Τι θα φέρει το Σύννεφο στη Διαχείριση Δεδομένων: Προκλήσεις και Ευκαιρίες Ελληνικό Συμπόσιο Διαχείρισης Δεδομένων 2010 Ευαγγελία Πιτουρά Τμήμα Πληροφορικής, Πανεπιστήμιο Ιωαννίνων, Ελλάδα

ΕΣΔΔ Αγία Νάπα DMOD Laboratory, University of Ioannina 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) What is the new research cloud infrastructure brings to data management? Το νέφος για τη διαχείριση 1  Started from an industry need for simplicity/easy of development (Amazon, google, yahoo)

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

ΕΣΔΔ Αγία Νάπα DMOD Laboratory, University of Ioannina 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

ΕΣΔΔ Αγία Νάπα DMOD Laboratory, University of Ioannina Το νέφος για τη διαχείριση δεδομένων 4 Approach 2 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

ΕΣΔΔ Αγία Νάπα DMOD Laboratory, University of Ioannina Το νέφος για τη διαχείριση δεδομένων 5 As a web service? API? Models: Key-value stores with provenance (time dimension) Analytical functionality - OLAP style processing Approach 3 Data management as a service

ΕΣΔΔ Αγία Νάπα DMOD Laboratory, University of Ioannina Το νέφος για τη διαχείριση δεδομένων 6 Approach 4 Use data management "favorites" across approaches Improve storage layer of any cloud Example: Indexes/data partitioning for MapReduce Replication/caching (+materialized views)

ΕΣΔΔ Αγία Νάπα DMOD Laboratory, University of Ioannina Mobile and distributed data management is especially relevant. What is 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

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

ΕΣΔΔ Αγία Νάπα DMOD Laboratory, University of Ioannina Ευχαριστώ