VisibleZ: A Mainframe Architecture Emulator for Computing Education

This paper describes a PC-based mainframe computer emulator called VisibleZ and its use in teaching mainframe Computer Organization and Assembly Programming classes. VisibleZ models IBM’s z/Architecture and allows direct interpretation of mainframe assembly language object code in a graphical user interface environment that was developed in Java. The VisibleZ emulator acts as an interactive visualization tool to simulate enterprise computer architecture. The provided architectural components include main storage, CPU, registers, Program Status Word (PSW), and I/O Channels. Particular attention is given to providing visual clues to the user by color-coding screen components, machine instruction execution, and animation of the machine architecture components. Students interact with VisibleZ by executing machine instructions in a step-by-step mode, simultaneously observing the contents of memory, registers, and changes in the PSW during the fetch-decode-execute machine instruction cycle. The object-oriented design and implementation of VisibleZ allows students to develop their own instruction semantics by coding Java for existing specific z/Architecture machine instructions or design and implement new machine instructions. The use of VisibleZ in lectures, labs, and assignments is described in the paper and supported by a website that hosts an extensive collection of related materials. VisibleZ has been proven a useful tool in mainframe Assembly Language Programming and Computer Organization classes. Using VisibleZ, students develop a better understanding of mainframe concepts, components, and how the mainframe computer works. ACM Computing Classification System (1998): C.0, K.3.2.

On modeling and satisfaction of non-functional requirements using cloud computing

Specification of the non-functional requirements of applications and determining the required resources for their execution are activities that demand a great deal of technical knowledge, frequently resulting in an inefficient use of resources. Cloud computing is an alternative for provisioning of resources, which can be done using either the provider's own infrastructure or the infrastructure of one or more public clouds, or even a combination of both. It enables more flexibly/elastic use of resources, but does not solve the specification problem. In this paper we present an approach that uses models at runtime to facilitate the specification of non-functional requirements and resources, aiming to facilitate dynamic support for application execution in cloud computing environments with shared resources. © 2013 IEEE.

Reproducible computing with Tzar and Docker

The potential for sharing environmental data and models is huge, but can be challenging for experts without specific programming expertise. We built an open-source, cross-platform framework (‘Tzar’) to run models across distributed machines. Tzar is simple to set up and use, allows dynamic parameter generation and enhances reproducibility by accessing versioned data and code. Combining Tzar with Docker helps us lower the entry barrier further by versioning and bundling all required modules and dependencies, together with the database needed to schedule work.

Revisiting goal-oriented models for self-aware systems-of-systems

Systems-of-systems (SoS) are systems resulted from the interaction among other independent constituent systems that collaborate to offer new functionalities towards accomplishing global missions. Each of these constituent systems accomplishes its individual missions and is able to contribute to the achievement of the global missions of the SoS, both being viewed as a set of associated goals. In the perspective of self-aware systems, SoS need to exhibit goal-awareness, i.e., They need to be aware of their own goals and of how their constituent systems contribute to their accomplishment. In this paper, we revisit goal-oriented concepts aiming at identifying and modeling goals at both SoS level and the constituent systems level. Moreover, we take advantage of such goal-oriented models to express the relationship among goals at these levels as well as to define how each constituent system can contribute to the accomplishment of global goals of an SoS. In addition, we shed light on important issues related to goal modeling in self-aware SoS to be addressed in future research.

A számítási felhő az Európai Unió egén (Cloud computing on the sky of European Union)

Napjaink informatikai világának talán legkeresettebb hívó szava a cloud computing, vagy magyar fordításban, a számítási felhő. A fordítás forrása az EU-s (Digitális Menetrend magyar változata, 2010) A számítási felhő üzleti modelljének részletes leírását adja (Bőgel, 2009). Bőgel György ismerteti az új, közműszerű informatikai szolgáltatás kialakulását és gazdasági előnyeit, nagy jövőt jósolva a számítási felhőnek az üzleti modellek versenyében. A szerző – a számítási felhő üzleti előnyei mellett – nagyobb hangsúlyt fektet dolgozatában a gyors elterjedést gátló tényezőkre, és arra, hogy mit jelentenek az előnyök és a hátrányok egy üzleti, informatikai vagy megfelelőségi vezető számára. Nem csökkentve a cloud modell gazdasági jelentőségét, fontosnak tartja, hogy a problémákról és a kockázatokról is szóljon. Kiemeli, hogy a kockázatokban – különösen a biztonsági és adatvédelmi kockázatokban – lényeges különbségek vannak az Európai Gazdasági Térség és a világ többi része, pl. az Amerikai Egyesült Államok között. A cikkben rámutat ezekre a különbségekre, és az olvasó magyarázatot kap arra is, hogy miért várható a számítási felhő lassabb terjedése Európában, mint a világ más részein. Bemutatja az EU erőfeszítéseit is a számítási felhő európai terjedésének elősegítésére, tekintettel a modell versenyképességet növelő hatására. / === / One of the most popular concept of the recent web searches is cloud computing. Several authors present detailed description of the new service model and it's business benefits and cite the optimistic prognoses of the cloud experts regarding the competition of information system service models. The author analyses the operational benefits of the cloud application and give a detailed description of the inhibitors of the fast expansion of the service modell. He also analyses the pros and cons of the cloud for a business manager, an information and a compliance officer. When understanding the advantages of the cloud, it is equally important to review the problems and risks associated with the model. The paper gives a list of the expected cloud-specific risks. It also explains the differences in security and data protection approach between the European Economic Area and the rest of the world, including the USA. The explains why slower expansion of the cloud modell is expected in Europe than in the rest of the world. The efforts of the EU Committee in helping to spread the cloud model is also presented, as the EU's officers consider the model as an important element of competitiveness.

Power and thermal aware scheduling for real-time computing systems

Over the past few decades, we have been enjoying tremendous benefits thanks to the revolutionary advancement of computing systems, driven mainly by the remarkable semiconductor technology scaling and the increasingly complicated processor architecture. However, the exponentially increased transistor density has directly led to exponentially increased power consumption and dramatically elevated system temperature, which not only adversely impacts the system's cost, performance and reliability, but also increases the leakage and thus the overall power consumption. Today, the power and thermal issues have posed enormous challenges and threaten to slow down the continuous evolvement of computer technology. Effective power/thermal-aware design techniques are urgently demanded, at all design abstraction levels, from the circuit-level, the logic-level, to the architectural-level and the system-level. ^ In this dissertation, we present our research efforts to employ real-time scheduling techniques to solve the resource-constrained power/thermal-aware, design-optimization problems. In our research, we developed a set of simple yet accurate system-level models to capture the processor's thermal dynamic as well as the interdependency of leakage power consumption, temperature, and supply voltage. Based on these models, we investigated the fundamental principles in power/thermal-aware scheduling, and developed real-time scheduling techniques targeting at a variety of design objectives, including peak temperature minimization, overall energy reduction, and performance maximization. ^ The novelty of this work is that we integrate the cutting-edge research on power and thermal at the circuit and architectural-level into a set of accurate yet simplified system-level models, and are able to conduct system-level analysis and design based on these models. The theoretical study in this work serves as a solid foundation for the guidance of the power/thermal-aware scheduling algorithms development in practical computing systems.^

Coupled symplectic maps as models for subdiffusive processes in disordered Hamiltonian lattices

Acknowledgements One of us (T. B.) acknowledges many interesting discussions on coupled maps with Professor C. Tsallis. We are also grateful to the anonymous referees for their constructive feedback that helped us improve the manuscript and to the HPCS Laboratory of the TEI of Western Greece for providing the computer facilities where all our simulations were performed. C. G. A. was partially supported by the “EPSRC EP/I032606/1” grant of the University of Aberdeen. This research has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES - Investing in knowledge society through the European Social Fund.

Coupled transport in rotor models

Acknowledgement One of us (AP) wishes to acknowledge S. Flach for enlightening discussions about the relationship between the DNLS equation and the rotor model.

Coupled symplectic maps as models for subdiffusive processes in disordered Hamiltonian lattices

Acknowledgements One of us (T. B.) acknowledges many interesting discussions on coupled maps with Professor C. Tsallis. We are also grateful to the anonymous referees for their constructive feedback that helped us improve the manuscript and to the HPCS Laboratory of the TEI of Western Greece for providing the computer facilities where all our simulations were performed. C. G. A. was partially supported by the “EPSRC EP/I032606/1” grant of the University of Aberdeen. This research has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES - Investing in knowledge society through the European Social Fund.

Scientific Discovery in a Space of Structural Models: An Example from the History of Solution Chemistry

Postprint

Informal Qualitative Models: A Systematic Approach to their Generation

Postprint

Models and empirical data for the production of referring expressions

Article Accepted Date: 29 May 2014 Acknowledgements The authors gratefully acknowledge the support of the Cognitive Science Society for the organisation of the Workshop on Production of Referring Expressions: Bridging the Gap between Cognitive and Computational Approaches to Reference, from which this special issue originated. Funding Emiel Krahmer and Albert Gatt thank The Netherlands Organisation for Scientific Research (NWO) for VICI grant Bridging the Gap between Computational Linguistics and Psycholinguistics: The Case of Referring Expressions (grant number 277-70-007).

Studying Recommender Systems to Enhance Distributed Computing Schedulers

Distributed Computing frameworks belong to a class of programming models that allow developers to

launch workloads on large clusters of machines. Due to the dramatic increase in the volume of

data gathered by ubiquitous computing devices, data analytic workloads have become a common

case among distributed computing applications, making Data Science an entire field of

Computer Science. We argue that Data Scientist's concern lays in three main components: a dataset,

a sequence of operations they wish to apply on this dataset, and some constraint they may have

related to their work (performances, QoS, budget, etc). However, it is actually extremely

difficult, without domain expertise, to perform data science. One need to select the right amount

and type of resources, pick up a framework, and configure it. Also, users are often running their

application in shared environments, ruled by schedulers expecting them to specify precisely their resource

needs. Inherent to the distributed and concurrent nature of the cited frameworks, monitoring and

profiling are hard, high dimensional problems that block users from making the right

configuration choices and determining the right amount of resources they need. Paradoxically, the

system is gathering a large amount of monitoring data at runtime, which remains unused.

In the ideal abstraction we envision for data scientists, the system is adaptive, able to exploit

monitoring data to learn about workloads, and process user requests into a tailored execution

context. In this work, we study different techniques that have been used to make steps toward

such system awareness, and explore a new way to do so by implementing machine learning

techniques to recommend a specific subset of system configurations for Apache Spark applications.

Furthermore, we present an in depth study of Apache Spark executors configuration, which highlight

the complexity in choosing the best one for a given workload.

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.

Aerodynamic optimization using Adjoint methods and parametric CAD models

This paper describes an implementation of a method capable of integrating parametric, feature based, CAD models based on commercial software (CATIA) with the SU2 software framework. To exploit the adjoint based methods for aerodynamic optimisation within the SU2, a formulation to obtain geometric sensitivities directly from the commercial CAD parameterisation is introduced, enabling the calculation of gradients with respect to CAD based design variables. To assess the accuracy and efficiency of the alternative approach, two aerodynamic optimisation problems are investigated: an inviscid, 3D, problem with multiple constraints, and a 2D high-lift aerofoil, viscous problem without any constraints. Initial results show the new parameterisation obtaining reliable optimums, with similar levels of performance of the software native parameterisations. In the final paper, details of computing CAD sensitivities will be provided, including accuracy as well as linking geometric sensitivities to aerodynamic objective functions and constraints; the impact in the robustness of the overall method will be assessed and alternative parameterisations will be included.