Security-as-a-Service in Multi-cloud and Federated Cloud Environments

The economic benefits of cloud computing are encouraging customers to bring complex applications and data into the cloud. However security remains the biggest barrier in the adoption of cloud, and with the advent of multi-cloud and federated clouds in practice security concerns are for applications and data in the cloud. This paper proposes security as a value added service, provisioned dynamically during deployment and operation management of an application in multi-cloud and federated clouds. This paper specifically considers a data protection and a host & application protection solution that are offered as a SaaS application, to validate the security services in a multi-cloud and federated cloud environment. This paper shares our experiences of validating these security services over a geographically distributed, large scale, multi-cloud and federated cloud infrastructure

Security-as-a-Service in Multi-cloud and Federated Cloud Environments: 9th IFIP WG 11.11 International Conference, IFIPTM 2015, Hamburg, Germany, May 26-28, 2015, Proceedings

The economic benefits of cloud computing are encouraging customers to bring complex applications and data into the cloud. However security remains the biggest barrier in the adoption of cloud, and with the advent of multi-cloud and federated clouds in practice security concerns are for applications and data in the cloud. This paper proposes security as a value added service, provisioned dynamically during deployment and operation management of an application in multi-cloud and federated clouds. This paper specifically considers a data protection and a host & application protection solution that are offered as a SaaS appli- cation, to validate the security services in a multi-cloud and federated cloud environment. This paper shares our experiences of validating these security services over a geographically distributed, large scale, multi-cloud and federated cloud infrastructure

Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud

With the advent of cloud computing, organizations are nowadays able to react rapidly to changing demands for computational resources. Not only individual applications can be hosted on virtual cloud infrastructures, but also complete business processes. This allows the realization of so-called elastic processes, i.e., processes which are carried out using elastic cloud resources. Despite the manifold benefits of elastic processes, there is still a lack of solutions supporting them. In this paper, we identify the state of the art of elastic Business Process Management with a focus on infrastructural challenges. We conceptualize an architecture for an elastic Business Process Management System and discuss existing work on scheduling, resource allocation, monitoring, decentralized coordination, and state management for elastic processes. Furthermore, we present two representative elastic Business Process Management Systems which are intended to counter these challenges. Based on our findings, we identify open issues and outline possible research directions for the realization of elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and P. Hoenisch (2015). Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud. Future Generation Computer Systems, Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00

A Model-Driven Engineering Approach for ROS using Ontological Semantics

This paper presents a novel ontology-driven software engineering approach for the development of industrial robotics control software. It introduces the ReApp architecture that synthesizes model-driven engineering with semantic technologies to facilitate the development and reuse of ROS-based components and applications. In ReApp, we show how different ontological classification systems for hardware, software, and capabilities help developers in discovering suitable software components for their tasks and in applying them correctly. The proposed model-driven tooling enables developers to work at higher abstraction levels and fosters automatic code generation. It is underpinned by ontologies to minimize discontinuities in the development workflow, with an integrated development environment presenting a seamless interface to the user. First results show the viability and synergy of the selected approach when searching for or developing software with reuse in mind.Comment: Presented at DSLRob 2015 (arXiv:1601.00877), Stefan Zander, Georg Heppner, Georg Neugschwandtner, Ramez Awad, Marc Essinger and Nadia Ahmed: A Model-Driven Engineering Approach for ROS using Ontological Semantic

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