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Cloud is referred to as a collection of infrastructure services, such as Infrastructure as a service (IaaS) and Platform as a service (PaaS), which are made available to us for utilization by various organizations in which the key factor is virtualization of data as it allow the user to manage, handle and compute a large number of tasks very easily. By referring to Cloud technologies we mean runtime such as Hadoop, Dryad and other Map Reduce frameworks. In this paper we would analyse the above mentioned software’s and techniques for the cloud system by comparing them on the basis of its processing speed, its data handling capacity, the nature of user friendliness. We would discuss large scale data analysis using different implementations on the above mentioned tools and after that we would give a performance analysis of these tools on the given implementation like Cap3, HEP, Cloudburst.

Cloud computing has emerged as a new paradigm for on-demand access to a wast pool of computing resources that provides an alternative to using on-premises resources. This paper discusses the challenges related to using the cloud computing infrastructures for scientific computing. An approach based on Everest platform addressing these challenges is presented along with the prototype integration of Everest with Google Compute Engine. The proposed integration enables Everest users to seamlessly provision and use cloud-based computing resources for running different types of workloads including HPC and HTC applications. In contrast to other efforts, the presented approach also supports building and sharing domain-specific web services that automate execution of applications on dynamically provisioned cloud resources or hybrid infrastructures.

Cloud computing is becoming increasingly fashionable in distributed computing environment. Processing and Data storage use cloud environment is becoming a movement universal. Software as a Service (SaaS) has on many business applications as well as in our day to day life, we can simply say that this disruptive technology. Cloud computing can be seen since Internet-based computing, in which shared resources, software, and information are made available to devices on demand. It allows resources towards leveraged on per-use basis. It diminishes cost and complexity of service providers by means of assets and operational costs. It allows users to access applications tenuously. On behalf of user, this construct directs cloud service provider to feel software updates and cost of servers etc. For both, cloud providers and consumers; availability, integrity, authenticity, confidentiality, and privacy are important concern. Infrastructure as a Service (IaaS) serves as base layer for many other release models and Platform-as-a-Service (PaaS) clouds. Security of PaaS clouds is considered from multiple perspective including access control, service continuity and privacy while protecting together the service provider and the user. Security problems of PaaS clouds are explored and classified. In this paper we are going to some major security issues of current cloud computing environments.

Distributed computing infrastructures (DCI) play an important role in modern research by enabling the use of multiple computing resources for solving complex problems. Despite the expansion of large-scale DCIs, the rapid integration of resources into ad-hoc infrastructures for small research projects or personal use still remains a challenge. The paper describes the integration and combined use of resources with Everest, a web-based distributed computing platform. In contrast to existing solutions, Everest follows the PaaS model and can be used remotely by multiple users to seamlessly run computations on arbitrary combinations of external resources attached by users. The integration of Everest with standalone resources and EGI grid is described. The presented approach is demonstrated by running real-world applications on ad-hoc infrastructures consisting of resources of different types.

Cloud computing is a promising computing paradigm which allows distribution of services from a pool of resources. The services are required by the clients through on-demand via pay and use method. The greatest utilization of resources and maximum profits with scheduling is the main goal of the cloud service providers. The major issue in cloud computing is scheduling of services with improved global throughput and job scheduling. Since, cloud computing is a service based one, the performance evaluation is an important criteria to be dealt with. In this paper, we propose a Priority based Queuing model to evaluate the services leased by the cloud service providers. We consider general service time and response time for arriving requests and the waiting requests are stored in the queue. The services are considered to be SaaS (Software as a Service), PaaS (Platform as a Service) and IaaS (Infrastructure as a Service). We construct this using Queuing model with markovian arrival rate, general service rate, ‘m’ number of servers, priority queue discipline and a buffer of size ‘r’. The advantage of the proposed analytical model is within the time span, the cloud service provider schedules his services to result in maximum profit.

Platform-as-a-Service (PaaS) is the Cloud area concentrating the major growth in the last years in terms of adoption and market share. At the same time Cloud models are evolving towards Hybrid Clouds through the consideration of service expansion across a diversity of Cloud offerings. So far, the majority of multi-cloud approaches have explored IaaS layer, neglecting the rest of the Cloud stack. This limits enterprises to enjoy the benefits of an agile, automated and adaptable infrastructure though flexibility provided by PaaS multi-cloud. This paper presents and compares results of two research projects addressing PaaS multi-cloud architectures: ASCETiC and SeaClouds.