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Τι θα φέρει το Σύννεφο στη Διαχείριση Δεδομένων:

Προκλήσεις και Ευκαιρίες

Ελληνικό Συμπόσιο Διαχείρισης Δεδομένων 2010

Ευαγγελία ΠιτουράΤμήμα Πληροφορικής,

Πανεπιστήμιο Ιωαννίνων, Ελλάδα

http://dmod.cs.uoi.gr

ΕΣΔΔ 10@ 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 datamanagement? Το νέφος για τη διαχείριση 1

Started from an industry need for simplicity/easy of development (Amazon, google, yahoo)

ΕΣΔΔ 10@ 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 10@ 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 10@ 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 10@ 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 10@ 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 10@ 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 10@ 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 10@ Αγία Νάπα DMOD Laboratory, University of Ioannina

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