Distributed Computing Primer UMBC CMSC 491 Hadoop-Based Distributed Computing Spring 2016 Adam Shook Some content adapted from Dr. Kalpakis’s CMSC 621.

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Presentation transcript:

Distributed Computing Primer UMBC CMSC 491 Hadoop-Based Distributed Computing Spring 2016 Adam Shook Some content adapted from Dr. Kalpakis’s CMSC 621 slides

Agenda Distributed Computing – Evolution of Computing Infrastructure – Networking Infrastructure – Properties of Distributed Systems – Example System Architectures

EVOLUTION OF COMPUTING INFRASTRUCTURE

Mainframe – 50s to 70s Custom hardware Custom low-level specialized code Very expensive solutions

Client/Server – 80s to 00s IT-led architectures More portable solutions Scalable solutions based on demand Reign of the Enterprise Data Warehouse

Cloud – 00s to Today Consumer-grade infrastructure Growing IaaS and PaaS markets Data revolution Focus on applications and not infrastructure

Where does Hadoop fit? A piece of your data infrastructure – Can crunch data for analytics – Can expose data for web applications Exploration of raw data Augments today’s infrastructure IMO, a big toolbox that can do a bit of everything

NETWORKING INFRASTRUCTURE

Single Server HDD CPU RAM NIC Server Scale Up Scale Out Faster CPUs Bigger Storage More Servers

Local-Area Network (LAN) Rack HDD CPU RAM NIC Server HDD CPU RAM NIC Server HDD CPU RAM NIC Server HDD CPU RAM NIC Server Rack HDD CPU RAM NIC Server HDD CPU RAM NIC Server HDD CPU RAM NIC Server HDD CPU RAM NIC Server WAN Gateway

Wide Area Network (WAN) London, England Beijing, China New York, NY

PROPERTIES OF DISTRIBUTED SYSTEMS

Distributed Systems The development of low-cost powerful microprocessors, together with the invention of high speed networks, enable us to construct computer systems by connecting a large number of computers A distributed system is a collection of independent computers that appears to its users as a single coherent system.

Properties of Distributed Systems Reliability Scalability Availability Efficiency CAP Theorem

Reliability Can the system deliver services in face of several component failures?

Scalability Can the system scale to support a growing number of tasks?

Availability How much latency is imposed on the system when a failure occurs?

Efficiency How efficient is the system, in terms of latency and throughput?

CAP Theorem Consistent Available Partition Tolerant Trade-off between Consistency and Availability

Stateful vs. Stateless Whether or not a distributed system saves their state on an attached device for recovery

EXAMPLE SYSTEM ARCHITECTURES

Simple Client/Server

Multi-Tiered Client/Server

Round-Robin Client/Server

Linux Reference A free and open source operating system In this course, we live in Eclipse and the command line Mastery of 'vi' gets you +4 charisma lpic1-v / uickref/linux.pdf

References Google Images