significant feature of task arrangement procedure in the cloud environment. In this paper, the new task scheduling model has been proposed, which utilizes the honey bee inspired algorithm for the load balancing which maximize the throughput of virtual machines in the cloud and optimize the execution time of assigned dependent tasks for the proper utilization of resources in the least possible cost. The proposed model balances the load between the jobs on VMs in a way that the overall waiting time of tasks in the queue is minimized. The proposed model is designed to calculate the CPU time in terms of earliest finish time (EFT). The load is calculated on the basis of resource usage percentage whereas the communication cost is evaluated by using process memory allocation, memory requirement, and data size, which is further used for final decision making by comparing the communication cost with the process cost. The experimental results have proven the effectiveness of the proposed model in comparison with the existing models.

Cost Aware Load Balanced Task Scheduling with Active VM Load Evaluation

The cloud platforms are gaining more popularity every year and adding up more customers to their portals. The cloud platforms are being flooded with the millions of user queries every second, which are becoming a major challenge to process them in the shortest possible time. The existing solutions do not evaluate the individual load on the virtual machines, while scheduling the tasks on the cloud platforms. In this paper, the new task scheduling model has been proposed, which utilizes the ant colony optimization for the load based VM allocation for each task loaded in the list for processing. The proposed model has been designed to calculate the load on the list of available VMs. The available list of the VMs is evaluated against the process cost, which checks the ability of VM in focus to process the given task. The VMs, who are eligible to process the given task, are shortlisted and the given task is assigned to the VM with the least load. The experimental results have manifested the effectiveness of the proposed model in comparison with the existing models to take the accurate offloading decisions.

Design of Hybrid Filter with Wavelet denoising and Anisotropic Diffusion Filter for Image Despeckling

SAR imaging is an efficient tool for retrieval of useful information about the surfaces of earth and various other planets as well. The synthetic aperture radar (SAR) generates severely degraded images due to a form of multiplicative noise called speckle. It is produced by the random interference of the electromagnetic waves which are backscattered because of the roughness of imaged surface. Numerous filters were developed which helped in enhancing the image quality to some extent. However, recent filtering techniques which were based on non local image modelling technique, proved to build way better filters which resulted in despeckling the images without tampering with its edges or oversmoothing the images. This paper introduces one such filtering technique which is based on the wavelet transformation followed by anisotropic filtering. The results are found and compared for Peak Signal to Noise Ratio, Coefficient of Correlation and Equivalent No of looks.