International Journal of Advanced Networking and Applications

2973-2978

Authors: E. Castillejos-Villatoro, G. Nangusé-Vázquez, A. Medina-Santiago, J. A. Velazquez-Martinez

This article presents the implementation of an identification system of geometric figures and their respective colors, this made with neural networks by using Backpropagation control implementation. This paper describes the process of extracting characteristic patterns of images, with the help of Artificial Neural Networks. The information Neuronal Network along with additional data of images and colors, will be stored in a database which will be put dynamic that will evolve with the figures they will be learning this by implementing a PID created in MATLAB Software. Subsequent to perform image capture with an independent PC WEBCAM. This processes the image and along with the data acquired and processed by the neural network in the pattern of shapes and colors. For image processing libraries MATLAB be used both in the implementation of a system acquisition by WEBCAM.

2986-2990

Authors: Yogita kaushik, Dr. C.K Jha

Cloud computing as a distributed paradigm, it has the latent to make over a large part of the Cooperative industry. In cloud computing it’s automatically describe more technologies like distributed computing, virtualization, software, web services and networking. We review the new cloud computing technologies, and indicate the main challenges for their development in future, among which load balancing problem stands out and attracts our attention Concept of load balancing in networking and in cloud environment both are widely different. Load balancing in networking its complete concern to avoid the problem of overloading and under loading in any sever networking cloud computing its complete different its involves different elements metrics such as security, reliability, throughput, tolerance, on demand services, cost etc. Through these elements we avoiding various node problem of distributing system where many services waiting for request and others are heavily loaded and through these its increase response time and degraded performance optimization. In this paper first we classify algorithms in static and dynamic. Then we analyzed the dynamic algorithms applied in dynamics environments in cloud. Through this paper we have been show compression of various dynamics algorithm in which we include honey bee algorithm, throttled algorithm, Biased random algorithm with different elements and describe how and which is best in cloud environment with different metrics mainly used elements are performance, resource utilization and minimum cost. Our main focus of paper is in the analyze various load balancing algorithms and their applicability in cloud environment.

2997-3002

Authors: Mohamed Ahmed Radwan, Ahmed Bahaa Farid, Associate Professor, Dr. Mona Mohamed Nasr

Many software organizations have moved from traditional methods for software development, such as waterfall method to usage of agile methods. Agile methods are used especially in software development and are constantly refurbishing and improving initial plans along the way. In software development the systems usually require frequent changes during the development process. This method is very suitable for small project and organizations, but it is very hard to implement it in large organizations with large projects and teams. This paper aims to identify weaknesses of two existing scrum frameworks used for large organizations and to present proposed hybrid framework scaled from both existing frameworks. It’s highlighting the importance of predefined life cycle of teams, which is key factor in achieving better timeline and to avoid mistakes that affects the time of release deployment.

3009-3013

Authors: Mamatha E, Saritha S, CS Reddy

Rule based heuristic scheduling algorithms in real time and cloud computing Systems employ for resource or task scheduling since they are suitable to implement for NP-complete problems. However, they are simple but there is much room to improve these algorithms. This study presents a heuristic scheduling algorithm, called High performance hyper-heuristic scheduling algorithm (HHSA) using detection operator, to find better scheduling solutions for real and cloud computing systems. The two operators - diversity detection and improvement detection operators - are employed in this algorithm to determine the timing to determine the heuristic algorithm.. These two are employed to dynamically determine a low level heuristic that can be used to find better solution. To evaluate the performance of this method, authors examined the above method with several scheduling algorithms and results prove that Hyper Heuristic Scheduling Algorithm can significantly decrease the makespan of task scheduling when compared with all other scheduling algorithms. A novel high-performance hyper-heuristic algorithm is proposed for scheduling on cloud computing systems to reduce the make span. This algorithm can be applied to both sequence dependent and sequence independent scheduling problems.