SE-CLOUD 2018 Abstracts
Full Papers
| Paper Nr: | 5 |
| Title: | Towards Supporting the Extended DevOps Approach through Multi-cloud Architectural Patterns for Design and Pre-deployment - A Tool Supported Approach |
| Authors: | Juncal Alonso, Marisa Escalante, Lena Farid, Maria Jose Lopez, Leire Orue-Echevarria and Simon Dutkowski |
| Abstract: | Recently the world of Cloud Computing is witnessing two major trends: Multi-cloud applications pushed by the increasing diversity of Cloud services leading to hybrid infrastructures and the DevOps paradigm, promising increased trust, faster software releases, and the ability to solve critical issues quickly (Steinborn, 2018). This paper presents a solution for merging and adapting both trends so that the benefits for software developers and operators are multiplied. The authors describe a tool-supported approach to extend the DevOps philosophy with the objective of supporting the design and pre-deployment of multi-cloud software applications. The paper begins with the presentation of the theoretical concepts, the proceeds with the description of the developed tools and the discussion of the validation performed with a sandbox application. |
| Paper Nr: | 7 |
| Title: | ZKPVM: A Zero-knowledge Authentication Protocol for VMs’ Live Migration in Mobile Cloud Computing |
| Authors: | Ed Kamya Kiyemba Edris and Mahdi Aiash |
| Abstract: | Mobile cloud computing is a model in which mobile applications are built, powered and hosted using cloud computing technology. Mobile devices with there limited resources will be accessing a wide variety of these cloud-based services such as video/audio streaming and online gaming. In order to improve the performance of this model, cloud-based services need to become aware of the movement of the mobile devices and to be launched closer to the demand. Such a requirement becomes achievable through virtual machine live migration, a feature that is currently supported in all virtualization platforms. Virtual machine live migration is widely performed in the data centres of the Cloud, for the purposes of load balance, reliability, availability, hardware maintenance and system upgrade. It entails moving all the state information of the virtual machine being migrated, including memory state, network state and storage state, from one physical server to another within the same data center or across different data centers. The security aspect of live migration has not been fully addressed yet. Some proposals rely on trusted third-parties for generating and producing the security parameters. Others assume the presence of pre-shared security parameters between the source and destination cloud providers. The author argues that such assumptions might not always be feasible in open, large scale cloud environment. Therefore, this paper introduces ZKPVM, a new authentication and key agreement protocol for securing virtual machine migration. The protocol is based on zero-knowledge authentication; it requires no knowledge between the source and destination cloud providers prior to the migration and it also does not demand the presence of a third-party. ZKPVM is formally verified using AVISPA formal methods and it is proven to meet a number of desired security properties. |
Short Papers
| Paper Nr: | 4 |
| Title: | An Intelligent Cloud Management Approach for the Workflow-cloud Framework WFCF |
| Authors: | Eric Kübler and Mirjam Minor |
| Abstract: | Workflow as a service is a recent trend in cloud computing. The opportunity to execute a workflow in a cloud is very attractive for business. There is, however, a lack of concepts for an integration of clouds and workflow management systems. Todays solutions are often not very effective in terms of resource usage. Further, they are not flexible enough to exchange the workflow management system, the cloud or multi-cloud environment. In this work, we evaluate WFCF our connector based integration framework for workflow management tools and clouds. WFCF uses intelligent methods to manage cloud resources with respect to monitoring information from both, the workflow and the cloud system. We introduce the architecture of WFCF and test the prototypical implementation. The evaluation is based on workflows from the music mastering domain. |
| Paper Nr: | 9 |
| Title: | Cost Comparison of Lambda Architecture Implementations for Transportation Analytics using Public Cloud Software as a Service |
| Authors: | Pedro F. Pérez-Arteaga, Cristian C. Castellanos, Harold Castro, Dario Correal, Luis A. Guzmán and Yves Denneulin |
| Abstract: | Lambda architecture has gained high relevance for big data analytics by offering mixed and coordinated data processing: real time processing for fast data streams and batch processing for large workloads with high latency. However, concrete implementations over cloud infrastructures and cost comparisons are still not being sufficiently analyzed. This paper presents a cost comparison of Lambda architecture implementations using Software as a Service (SaaS) to support IT decision makers when streaming-analytics solutions must be implemented. To do that, a case study of transportation analytics is developed on three public cloud providers: Google Cloud Platform, Microsoft Azure, and Amazon Web Services Cloud. The evaluation is carried out by comparing deployment, configuration, development, and performance costs in a public-transportation delay-monitoring case study assessing various concurrency scenarios. |
| Paper Nr: | 13 |
| Title: | Cloud Computing Market Segmentation |
| Authors: | Caesar Wu, Rajkumar Buyya and Kotagiri Ramamohanarao |
| Abstract: | The topics of cloud pricing models and resources management have been receiving enormous attention recently. However, very few studies have considered the importance of cloud market segmentation. Moreover, there is no a better, practical and quantifiable solution for a cloud service providers (CSP) to segment cloud market. We propose a novel solution that combines both hierarchical clustering and time series forecasting on the basis of the classical theory of market segmentation. In comparison with some traditional approaches, such as nested, analytic, Delphi, and strategy-based approaches, our method is much more effective, flexible, measurable and practical for CSPs to implement their cloud market strategies by rolling out different pricing models. Our tested results and empirical analysis show that our solution can efficiently segment cloud markets and also predict the market demands. Our primary goal is to offer a new solution so that CSPs can tail its limited cloud resources for its targeted market or cloud customers. |
Posters
| Paper Nr: | 6 |
| Title: | Cloud-RA: A Reference Architecture for Cloud Based Information Systems |
| Authors: | Jalal Kiswani, Sergiu M. Dascalu, Frederick C. Harris and Jr |
| Abstract: | Software architecture is an essential phase of the software development process, as it significantly increases the success rate of software projects and enables achieving their quality attributes and goals. However, implementing software architecture is not a straightforward process, and requires specialized expertise and knowledge -in both domain and technology-to achieve its requirements. To overcome this complexity, many tools have been developed to make the architecture process systemic, predictable and repeatable. These tools include architectural styles, architectural patterns, and reference architectures. In fact, these tools encourage sharing of experience and reducing the architecture process cost. In addition, tools such as reference architecture can make non-expert architects and developers start with ready-made architecture templates ”as is,” or with minimal customization. On the other hand, cloud computing is everywhere, and many applications are developed as cloud applications in what is called Software as a Service delivery model. In this paper, we propose Cloud-RA, a reference architecture for developing cloud-based multi-tenant information systems. In particular, it includes the problem, motivation, and proposed architecture. We hope this proposed work can be the bases for future cloud application reference architectures. |
Area 1 - Software Agents and Internet Computing
Short Papers| Paper Nr: | 10 |
| Title: | Air Traffic Safety Risk Assessment based on Rough Set and BP Neural Network |
| Authors: | Lan Ma, Weian Li and Zengxian Geng |
| Abstract: | The safety of air traffic control is an important link in the safety system of civil aviation industry. In order to evaluate the safety risk of air traffic control in a more comprehensive and reliable way, proposing an air traffic safety risk modeling and evaluation method based on rough set and BP neural network. After analyzing the factors that may affect the safety in the actual work of ATC, 24 attribute variables which can measure the safety risk of ATC are given. Aiming at the shortcomings of traditional neural network training with high redundancy, slow convergence and easy to fall into local optimum, the attribute reduction method is used to reduce the input attribute by rough set theory. Under the premise of not affecting the training results and the accuracy of the data, removing the low correlation attributes with the results, the network structure is simplified, the training times are reduced, and the training speed and accuracy of the neural network are improved. Use the simplified condition attributes of the original data after rough attribute reduction as input data, the conflict resolution object is as output data, using MATLAB to build the neural network, and the trained network is tested and verified to be reliable. Compared with the model before the reduction of the initial data, significantly improves the accuracy and efficiency. The model is verified by examples The results show that the combination of rough set and BP neural network can accurately evaluate the risk of air traffic control, change the risk assessment from qualitative to quantitative, and provide guidance for the actual operation. |
| Paper Nr: | 11 |
| Title: | Cloud Software Engineering: Traditional or Innovative – The Choice Is Yours |
| Authors: | Christoph Bussler |
| Abstract: | Cloud environments provide different levels of resource abstractions, most commonly categorized as IaaS (Infrastructure as a Service) and PaaS (Platform as a Service) as well as SaaS (Software as a Service – which is not relevant for this position paper). A detailed discussion of the difference between the IaaS and PaaS abstractions in this paper will lead to the following position: a single cloud software engineering process will not be sufficient for software development in the cloud. Depending on the target abstraction (IaaS or PaaS), the software engineering process will have to be different. It is predicted that the target abstractions of PaaS will dominate those of IaaS in the long run. |
| Paper Nr: | 12 |
| Title: | Software Engineering Approach to Bug Prediction Models using Machine Learning as a Service (MLaaS) |
| Authors: | Uma Subbiah, Muthu Ramachandran and Zaigham Mahmood |
| Abstract: | The presence of bugs in a software release has become inevitable. The loss incurred by a company due to the presence of bugs in a software release is phenomenal. Modern methods of testing and debugging have shifted focus from “detecting” to “predicting” bugs in the code. The existing models of bug prediction have not been optimized for commercial use. Moreover, the scalability of these models has not been discussed in depth yet. Taking into account the varying costs of fixing bugs, depending on which stage of the software development cycle the bug is detected in, this paper uses two approaches – one model which can be employed when the ‘cost of changing code’ curve is exponential and the other model can be used otherwise. The cases where each model is best suited are discussed. This paper proposes a model that can be deployed on a cloud platform for software development companies to use. The model in this paper aims to predict the presence or absence of a bug in the code, using machine learning classification models. Using Microsoft Azure’s machine learning platform this model can be distributed as a web service worldwide, thus providing Bug Prediction as a Service (BPaaS). |
Posters
| Paper Nr: | 2 |
| Title: | Non-functional Requirements for Real World Big Data Systems - An Investigation of Big Data Architectures at Facebook, Twitter and Netflix |
| Authors: | Thalita Vergilio and Muthu Ramachandran |
| Abstract: | This research represents a unique contribution to the field of Software Engineering for Big Data in the form of an investigation of the big data architectures of three well-known real-world companies: Facebook, Twitter and Netflix. The purpose of this investigation is to gather significant non-functional requirements for real-world big data systems, with an aim to addressing these requirements in the design of our own unique architecture for big data processing in the cloud: MC-BDP (Multi-Cloud Big Data Processing). MC-BDP represents an evolution of the PaaS-BDP architectural pattern, previously developed by the authors. However, its presentation is not within the scope of this paper. The scope of this comparative study is limited to the examination of academic papers, technical blogs, presentations, source code and documentation officially published by the companies under investigation. Ten non-functional requirements are identified and discussed in the context of these companies’ architectures: batch data, stream data, late and out-of-order data, processing guarantees, integration and extensibility, distribution and scalability, cloud support and elasticity, fault-tolerance, flow control, and flexibility and technology agnosticism. They are followed by the conclusion and considerations for future work. |