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Cloud computing is the most recent technology in today's world of computing and it overcomes deficiencies of traditional ways of computing. Cloud computing is a new way of providing the essential services to cloud users on "Pay As You Go" basis. Cloud computing provides different features like on demand access, flexibility, instant response, pay per use etc. to customers. In order to provide all these features to cloud users, cloud computing systems must be structured and managed efficiently to provide the Quality of Services (QOS) to users. Various technological concepts such as abstraction and virtualization are used that hides the implementation details from an average cloud user. Cloud load balancing plays a very important role in providing all the cloud features to users which is the main topic of interest in our research. Different archictures apply altogether different load balancing algorithms. This paper includes the Study of different approaches of effective management of cloud systems. The study includes load balancing approaches in different system architectures like Centralized, Distributed and Cluster based architecture. Finally various algorithms have been compared based on the different parameters like response time, efficiency and throughput etc.

Edge detection is one of the most important techniques used for image segmentation. Image segmentation remains a puzzled problem even after four decades of research. In this paper, a soft computing approach based on fuzzy logic is applied on histogram of an image to enhance edge detection technique. We used BSD images for experimentation and their respective ground truths for qualitative evaluation of proposed approach.