Biblio
The purpose of this paper is to analyze all Cloud based Service Models, Continuous Integration, Deployment and Delivery process and propose an Automated Continuous Testing and testing as a service based TestBot and metrics dashboard which will be integrated with all existing automation, bug logging, build management, configuration and test management tools. Recently cloud is being used by organizations to save time, money and efforts required to setup and maintain infrastructure and platform. Continuous Integration and Delivery is in practice nowadays within Agile methodology to give capability of multiple software releases on daily basis and ensuring all the development, test and Production environments could be synched up quickly. In such an agile environment there is need to ramp up testing tools and processes so that overall regression testing including functional, performance and security testing could be done along with build deployments at real time. To support this phenomenon, we researched on Continuous Testing and worked with industry professionals who are involved in architecting, developing and testing the software products. A lot of research has been done towards automating software testing so that testing of software product could be done quickly and overall testing process could be optimized. As part of this paper we have proposed ACT TestBot tool, metrics dashboard and coined 4S quality metrics term to quantify quality of the software product. ACT testbot and metrics dashboard will be integrated with Continuous Integration tools, Bug reporting tools, test management tools and Data Analytics tools to trigger automation scripts, continuously analyze application logs, open defects automatically and generate metrics reports. Defect pattern report will be created to support root cause analysis and to take preventive action.
Cloud computing is widely believed to be the future of computing. It has grown from being a promising idea to one of the fastest research and development paradigms of the computing industry. However, security and privacy concerns represent a significant hindrance to the widespread adoption of cloud computing services. Likewise, the attributes of the cloud such as multi-tenancy, dynamic supply chain, limited visibility of security controls and system complexity, have exacerbated the challenge of assessing cloud risks. In this paper, we conduct a real-world case study to validate the use of a supply chaininclusive risk assessment model in assessing the risks of a multicloud SaaS application. Using the components of the Cloud Supply Chain Cyber Risk Assessment (CSCCRA) model, we show how the model enables cloud service providers (CSPs) to identify critical suppliers, map their supply chain, identify weak security spots within the chain, and analyse the risk of the SaaS application, while also presenting the value of the risk in monetary terms. A key novelty of the CSCCRA model is that it caters for the complexities involved in the delivery of SaaS applications and adapts to the dynamic nature of the cloud, enabling CSPs to conduct risk assessments at a higher frequency, in response to a change in the supply chain.
Science gateways bring out the possibility of reproducible science as they are integrated into reusable techniques, data and workflow management systems, security mechanisms, and high performance computing (HPC). We introduce BioinfoPortal, a science gateway that integrates a suite of different bioinformatics applications using HPC and data management resources provided by the Brazilian National HPC System (SINAPAD). BioinfoPortal follows the Software as a Service (SaaS) model and the web server is freely available for academic use. The goal of this paper is to describe the science gateway and its usage, addressing challenges of designing a multiuser computational platform for parallel/distributed executions of large-scale bioinformatics applications using the Brazilian HPC resources. We also present a study of performance and scalability of some bioinformatics applications executed in the HPC environments and perform machine learning analyses for predicting features for the HPC allocation/usage that could better perform the bioinformatics applications via BioinfoPortal.
The objective of the paper is to propose a social network security management model for a multi-tenancy SaaS application using Unified Communications as a Service (UCaaS) approach. The earlier security management models do not cover the issues when data inadvertently get exposed to other users due to poor implementation of the access management processes. When a single virtual machine moves or dissolves in the network, many separate machines may bypass the security conditions that had been implemented for its neighbors which lead to vulnerability of the hosted services. When the services are multi-tenant, the issue becomes very critical due to lack of asynchronous asymmetric communications between virtual when more number of applications and users are added into the network creating big data issues and its identity. The TRAIN model for the security management using PC-FAST algorithm is proposed in order to detect and identify the communication errors between the hosted services.