Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations

AIOCJ: A Choreographic Framework for Safe Adaptive Distributed Applications

We present AIOCJ, a framework for programming distributed adaptive applications. Applications are programmed using AIOC, a choreographic language suited for expressing patterns of interaction from a global point of view. AIOC allows the programmer to specify which parts of the application can be adapted. Adaptation takes place at runtime by means of rules, which can change during the execution to tackle possibly unforeseen adaptation needs. AIOCJ relies on a solid theory that ensures applications to be deadlock-free by construction also after adaptation. We describe the architecture of AIOCJ, the design of the AIOC language, and an empirical validation of the framework.Comment: Technical Repor

Issues in digital preservation: towards a new research agenda

Digital Preservation has evolved into a specialized, interdisciplinary research discipline of its own, seeing significant increases in terms of research capacity, results, but also challenges. However, with this specialization and subsequent formation of a dedicated subgroup of researchers active in this field, limitations of the challenges addressed can be observed. Digital preservation research may seem to react to problems arising, fixing problems that exist now, rather than proactively researching new solutions that may be applicable only after a few years of maturing. Recognising the benefits of bringing together researchers and practitioners with various professional backgrounds related to digital preservation, a seminar was organized in Schloss Dagstuhl, at the Leibniz Center for Informatics (18-23 July 2010), with the aim of addressing the current digital preservation challenges, with a specific focus on the automation aspects in this field. The main goal of the seminar was to outline some research challenges in digital preservation, providing a number of "research questions" that could be immediately tackled, e.g. in Doctoral Thesis. The seminar intended also to highlight the need for the digital preservation community to reach out to IT research and other research communities outside the immediate digital preservation domain, in order to jointly develop solutions

The Blended Learning Unit, University of Hertfordshire: A Centre for Excellence in Teaching and Learning, Evaluation Report for HEFCE

The University of Hertfordshire’s Blended Learning Unit (BLU) was one of the 74 Centres for Excellence in Teaching and Learning (CETLs) funded by the Higher Education Funding Council for England (HEFCE) between 2005 and 2010. This evaluation report follows HEFCE’s template. The first section provides statistical information about the BLU’s activity. The second section is an evaluative reflection responding to 13 questions. As well as articulating some of our achievements and the challenges we have faced, it also sets out how the BLU’s activity will continue and make a significant contribution to delivery of the University of Hertfordshire’s 2010-2015 strategic plan and its aspirations for a more sustainable future. At the University of Hertfordshire, we view Blended Learning as the use of Information and Communication Technology (ICT) to enhance the learning and learning experience of campus-based students. The University has an excellent learning technology infrastructure that includes its VLE, StudyNet. StudyNet gives students access to a range of tools, resources and support 24/7 from anywhere in the world and its robustness, flexibility and ease of use have been fundamental to the success of the Blended Learning agenda at Hertfordshire. The BLU has comprised a management team, expert teachers seconded from around the University, professional support and a Student Consultant. The secondment staffing model was essential to the success of the BLU. As well as enabling the BLU to become fully staffed within the first five months of the CETL initiative, it has facilitated access to an invaluable spectrum of Blended Learning, research and Change Management expertise to inform pedagogically sound developments and enable change to be embedded across the institution. The BLU used much of its capital funding to reduce barriers to the use of technology by, for example, providing laptop computers for all academic staff in the institution, enhancing classroom technology provision and wirelessly enabling all teaching accommodation. Its recurrent funding has supported development opportunities for its own staff and staff around the institution; supported evaluation activities relating to individual projects and of the BLU’s own impact; and supported a wide range of communication and dissemination activities internally and externally. The BLU has led the embedding a cultural change in relation to Blended Learning at the University of Hertfordshire and its impact will be sustained. The BLU has produced a rich legacy of resources for our own staff and for others in the sector. The University’s increased capacity in Blended Learning benefits all our students and provides a learning experience that is expected by the new generation of learners in the 21st century. The BLU’s staffing model and partnership ways of working have directly informed the structure and modus operandi of the University’s Learning and Teaching Institute (LTI). Indeed a BLU team will continue to operate within the LTI and help drive and support the implementation of the University’s 2010-2015 Strategic plan. The plan includes ambitions in relation to Distance Learning and Flexible learning and BLU will be working to enable greater engagement with students with less or no need to travel to the university. As well as opening new markets within the UK and overseas, even greater flexibility for students will also enable the University to reduce its carbon footprint and provide a multifaceted contribution to our sustainability agenda. We conclude this executive summary with a short paragraph, written by Eeva Leinonen, our former Deputy Vice-Chancellor, which reflects our aspiration to transform Learning and Teaching at the University of Hertfordshire and more widely in the sector. ‘As Deputy Vice Chancellor at Hertfordshire I had the privilege to experience closely the excellent work of the Blended Learning Unit, and was very proud of the enormous impact the CETL had not only across the University but also nationally and internationally. However, perhaps true impact is hard to judge at such close range, but now as Vice Principal (Education) at King's College London, I can unequivocally say that Hertfordshire is indeed considered as the leading Blended Learning university in the sector. My new colleagues at King's and other Russell Group Universities frequently seek my views on the 'Hertfordshire Blended Learning' experience and are keen to emulate the successes achieved at an institutional wide scale. The Hertfordshire CETL undoubtedly achieved not only what it set out to achieve, but much more in terms of scale and impact. All those involved in this success can be justifiably proud of their achievements.’ Professor Eeva Leinonen, Vice Principal (Education), King's College, Londo

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Managing stimulation of regional innovation subjects’ interaction in the digital economy

The reported study was funded by RFBR according to the research project No. 18-01000204_a, No. 16-07-00031_a, No. 18-07-00975_a.Purpose: The article is devoted to solving fundamental scientific problems in the scope of the development of forecasting modeling methods and evaluation of regional company’s innovative development parameters, synthesizing new methods of big data processing and intelligent analysis, as well as methods of knowledge eliciting and forecasting the dynamics of regional innovation developments through benchmarking. Design/Methodology/Approach: For regional economic development, it is required to identify the mechanisms that contribute to (or impede) the innovative economic development of the regions. The synergetic approach to management is based on the fact that there are multiple paths of IS development (scenarios with different probabilities), although it is necessary to reach the required attractor by meeting the management goals. Findings: The present research is focused on obtainment of new knowledge in creating a technique of multi-agent search, collection and processing of data on company’s innovative development indicators, models and methods of intelligent analysis of the collected data. Practical Implications: The author developed recommendations before starting the process of institutional changes in a specific regional innovation system. The article formulates recommendations on the implementation of institutional changes in the region taking into account the sociocultural characteristics of the region’s population. Originality/Value: It is the first time, when a complex of models and methods is based on the use of a convergent model of large data volumes processing is presented.peer-reviewe

CloudScope: diagnosing and managing performance interference in multi-tenant clouds

© 2015 IEEE.Virtual machine consolidation is attractive in cloud computing platforms for several reasons including reduced infrastructure costs, lower energy consumption and ease of management. However, the interference between co-resident workloads caused by virtualization can violate the service level objectives (SLOs) that the cloud platform guarantees. Existing solutions to minimize interference between virtual machines (VMs) are mostly based on comprehensive micro-benchmarks or online training which makes them computationally intensive. In this paper, we present CloudScope, a system for diagnosing interference for multi-tenant cloud systems in a lightweight way. CloudScope employs a discrete-time Markov Chain model for the online prediction of performance interference of co-resident VMs. It uses the results to optimally (re)assign VMs to physical machines and to optimize the hypervisor configuration, e.g. the CPU share it can use, for different workloads. We have implemented CloudScope on top of the Xen hypervisor and conducted experiments using a set of CPU, disk, and network intensive workloads and a real system (MapReduce). Our results show that CloudScope interference prediction achieves an average error of 9%. The interference-aware scheduler improves VM performance by up to 10% compared to the default scheduler. In addition, the hypervisor reconfiguration can improve network throughput by up to 30%