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Ευαγγελία Πιτουρά Τμήμα Πληροφορικής, Πανεπιστήμιο Ιωαννίνων, Ελλάδα dmod.cs.uoi.gr

Τι θα φέρει το Σύννεφο στη Διαχείριση Δεδομένων : Προκλήσεις και Ευκαιρίες Ελληνικό Συμπόσιο Διαχείρισης Δεδομένων 20 10. Ευαγγελία Πιτουρά Τμήμα Πληροφορικής, Πανεπιστήμιο Ιωαννίνων, Ελλάδα http://dmod.cs.uoi.gr.

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Ευαγγελία Πιτουρά Τμήμα Πληροφορικής, Πανεπιστήμιο Ιωαννίνων, Ελλάδα dmod.cs.uoi.gr

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  1. Τι θα φέρει το Σύννεφοστη Διαχείριση Δεδομένων:Προκλήσεις και ΕυκαιρίεςΕλληνικό Συμπόσιο Διαχείρισης Δεδομένων 2010 Ευαγγελία Πιτουρά Τμήμα Πληροφορικής, Πανεπιστήμιο Ιωαννίνων, Ελλάδα http://dmod.cs.uoi.gr

  2. What is the new research cloud infrastructure brings to datamanagement? Το νέφος για τη διαχείριση 1 • Δύο νεφελώδη ερωτήματα: • Τι είναι το νέφος; • Είναι κάτι νέο; Ομοιότητες με meta-computing, clusters, grid, κλπ • Shift from local servers to data centers hosted by large infrastructure companies • Pay-as-you-go utility computing • ελαστικότητα (όχι περιορισμοί στους πόρους) + κόστος (χρέωση) με βάση τη χρήση • Προβλέψιμη (;) απόδοση • Ευκολία στη χρήση/ανάπτυξη/επένδυση • economy of scale ("χοντρική-αντί-λιανική") • Scale (data, machines, etc) • Started from an industry need for simplicity/easy of development (Amazon, google, yahoo) DMOD Laboratory, University of Ioannina

  3. Το νέφος για τη διαχείριση δεδομένων 2 • IaaS: Infrastructure as a service • PaaS: Platform as a service • SaaS: Software as a Service • Level of abstraction providedto the programmer by the cloud • Where does data management fit in the stack? • Central point: transactional vs analytical data management DMOD Laboratory, University of Ioannina

  4. Το σύννεφο για τη διαχείριση δεδομένων 3 Approach 1 Build a DBMS on the cloud seen as an infrastructure (hardware) Build a traditional relational DBMS on Virtual Machines Transparency/Elasticity (by allocating new resources, etc) Cost Model ($) Complete Re-design/implementation DMOD Laboratory, University of Ioannina

  5. Το νέφος για τη διαχείριση δεδομένων 4 • Approach2 • Build a DBMS on the cloud seen as a platform • "DBMS"-functionality build on top of: • a "cloud"-like storage, (ie, key-value one) + a programming framework (i.e., MapReduce) • Extend the programming model with database (declarative) functionality (eg Pig Latin) • Query processing on top of MapReduce: query optimization, new implementation of physical operators (eg, joins) • Transaction properties (consistency, availability, durability, fault tolerance (for analytical: amount of work to be redone) • View definition/materilization functionality DMOD Laboratory, University of Ioannina

  6. Το νέφος για τη διαχείριση δεδομένων 5 Approach3 Data management as a service As a web service? API? Models: Key-value stores with provenance (time dimension) Analytical functionality - OLAP style processing DMOD Laboratory, University of Ioannina

  7. Το νέφος για τη διαχείριση δεδομένων 6 Approach4 Use data management "favorites" across approaches Improve storage layer of any cloud Example: Indexes/data partitioning for MapReduce Replication/caching (+materialized views) DMOD Laboratory, University of Ioannina

  8. Mobile and distributed data management is especially relevant. Whatis the "cloud face" of the state of the art there? • There is distribibution at the physical layer (more than one data centers, users geographically distributed) • it costs to move data • transaction management is expensive • Mobility of users • location information • unpredictability DMOD Laboratory, University of Ioannina

  9. How does "old" traditional research change? General Issues • Who owns the data • Analytics for performance • Quality of data/service • Economic Model • Elasticity is central • Scale (amount of data/users) DMOD Laboratory, University of Ioannina

  10. Ευχαριστώ DMOD Laboratory, University of Ioannina

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