An affinity analysis based CIM-to-PIM transformation

To tackle the problems such as the imperfection and inconsistency in software requirements in traditional Computation Independent Model (CIM) modelling, the low degree of automation as well as the imperfection in the description of Platform Independent Model (PIM) in CIM-to-PIM transforming, in this article, we propose a Business-Process-based CIM modelling method and a CIM-to-PIM transformation approach. Business Process Model is used to express CIM, and UML‘s Sequence Diagram, State Chart Diagram as well as Class Diagram are used to express PIM. Firstly, the users’ requirements are obtained through business process models. We extract use cases from business processes and create use case specifications. A verification mechanism is also added for the use case specification. Secondly, we transform CIMs into PIMs automatically with use case specifications as the inputs as well as combining with use case based thinking, responsibility based thinking and affinity analysis. Finally, by comparing with the methods in other studies, we conclude that methods proposed in this article can ensure model integrity and increase the degree of model transformation automation

An automated closed-loop framework to enforce security policies from anomaly detection

Due to the growing complexity and scale of IT systems, there is an increasing need to automate and streamline routine maintenance and security management procedures, to reduce costs and improve productivity. In the case of security incidents, the implementation and application of response actions require significant efforts from operators and developers in translating policies to code. Even if Machine Learning (ML) models are used to find anomalies, they need to be regularly trained/updated to avoid becoming outdated. In an evolving environment, a ML model with outdated training might put at risk the organization it was supposed to defend. To overcome those issues, in this paper we propose an automated closed-loop process with three stages. The first stage focuses on obtaining the Decision Trees (DT) that classify anomalies. In the second stage, DTs are translated into security Policies as Code based on languages recognized by the Policy Engine (PE). In the last stage, the translated security policies feed the Policy Engines that enforce them by converting them into specific instruction sets. We also demonstrate the feasibility of the proposed framework, by presenting an example that encompasses the three stages of the closed-loop process. The proposed framework may integrate a broad spectrum of domains and use cases, being able for instance to support the decide and the act stages of the ETSI Zero-touch Network & Service Management (ZSM) framework.info:eu-repo/semantics/publishedVersio

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR