PyGirl Generating Whole-System VMs from high-level models using PyPy

Virtual machines emulating hardware devices are generally implemented in low-level languages and using a low-level style for performance reasons. This trend results in largely difficult to understand, difficult to extend and unmaintainable systems. As new general techniques for virtual machines arise, it gets harder to incorporate or test these techniques because of early design and optimization decisions. In this paper we show how such decisions can be postponed to later phases by separating virtual machine implementation issues from the high-level machine-specific model. We construct compact models of whole-system VMs in a high-level language, which exclude all low-level implementation details. We use the pluggable translation toolchain PyPy to translate those models to executables. During the translation process, the toolchain reintroduces the VM implementation and optimization details for specific target platforms. As a case study we implement an executable model of a hardware gaming device. We show that our approach to VM building increases understandability, maintainability and extendability while preserving performance.

PyGirl: Generating Whole-System VMs from High-Level Prototypes Using PyPy

Virtual machines (VMs) emulating hardware devices are generally implemented in low-level languages for performance reasons. This results in unmaintainable systems that are difficult to understand. In this paper we report on our experience using the PyPy toolchain to improve the portability and reduce the complexity of whole-system VM implementations. As a case study we implement a VM prototype for a Nintendo Game Boy, called PyGirl, in which the high-level model is separated from low-level VM implementation issues. We shed light on the process of refactoring from a low-level VM implementation in Java to a high-level model in RPython. We show that our whole-system VM written with PyPy is significantly less complex than standard implementations, without substantial loss in performance.

Volcanostratigraphic Controls on the Occurrence of Massive Sulfide (VMS) Deposits in the Semail Ophiolite, Oman

The Semail ophiolite in Oman is capped by up to 2 km of basaltic-andesitic lavas that host copper-dominant, Cyprus-type, volcanogenic massive sulfide (VMS) deposits. This study identifies multiple volcanostratigraphic horizons on which the deposits are situated, based on characterization of footwall and hanging-wall lavas from 16 deposits or deposit clusters. Comparison of field and petrographic features, compositions of igneous clinopyroxenes, and whole-rock geochemical signatures permits classification of the lavas within a modified version of the established regional volcanostratigraphy. Four extrusive units host deposits: Geotimes (earliest), Lasail, Alley, and Boninitic Alley (latest). The latter was previously known only at few localities, but this study reveals its regional extent and significance as a host for VMS deposits. The Geotimes and Lasail units represent Late Cretaceous, ocean spreading ridge and related off-axis volcanic environments, respectively. The Alley and Boninitic Alley units represent younger, subduction-related volcanism prior to Coniacian-Santonian obduction of the ophiolite. Our results show that VMS deposits occur on or near the Geotimes/Lasail and Geotimes/Alley contacts as well as entirely within the Geotimes, Lasail, Alley, and Boninitic Alley units. Highest Cu grades tend to occur in deposits lying on or within the Geotimes, whereas highest Au grades occur in deposits within the Boninitic Alley. In contrast to earlier studies, we conclude that essentially every horizon marking a hiatus in lava deposition in the Semail ophiolite, i.e., contacts between the four major eruptive units, and umbers and sedimentary chert layers within the units, has exploration potential for Cu-Au VMS deposits.

Back to the future in one week - implementing a Smalltalk VM in PyPy

We report on our experiences with the Spy project, including implementation details and benchmark results. Spy is a re-implementation of the Squeak (i.e., Smalltalk-80) VM using the PyPy toolchain. The PyPy project allows code written in RPython, a subset of Python, to be translated to a multitude of different backends and architectures. During the translation, many aspects of the implementation can be independently tuned, such as the garbage collection algorithm or threading implementation. In this way, a whole host of interpreters can be derived from one abstract interpreter definition. Spy aims to bring these benefits to Squeak, allowing for greater portability and, eventually, improved performance. The current Spy codebase is able to run a small set of benchmarks that demonstrate performance superior to many similar Smalltalk VMs, but which still run slower than in Squeak itself. Spy was built from scratch over the course of a week during a joint Squeak-PyPy Sprint in Bern last autumn.

Dynamic Optimization of SLA-Based Services Scaling Rules

Current advanced cloud infrastructure management solutions allow scheduling actions for dynamically changing the number of running virtual machines (VMs). This approach, however, does not guarantee that the scheduled number of VMs will properly handle the actual user generated workload, especially if the user utilization patterns will change. We propose using a dynamically generated scaling model for the VMs containing the services of the distributed applications, which is able to react to the variations in the number of application users. We answer the following question: How to dynamically decide how many services of each type are needed in order to handle a larger workload within the same time constraints? We describe a mechanism for dynamically composing the SLAs for controlling the scaling of distributed services by combining data analysis mechanisms with application benchmarking using multiple VM configurations. Based on processing of multiple application benchmarks generated data sets we discover a set of service monitoring metrics able to predict critical Service Level Agreement (SLA) parameters. By combining this set of predictor metrics with a heuristic for selecting the appropriate scaling-out paths for the services of distributed applications, we show how SLA scaling rules can be inferred and then used for controlling the runtime scale-in and scale-out of distributed services. We validate our architecture and models by performing scaling experiments with a distributed application representative for the enterprise class of information systems. We show how dynamically generated SLAs can be successfully used for controlling the management of distributed services scaling.

Diagnosis and Treatment of Venous Malformations Consensus Document of the International Union of Phlebology (IUP): updated 2013

Venous malformations (VMs) are the most common vascular developmental anomalies (birth defects) . These defects are caused by developmental arrest of the venous system during various stages of embryogenesis. VMs remain a difficult diagnostic and therapeutic challenge due to the wide range of clinical presentations, unpredictable clinical course, erratic response to the treatment with high recurrence/persistence rates, high morbidity following non-specific conventional treatment, and confusing terminology. The Consensus Panel reviewed the recent scientific literature up to the year 2013 to update a previous IUP Consensus (2009) on the same subject. ISSVA Classification with special merits for the differentiation between the congenital vascular malformation (CVM) and vascular tumors was reinforced with an additional review on syndrome-based classification. A "modified" Hamburg classification was adopted to emphasize the importance of extratruncular vs. truncular sub-types of VMs. This incorporated the embryological origin, morphological differences, unique characteristics, prognosis and recurrence rates of VMs based on this embryological classification. The definition and classification of VMs were strengthened with the addition of angiographic data that determines the hemodynamic characteristics, the anatomical pattern of draining veins and hence the risk of complication following sclerotherapy. The hemolymphatic malformations, a combined condition incorporating LMs and other CVMs, were illustrated as a separate topic to differentiate from isolated VMs and to rectify the existing confusion with name-based eponyms such as Klippel-Trenaunay syndrome. Contemporary concepts on VMs were updated with new data including genetic findings linked to the etiology of CVMs and chronic cerebrospinal venous insufficiency. Besides, newly established information on coagulopathy including the role of D-Dimer was thoroughly reviewed to provide guidelines on investigations and anticoagulation therapy in the management of VMs. Congenital vascular bone syndrome resulting in angio-osteo-hyper/hypotrophy and (lateral) marginal vein was separately reviewed. Background data on arterio-venous malformations was included to differentiate this anomaly from syndrome-based VMs. For the treatment, a new section on laser therapy and also a practical guideline for follow up assessment were added to strengthen the management principle of the multidisciplinary approach. All other therapeutic modalities were thoroughly updated to accommodate a changing concept through the years.

Service Level Agreements-Driven Management of Distributed Applications in Cloud Computing Environments

Cloud Computing enables provisioning and distribution of highly scalable services in a reliable, on-demand and sustainable manner. However, objectives of managing enterprise distributed applications in cloud environments under Service Level Agreement (SLA) constraints lead to challenges for maintaining optimal resource control. Furthermore, conflicting objectives in management of cloud infrastructure and distributed applications might lead to violations of SLAs and inefficient use of hardware and software resources. This dissertation focusses on how SLAs can be used as an input to the cloud management system, increasing the efficiency of allocating resources, as well as that of infrastructure scaling. First, we present an extended SLA semantic model for modelling complex service-dependencies in distributed applications, and for enabling automated cloud infrastructure management operations. Second, we describe a multi-objective VM allocation algorithm for optimised resource allocation in infrastructure clouds. Third, we describe a method of discovering relations between the performance indicators of services belonging to distributed applications and then using these relations for building scaling rules that a CMS can use for automated management of VMs. Fourth, we introduce two novel VM-scaling algorithms, which optimally scale systems composed of VMs, based on given SLA performance constraints. All presented research works were implemented and tested using enterprise distributed applications.

Motivational profiles of sport event volunteers.

Aim of the study Due to the valuable contribution made by volunteers to sporting events, a better understanding of volunteers’ motivation is imperative for event managers in order to develop effective volunteer re-cruitment and retention strategies. The adoption of working conditions and task domains to the mo-tives and needs of volunteers is one of the key challenges in volunteer management. Conversely, an ignorance of the motives and needs of volunteers could negatively affect their performance and attitude, which will have negative consequences for the execution of events (Strigas & Jackson, 2003). In general, the motives of volunteers are located on a continuum between selflessness (e.g. helping others), and self-interest (e.g. pursuing one’s own interests). Furthermore, it should take into account that volunteers may be motivated by more than one need or goal, and therefore, configure different bundles of motives, resulting in heterogeneous types of motives for voluntary engagement (Dolnicar & Randle, 2007). Despite the extensive number of studies on the motives of sport event volunteers, only few studies focus on the analysis of individual motive profiles concerning volun-teering. Accordingly, we will take a closer look at the following questions: To what extent do volun-teers at sporting events differ in the motives of their engagement, and how can the volunteers be ade-quately classified? Theoretical Background According to the functional approach, relevant subjective motives are related to the outcomes and consequences that volunteering is supposed to lead to and to produce. This means, individuals’ mo-tives determine which incentives are anticipated in return for volunteering (e.g. increase in social contacts), and are important for engaging in volunteering, e.g. the choice between different oppor-tunities for voluntary activity, or different tasks (Stukas et al., 2009). Additionally, inter-individual differences of motive structures as well as matching motives in the reflections of voluntary activities will be considered by using a person-oriented approach. In the person-oriented approach, it is not the specific variables that are made the entities of investigation, but rather persons with a certain combination of characteristic features (Bergmann et al., 2003). Person-orientation in the field of sports event volunteers, it is therefore essential to implement an orientation towards people as a unit of analysis. Accordingly, individual motive profiles become the object of investigation. The individ-ual motive profiles permit a glimpse of intra-individual differences in the evaluation of different motive areas, and thus represent the real subjective perspective. Hence, a person will compare the importance of individual motives for his behaviour primarily in relation to other motives (e.g. social contacts are more important to me than material incentives), and make fewer comparisons with the assessments of other people. Methodology, research design and data analysis The motives of sports event volunteers were analysed in the context of the European Athletics Championships 2014 in Zürich. After data cleaning, the study sample contained a total of 1,169 volunteers, surveyed by an online questionnaire. The VMS-ISA scale developed by Bang and Chel-ladurai (2009) was used and replicated successfully by a confirmatory factor analysis. Accordingly, all seven factors of the scale were included in the subsequent cluster analysis to determine typical motive profiles of volunteers. Before proceeding with the cluster analysis, an intra-individual stand-ardization procedure (according to Spiel, 1998) was applied to take advantage of the intra-individual relationships between the motives of the volunteers. Intra-individual standardization means that every value of each motive dimension was related to the average individual level of ex-pectations. In the final step, motive profiles were determined using a hierarchic cluster analysis based on Ward’s method with squared Euclidean distances. Results, discussion and implications The results reveal that motivational processes differ among sports event volunteers, and that volunteers sometimes combine contradictory bundles of motives. In our study, four different volunteer motive profiles were identified and described by their positive levels on the individual motive dimension: the community supporters, the material incentive seekers, the social networkers, and the career and personal growth pursuers. To describe the four identified motive profiles in more detail and to externally validate them, the clusters were analysed in relation to socio-economic, sport-related, and voluntary work characteristics. This motive-based typology of sports event volunteers can provide valuable guidance for event managers in order to create distinctive and designable working conditions and tasks at sporting events that should, in relation to a person-oriented approach, be tailored to a wide range of individ-ual prerequisites. Furthermore, specific recruitment procedures and appropriate communication measures can be defined in order to approach certain groups of potential volunteers more effectively. References Bang, H., & Chelladurai, P. (2009). Development and validation of the volunteer motivations scale for international sporting events (VMS-ISE). International Journal Sport Management and Market-ing, 6, 332-350. Bergmann, L. R., Magnusson, D., & El-Khouri, B. M. (2003). Studying individual development in an interindividual context. Mahwah, NJ: Erlbaum. Dolnicar, S., & Randle, M. (2007). What motivates which volunteers? Psychographic heterogeneity among volunteers in Australia. Voluntas, 18, 135-155. Spiel, C. (1998). Four methodological approaches to the study of stability and change in develop-ment. Methods of Psychological Research Online, 3, 8-22. Stukas, A. A., Worth, K. A., Clary, E. G., & Snyder, M. (2009). The matching of motivations to affordances in the volunteer environment: an index for assessing the impact of multiple matches on volunteer outcomes. Nonprofit and Voluntary Sector Quarterly, 38, 5-28.

Service level agreements-driven management of distributed applications in cloud computing environments

Advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing workload conditions, such as number of connected users, application performance might suffer, leading to violations of Service Level Agreements (SLA) and possible inefficient use of hardware resources. Combining dynamic application requirements with the increased use of virtualised computing resources creates a challenging resource Management context for application and cloud-infrastructure owners. In such complex environments, business entities use SLAs as a means for specifying quantitative and qualitative requirements of services. There are several challenges in running distributed enterprise applications in cloud environments, ranging from the instantiation of service VMs in the correct order using an adequate quantity of computing resources, to adapting the number of running services in response to varying external loads, such as number of users. The application owner is interested in finding the optimum amount of computing and network resources to use for ensuring that the performance requirements of all her/his applications are met. She/he is also interested in appropriately scaling the distributed services so that application performance guarantees are maintained even under dynamic workload conditions. Similarly, the infrastructure Providers are interested in optimally provisioning the virtual resources onto the available physical infrastructure so that her/his operational costs are minimized, while maximizing the performance of tenants’ applications. Motivated by the complexities associated with the management and scaling of distributed applications, while satisfying multiple objectives (related to both consumers and providers of cloud resources), this thesis proposes a cloud resource management platform able to dynamically provision and coordinate the various lifecycle actions on both virtual and physical cloud resources using semantically enriched SLAs. The system focuses on dynamic sizing (scaling) of virtual infrastructures composed of virtual machines (VM) bounded application services. We describe several algorithms for adapting the number of VMs allocated to the distributed application in response to changing workload conditions, based on SLA-defined performance guarantees. We also present a framework for dynamic composition of scaling rules for distributed service, which used benchmark-generated application Monitoring traces. We show how these scaling rules can be combined and included into semantic SLAs for controlling allocation of services. We also provide a detailed description of the multi-objective infrastructure resource allocation problem and various approaches to satisfying this problem. We present a resource management system based on a genetic algorithm, which performs allocation of virtual resources, while considering the optimization of multiple criteria. We prove that our approach significantly outperforms reactive VM-scaling algorithms as well as heuristic-based VM-allocation approaches.

Motivational profiles of sporting event volunteers

This article presents an approach for segmenting sporting event volunteers according to differences in their motives. Empirical data were obtained from a sample of 1169 volunteers who registered for the 2014 European Athletics Championships in Zürich. They completed the ‘Volunteer Motivation Scale for International Sporting Events’ (VMS-ISE) questionaire. The validity of the VMS-ISE was replicated by confirmatory factor analysis and the data were cluster analysed to identify distinct motivation-based volunteer profiles. These segmented volunteers on the basis of mutually exclusive motivational characteristics. The external validity of the four motivation-based types (‘community supporters’, ‘material incentive seekers’, ‘social networkers’ and ‘career and personal growth orienteers’) was confirmed with socio-economic, sport-related and volunteer activity-related variables. It is concluded that motivation-based segmentation represents a useful way of gaining a clearer understanding of the patterns underlying the heterogeneity of sporting events volunteers.