Tasks scheduling in cloud computing is an optimization problem. Tasks balancing should in Virtual Machine's(VMs) is one of the important aspect in cloud environment. Whenever certain VMs are overloaded and remaining VMs are under loaded for processing the task, the load has to be balanced to achieve optimal machine utilization. In this paper, we use an algorithm named Bee colony optimization, which aims to achieve well balanced load across virtual machines for maximizing the throughput. One of the techniques for increasing flexibility and scalability of cloud data centers is Task Migration. The act of migration is performed for different goals like balancing and load sharing, reducing response time and improving the Quality of Service. We have compared the proposed algorithm with existing load balancing algorithms. The results after experiments show that the algorithm is effective when compared with existing algorithms. In our approach, there is a significant improvement in average execution time and and reduces the response time.

It is broadly accepted in the modern years that the purpose of information and communication technologies in the health care environment will improve care delivery to a great extent. The Health Care Organization is one of the largest service organizations in the world which mainly relies on Information Technology and Management Systems to provide better service and accuracy of information to their patients. Nevertheless the existing system are mainly focusing on information of patient health record and the electronic medical record within the organization. In order to develop public oriented health care system we propose a framework for PHC( primary health centres) which is based on cloud computing infrastructures with semantic web and machine learning algorithm by collecting the medical data from various PHC located in various parts of India. The cloud-based storage makes the information available for everyone who's taking care of the patients, allowing health care practitioners to view their patient history, diagnoses, treatment and also provide remote health consultation. Encryption techniques for service composition are used for the improvement of the proposed public health care platform.

A multi-tenant portal implementation extends the capabilities of an enterprise by enabling several customers to run independently on the same portal infrastructure hosted by a service provider. However, building multi-tenant solutions requires ad-dressing several technical challenges, but service providers/solution developers can build and deploy scalable, customizable, manageable, and cost-effective multi-tenant solutions. Addressing multi-tenancy is a key consideration in implementing Cloud enabled enterprise portal solutions which are becoming an international phenome-non, driven by both local demand as well as global reach by medium or large enter-prises that are in need of reducing cost of infrastructure/ hosting.

The paper enlighten the focus on the core of multi-tenant portal infrastructure along with key design elements that need to be considered in providing Cloud- ena-bled multi-tenant portal solutions. The paper would not focus on any specific vendor or their product suites but it acts as a synopsis for developer and architect community to strategize their thinking towards right portal architecture by gaining insights on multi-tenant portal features, key benefits and portal landscape.

Cloud computing has emerged as an effective solution in the computing world. The data capacity of the cloud has gone up to a Zettabyte from gigabyte. Cloud has been a computer and storage solution for many data centric organizations. The problem today those organizations are facing from the cloud is in data searching in an efficient manner. When the cloud is used for large amounts of data storage, searching for any required data takes lots of time. A framework is required to distribute the work of searching and fetching from thousands of computers. The data in Hadoop Distributed File System is scattered and needs lots of time to retrieve. MapReduce, which is a programming paradigm that expresses distributed computation as a sequence of distributed operations on the data set of key & value pairs. The proposed work aims to minimize the data retrieval time taken by the MapReduce program in the cloud. The major idea is to design a web server in the map phase using the jetty web server which will give a fast and efficient way of searching data in MapReduce paradigm. Web application are used to handle traffic throughput. Scalability and performance are key factors to the success of many enterprises involved in doing business on the performance of web application in the cloud. By web clustering technology we can improve the application performance. To keep the work down, the load balancer should automatically be able to distribute load to the newly added nodes in the server. The load balancer is used to balance the workload across servers to improve its availability, performance and scalability. To be able to exploit the elasticity of a cloud infrastructure, the applications usually need to be able to scale horizontally, i.e. it must be possible to add and remove nodes offering the same capabilities as the existing ones. In such scenarios, a load balancer is usually used.