Enhanced Cloud Computing Model Using Systematic Approach Towards The Quality Of Service In A Cloud Computing

Cloud computing is modrendevelopingtechnoloy which provides on-claim resources in cloud computing envoirnment.  Cloud computing is modern technology which guarantees to provide elastic Infrastructure, resources accessible via the Internet with low cost. Cloud refers to a huge bundle of computing and data resources which can be access to different protocols and interfaces. Cloud service model containsSoftware-as-a-service (SaaS),Infrastructure-as-a service (IaaS), and Platform-as-a-service (PaaS. Cloud users can enjoy these services without knowing the underlying technology behind the cloud. Quality of service playsa vital role in any network while providing efficient resourcesto users. To competitive gain, it is compulsory to cloud computing network operator  to gain  trust of users by providing the best quality of services. Resource virtualization, share pool of resources, on-demand network access, large datacentres, and highly-interactive web applications needs quality of services. In this paper we put an effort to enhance the cloud computing model to show the “Quality as-a-service(QaaS)”layer. This service layer will help the cloud provider how to enhance the quality of service to cloud users to gain competitive advantage over other cloud service providers.  Parameters which are to useto measure the quality of services includeService Response Time, Reliability, Interoperability, Accuracy,Execution time etc

Service-oriented architecture for big data and business intelligence analytics in the cloud

© 2017 by Taylor & Francis Group, LLC. Service-oriented architecture (SOA) has emerged, supporting scalability and service reuse. At the same time, Big Data analytics has impacted on business services and business process management. However, there is a lack of a systematic engineering approach to Big Data analytics. This chapter provides a systematic approach to SOA design strategies and business process for Big Data analytics. Our approach is based on SOA reference architecture and service component model for Big Data applications, known as softBD and also includes a large-scale, real-world case study demonstrating our approach to SOA for Big Data analytics. SOA Big Data architecture is scalable, generic, and customizable for a variety of data applications. The main contribution of this chapter includes a unique, innovative, and generic softBD framework, service component model, and a generic SOA architecture for large-scale Big Data applications. This chapter also contributes to Big Data metrics, which allows measurement and evaluation when analyzing data

The European Industrial Data Space (EIDS)

This research work has been performed in the framework of the Boost 4.0 Big Data lighthouse initiative, a project that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 780732. This datadriven digital transformation research is also endorsed by the Digital Factory Alliance (DFA)The path that the European Commission foresees to leverage data in the best possible way for the sake of European citizens and the digital single market clearly addresses the need for a European Data Space. This data space must follow the rules, derived from European values. The European Data Strategy rests on four pillars: (1) Governance framework for access and use; (2) Investments in Europe’s data capabilities and infrastructures; (3) Competences and skills of individuals and SMEs; (4) Common European Data Spaces in nine strategic areas such as industrial manufacturing, mobility, health, and energy. The project BOOST 4.0 developed a prototype for the industrial manufacturing sector, called European Industrial Data Space (EIDS), an endeavour of 53 companies. The publication will show the developed architectural pattern as well as the developed components and introduce the required infrastructure that was developed for the EIDS. Additionally, the population of such a data space with Big Data enabled services and platforms is described and will be enriched with the perspective of the pilots that have been build based on EIDS.publishersversionpublishe

Elastic Dataflow Processing on the Cloud

Τα νεφη εχουν μετατραπει σε μια ελκυστικη πλατφορμα για την πολυπλοκη επεξεργασια δεδομενων μεγαλης κλιμακας, ειδικα εξαιτιας της εννοιας της ελαστικοτητας, η οποια και τα χαρακτηριζει: οι υπολογιστικοι ποροι μπορουν να εκμισθωθουν δυναμικα και να χρησιμοποιουνται για οσο χρονο ειναι απαραιτητο. Αυτο δινει την δυνατοτητα να δημιουργηθει μια εικονικη υποδομη η οποια μπορει να αλλαζει δυναμικα στο χρονο. Οι συγχρονες εφαρμογες απαιτουν την εκτελεση πολυπλοκων ερωτηματων σε Μεγαλα Δεδομενα για την εξορυξη γνωσης και την υποστηριξη επιχειρησιακων αποφασεων. Τα πολυπλοκα αυτα ερωτηματα, εκφραζονται σε γλωσσες υψηλου επιπεδου και τυπικα μεταφραζονται σε ροες επεξεργασιας δεδομενων, η απλα ροες δεδομενων. Ενα λογικο ερωτημα που τιθεται ειναι κατα ποσον η ελαστικοτητα επηρεαζει την εκτελεση των ροων δεδομενων και με πιο τροπο. Ειναι λογικο οτι η εκτελεση να ειναι πιθανον γρηγοροτερη αν χρησιμοποιηθουν περισ- σοτεροι υπολογιστικοι ποροι, αλλα το κοστος θα ειναι υψηλοτερο. Αυτο δημιουργει την εννοια της οικο-ελαστικοτητας, ενος επιπλεον τυπου ελαστικοτητας ο οποιος προερχεται απο την οικονο- μικη θεωρια, και συλλαμβανει τις εναλλακτικες μεταξυ του χρονου εκτελεσης και του χρηματικου κοστους οπως προκυπτει απο την χρηση των πορων. Στα πλαισια αυτης της διδακτορικης διατριβης, προσεγγιζουμε την ελαστικοτητα με ενα ενοποιημενο μοντελο που περιλαμβανει και τις δυο ειδων ελαστικοτητες που υπαρχουν στα υπολογιστικα νεφη. Αυτη η ενοποιημενη προσεγγιση της ελαστικοτητας ειναι πολυ σημαντικη στην σχεδιαση συστηματων που ρυθμιζονται αυτοματα (auto-tuned) σε περιβαλλοντα νεφους. Αρχικα δειχνουμε οτι η οικο-ελαστικοτητα υπαρχει σε αρκετους τυπους υπολογισμου που εμφανιζονται συχνα στην πραξη και οτι μπορει να βρεθει χρησιμοποιωντας εναν απλο, αλλα ταυτοχρονα αποδοτικο και ε- πεκτασιμο αλγοριθμο. Επειτα, παρουσιαζουμε δυο εφαρμογες που χρησιμοποιουν αλγοριθμους οι οποιοι χρησιμοποιουν το ενοποιημενο μοντελο ελαστικοτητας που προτεινουμε για να μπορουν να προσαρμοζουν δυναμικα το συστημα στα ερωτηματα της εισοδου: 1) την ελαστικη επεξεργασια αναλυτικων ερωτηματων τα οποια εχουν πλανα εκτελεσης με μορφη δεντρων με σκοπο την μεγι- στοποιηση του κερδους και 2) την αυτοματη διαχειριση χρησιμων ευρετηριων λαμβανοντας υποψη το χρηματικο κοστος των υπολογιστικων και των αποθηκευτικων πορων. Τελος, παρουσιαζουμε το EXAREME, ενα συστημα για την ελαστικη επεξεργασια μεγαλου ογκου δεδομενων στο νεφος το οποιο εχει χρησιμοποιηθει και επεκταθει σε αυτην την δουλεια. Το συστημα προσφερει δηλωτικες γλωσσες που βασιζονται στην SQL επεκταμενη με συναρτησεις οι οποιες μπορει να οριστουν απο χρηστες (User-Defined Functions, UDFs). Επιπλεον, το συντακτικο της γλωσσας εχει επεκταθει με στοιχεια παραλληλισμου. Το EXAREME εχει σχεδιαστει για να εκμεταλλευεται τις ελαστικοτη- τες που προσφερουν τα νεφη, δεσμευοντας και αποδεσμευοντας υπολογιστικους πορους δυναμικα με σκοπο την προσαρμογη στα ερωτηματα.Clouds have become an attractive platform for the large-scale processing of modern applications on Big Data, especially due to the concept of elasticity, which characterizes them: resources can be leased on demand and used for as much time as needed, offering the ability to create virtual infrastructures that change dynamically over time. Such applications often require processing of complex queries that are expressed in a high-level language and are typically transformed into data processing flows (dataflows). A logical question that arises is whether elasticity affects dataflow execution and in which way. It seems reasonable that the execution is faster when more resources are used, however the monetary cost is higher. This gives rise to the concept eco-elasticity, an additional kind of elasticity that comes from economics, and captures the trade-offs between the response time of the system and the amount of money we pay for it as influenced by the use of different amounts of resources. In this thesis, we approach the elasticity of clouds in a unified way that combines both the traditional notion and eco-elasticity. This unified elasticity concept is essential for the development of auto-tuned systems in cloud environments. First, we demonstrate that eco-elasticity exists in several common tasks that appear in practice and that can be discovered using a simple, yet highly scalable and efficient algorithm. Next, we present two cases of auto-tuned algorithms that use the unified model of elasticity in order to adapt to the query workload: 1) processing analytical queries in the form of tree execution plans in order to maximize profit and 2) automated index management taking into account compute and storage re- sources. Finally, we describe EXAREME, a system for elastic data processing on the cloud that has been used and extended in this work. The system offers declarative languages that are based on SQL with user-defined functions (UDFs) extended with parallelism primi- tives. EXAREME exploits both elasticities of clouds by dynamically allocating and deallocating compute resources in order to adapt to the query workload

The changing governance of higher education systems in Post-Soviet countries

25 Jahre nach dem Zusammenbruch der Sowjetunion sind aus einem unitären Hochschulsystem 15 einzigartige nationale Systeme hervorgegangen. Deren Entwicklung wurde von je eigenen ökonomischen, kulturellen und politischen Kräften beeinflusst und geprägt, sowohl nationalen wie internationalen Ursprungs (Johnstone and Bain 2002). Die vorliegende Doktorarbeit untersucht die Veränderungen der Governance von Hochschulsystemen der drei postsowjetischen Staaten Russland, Kasachstan und Moldau über den Zeitraum von 1991 bis 2015, analysiert, zu welchem Grad diese Entwicklungen einem Prozess der Konvergenz hin zu einem „globalen Modell“ oder einem „postsowjetischen Modell“ folgen und formuliert Hypothesen über die treibenden Kräfte und Pfadabhängigkeiten, welche auf nationalem, regionalen und globaler Ebene diese Entwicklungen befördert, gehemmt oder auf idiosynkratische Art und Weise geprägt haben. Die Ergebnisse zeigen, dass global propagierte Governanceinstrumente – wie z.B. Globalbudgets, erweiterte Befugnisse der Hochschulleitung, externe Qualitätssicherung, Stakeholdergovernancegremien – in allen drei untersuchten Ländern Verbreitung finden und ein Prozess der Konvergenz hin zu einem „global Modell“ der Hochschulgovernance stattfindet. Gleichzeitig zeigen die Ergebnisse, dass die spezifischen Eigenarten der nationalen Governancearrangements durch die Einführung dieser neuen Instrumente in der Regel nicht ersetzt werden und dem Bestehenden stattdessen als zusätzliche Ebenen hinzugefügt werden. Wo die Logiken der neuen mit den alten Strukturen kollidieren, zeigt sich, dass sich die tradierten Strukturen und Prozesse in der Regel durchsetzen. Zudem zeigt sich, dass die Governancearrangements der drei untersuchten Länder eine große Zahl spezieller Eigenschaften teilen, durch die sie sich systematisch von jenem propagierten globalen Modell abheben. Jenes „Postsowjetische Modell“ der Hochschulgovernance zeichnet sich durch dominante Rolle des Staates, Hierarchie als primäre und legitime Form der Governance sowie einen geringen Grad an Vertrauen zwischen den zentralen Akteuren des Hochschulsystems aus. Zuletzt illustriert die Dissertation die Divergenzen und Besonderheiten der Governancemodelle in Russland, Kasachstan und Moldau. Die vorliegende Dissertation leistet somit einen Beitrag zum Verständnis der Entwicklung der Governance der Hochschulsysteme in einer sich dynamisch entwickelten Weltregion, welche in der akademischen Literatur bislang nur wenig Aufmerksamkeit erhalten hat.:Table of Contents Table of Figures ............................................................................................................................. 5 Preliminary remarks and acknowledgements .................................................................................. 6 Glossary ........................................................................................................................................... 8 1 Abstract ........................................................................................................................................ 11 2 Introduction .................................................................................................................................. 13 2.1 Research Topic ...................................................................................................................... 13 2.2 Starting point and personal research interest ......................................................................... 14 2.3 Research approach ................................................................................................................. 15 2.4 Relevance to research and practice ........................................................................................ 16 2.5 Structure ................................................................................................................................ 16 3 Steps towards a framework of analysis ........................................................................................ 17 3.1 The Governance of Higher Education Systems ..................................................................... 17 3.1.1 Higher Education systems ............................................................................................. 17 3.1.2 Governance in higher education .................................................................................... 23 3.1.3 Summary: Making sense of higher education governance ............................................ 32 3.2 The changing governance of higher education systems ........................................................ 33 3.2.1 Conceptualizing forces of change in the governance of higher education systems: The ‘Glonacal’ agency heuristic ........................................................................................................... 33 3.2.2 Global trends and the emergence of a “global model” of higher education governance36 3.2.3 Instruments of Governance of Higher Education Systems ............................................ 49 3.2.4 Conclusion: A global model of HE governance? .......................................................... 66 3.3 State of research on the governance of higher education in post-Soviet countries ............... 67 3.3.1 European Integration in the post-Soviet space .............................................................. 70 4 Framework of Analysis and Research Design .............................................................................. 73 4.1 Research Questions and Scope of Analysis ........................................................................... 73 4.2 Research Methodology, Case Study Design, and Data Collection ........................................ 74 4.2.1 Case Studies and data collection ................................................................................... 74 4.2.2 Comparing the governance of higher education systems and assessing convergence .. 77 4.2.3 Discussion of validity and reliability of the chosen case study design .......................... 78 4.3 Limitations of the study ......................................................................................................... 79 5 The Point of Departure: The Soviet Union ................................................................................... 80 5.1 Introduction - Key features of the Soviet Higher Education system ..................................... 80 5.2 Structure of the HE system .................................................................................................... 83 5.3 The governance of higher education in the Soviet Union ..................................................... 85 5.3.1 Actors and their capabilities .......................................................................................... 85 5.3.2 Educational Standards and Quality Assurance .............................................................. 86 page 3 5.3.3 Regulation of admission into higher education ............................................................. 88 5.3.4 Institutional governance, decision-making and institutional autonomy ........................ 89 5.3.5 Financing of HEIs.......................................................................................................... 90 5.4 The HE Reforms of 1987 ...................................................................................................... 91 5.5 The break-up and transition of the Soviet higher education system ...................................... 94 6 The Russian Federation ................................................................................................................ 99 6.1 Introduction ........................................................................................................................... 99 6.2 The development of the governance of the higher education system in Russia .................... 99 6.2.1 De-regulation and marketization of higher education (1991-2000) ............................ 100 6.2.2 Renaissance of state control, internationalization and renewed investment into higher education (2000-2004) ................................................................................................................ 105 6.2.3 Asserting state control and promoting differentiation of the higher education system (2004-2012) ................................................................................................................................. 110 6.2.4 Differentiated state steering (2012-2016) .................................................................... 119 6.3 The governance model of the Russian HE system by 2015 ................................................ 128 7 The Republic of Kazakhstan ........................................................................................................ 134 7.1 Introduction ......................................................................................................................... 134 7.2 The development of the governance of the higher education system in Kazakhstan .......... 135 7.2.1 Establishing statehood and institutions (1991-1999) ................................................... 136 7.2.2 Curbing corruption and saddling the market (1999-2004) .......................................... 139 7.2.3 Preparing to join the Bologna Space (2005-2010) ...................................................... 146 7.2.4 Differentiation and expanding autonomy (2011-2017) ............................................... 153 7.3 The governance model of the Kazakh HE system by 2015 ................................................. 171 8 The Republic of Moldova ............................................................................................................. 173 8.1 Introduction ......................................................................................................................... 173 8.2 The development of the governance of the higher education system in Moldova .............. 176 8.2.1 Experimentation and laisser-faire after independence (1991-1994) ............................ 177 8.2.2 Attempts to establish impartial instruments to regulate quality (1994-2001) ............. 178 8.2.3 Re-Centralization of powers in the Ministry of Education (2001-2006) ..................... 181 8.2.4 Creation of dysfunctional public structures (2006-2009) ............................................ 183 8.2.5 The long struggle for a new system of governance (2009-2015) ................................ 184 8.3 The governance model of the Moldovan HE system by 2015 ............................................ 194 9 Cross-National Comparison of Developments and Discussion of Results ................................... 197 9.1 How has the governance of higher education systems changed between 1991-2015? ....... 197 9.1.1 Common challenges and similar answers .................................................................... 197 9.1.2 Diverging paths ........................................................................................................... 200 9.1.3 Two-track state steering system in Russia ................................................................... 203 9.1.4 Marketization and expanding state-overseen stakeholder governance in Kazakhstan 205 page 4 9.1.5 Imitation of “European” institutions in Moldova ........................................................ 207 9.2 Is there a convergence towards a “post-Soviet” or global model of governance of higher education systems? .......................................................................................................................... 208 9.2.1 Quality Assurance ....................................................................................................... 208 9.2.2 Institutional Governance and University Autonomy ................................................... 210 9.2.3 Regulation of access .................................................................................................... 211 9.2.4 Financing ..................................................................................................................... 212 9.2.5 Conclusion: Is there a common model of governance? ............................................... 213 9.3 The interplay of national, regional and global factors on the development of the governance of higher education .......................................................................................................................... 218 9.3.1 Global and European forces ........................................................................................ 218 9.3.2 Regional forces ............................................................................................................ 224 9.3.3 National-level: Governments and Ministries responsible for higher education .......... 225 9.3.4 National-level: Stakeholder organizations................................................................... 232 9.3.5 National-level: Higher Education Institutions ............................................................. 234 9.3.6 National-level: Institutional factors of path dependence ............................................. 235 10 Discussion and Outlook .............................................................................................................. 244 10.1 Concluding reflections on the contribution of this study to the field of research ................ 246 11 References .................................................................................................................................. 247 12 Annexes ...................................................................................................................................... 269 12.1 Annex 1: Russia - The governance of the higher education system .................................... 269 12.1.1 Russia: Structure of the higher education system ........................................................ 269 12.1.2 Actors and their capabilities ........................................................................................ 273 12.1.3 Instruments of higher education governance in Russia ............................................... 283 12.1.4 Competitive programs for investment and differentiation of higher education........... 295 12.2 Annex 2: Kazakhstan – The governance of the higher education system ........................... 299 12.2.1 Kazakhstan: Structure of the higher education system ................................................ 299 12.2.2 Actors and their capabilities ........................................................................................ 302 12.2.3 Instruments of higher education governance in Kazakhstan ....................................... 310 12.3 Annex 3: Moldova – The governance of the higher education system ............................... 322 12.3.1 Moldova: Structure of the higher education system .................................................... 322 12.3.2 Actors and their capabilities ........................................................................................ 325 12.3.3 Instruments of higher education governance in Moldova ........................................... 328 12.4 Annex 4: The European “infrastructure” of quality assurance ............................................ 336After 25 years of transformations of higher education systems in post-Soviet countries, the single Soviet model of higher education has evolved into fifteen unique national systems, shaped by economic, cul-tural, and political forces, both national and global (Johnstone and Bain 2002). International agencies such as the World Bank and the OECD have lobbied for a set of policies associated with the Washington Consensus (Neave, G. R. & van Vught, 1991). The Bologna Process has created isomorphic pressures, supported by EU policies and funding. Many post-Soviet States have responded to these influences, albeit with different motivations and unclear outcomes (Tomusk, 2011). Comparative research on these developments, however, is scarce and has primarily discussed them in terms of decentralization, mar-ketization and institutional autonomy (Heyneman 2010; Silova, 2011). This PhD thesis aims to 1) reconstruct the developments of governance of higher education systems, 2) analyze to what degree the developments represent a convergence towards a “global model” or a “Post-Soviet model” and 3) formulate hypotheses about driving forces and path dependencies at national, regional and global level which have driven or impeded these changes. Following work by Becher & Kogan (1992), Clark (1983), Jongbloed (2003), Paradeise (2009); Hood (2004); Dill (2010) and Dobbins et al. (2011), the research analyzes the object of analysis, the govern-ance of higher education systems, on five dimensions: 1. Educational Standards, quality assessment, and information provision; 2. Regulation of admissions to higher education; 3. Institutional structures, decision-making, and autonomy; 4. Higher education financing and incentive structures; and 5. The relationship of higher education and the state. Explanatory approaches draw upon perspectives of path dependence and models of institutional change drawing on work by North (1990), Steinmo (1992), Weick (1976), Pierson (2000) and Witte (2006). Three post-Soviet, non-EU, Bologna signatory states were selected to represent a diverse geographical sub-sample of the 15 post-Soviet States. The three countries studied in-depth are Russia, Moldova and Kazakhstan. The period of analysis comprises the changes taking place over a 25-year period between 1991 and 2015. Methodologically, the study rests on extensive literature analysis of previous academic publications, reports by international organizations such as the World Bank, OECD, and the EU, and national strategy papers. Building on this document analysis, over 60 semi-structured expert interviews were conducted with representatives of State organizations, HEIs and other stakeholder groups engaged in the govern-ance of higher education. The outcomes of interviews were used to situate developments in the particular page 12 social-political and societal contexts and to triangulate policy documents with various stakeholder per-spectives, in order to reconstruct how and why specific policy changes came about, were implemented or abandoned. The results show a differentiated picture: The governance instruments promoted by OECD, WB and EU are clearly recognizable in the 2015 governance arrangements in all three case countries. On this instru-ments-level “surface”, a process of convergence towards the “global model” is clearly taking place. While these new instruments are being adopted, however, the specific national governance arrangements persist and continue to matter. Only in isolated instances are old instruments fully displaced. More com-monly, new structures are added as additional layers to existing governance arrangements. The three countries continue to share a number of unique characteristics which sets them apart from the Anglo-Saxon higher education systems, which have inspired the “global model”. The dominating con-trolling role of the state has remained in place in all countries. This is strongly reinforced by national-level institutions and mental models which affirm hierarchy as the legitimate principle in governance and a lack of trust between actors in the system. In all case countries, the mutual expectation of state and HEIs alike remains that the state should be steering the higher education sector. This it does (Russia and Kazakhstan) or attempts to do (Moldova). Clearly, the adoption of governance instruments which are inspired by the “global model” does in no way equate with a retreat of the state. While the elements of university autonomy and stakeholder governance are slowly expanded, even this very process of loosening the reigns of the state is in great measure overseen and steered by the state. Shared character-istics, such as centralized control over admission; a state claim to steer and, in many cases, control the system; a hierarchical, authoritarian, personalized style of governance, management, leadership, as well as accountability form the discernable core of a common “post-Soviet” model of HE governance. The shared institutional past of the Soviet era, as well as common challenges, have facilitated and maintained these commonalities. As time passes, however, these post-Soviet commonalities are getting weaker. Divergent national-level forces and actors are driving or impeding reforms: While in Moldova, political volatility and underfund-ing have repeatedly undermined substantial reforms, Russia and Kazakhstan have each adopted govern-ance and management practices from New Public Management in new idiosyncratic ways: Kazakhstan has embarked on an authoritarian-driven decentralization program. Russia has created a two-tier system of state steering through financial incentivization and evaluation on the one hand, and tight oversight, control and intervention on the other. This dissertation sheds light on the developments, driving forces and mechanisms behind the convergence and divergence of approaches to higher education governance in an under-studied region of the world.:Table of Contents Table of Figures ............................................................................................................................. 5 Preliminary remarks and acknowledgements .................................................................................. 6 Glossary .......................................................................................................

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical