1. Distributed Computing Systems CSCI 4780/6780

2. Scalability ConceptExample Centralized servicesA single server for all users Centralized dataA single on-line telephone book Centralized algorithmsDoing routing based on complete information Scalability with respect to size Scalability with respect to geographical location Scalability with respect to management Scalability Problems

3. Example Problem: Design a Highly Scalable Map (Direction) Service Data – Route information Task – Computing shortest (least cost) routes Option 1: Store all data at a single server, perform all computations there Option 2: Store data centrally, but perform operations on multiple nodes Option 3: Store data at multiple locations and perform operations in a distributed fashion

4. Characteristics of Distributed Algorithms Single machine does not have complete information Decisions are made based on local information Failure of a node does not cause the algorithm to fail No global clock Distributed algorithms are much harder than centralized algorithms

5. Geographical Scalability Challenges Synchronous communication –Waiting for a reply does not scale well!! Unreliable networks –Broadcasting does not work Centralized components –Will eventually become bottlenecks Multiple administrative domains –Security mechanisms do not come for free

6. Scalability Techniques Hiding communication latencies –Asynchronous communication –Reduce amount of data transmitted Distribution –Spreading work across system Caching and replication –Make copies of data and services Balance load –Avoid hot spots

7. Scaling Techniques 1.4 The difference between letting: a)a server or b)a client check forms as they are being filled

8. Scaling Techniques (2) 1.5 An example of dividing the DNS name space into zones.

9. Pitfalls Network is reliable Network is secure Network is homogeneous Topology does not change Latency is zero Bandwidth is infinite Transport cost is zero Single administrator

10. Types of Distributed Systems High-Performance Computing Systems Cluster computing Grid computing Distributed Information Systems Transaction processing systems Enterprise application integration P2P systems Distributed Pervasive Systems Mobile networks Sensor networks

11. Cluster Computing Super computing using PCs/workstations Used for parallel programming Homogeneity among nodes Master allocates nodes to tasks Compute nodes run standard operating systems (and may be a very thin middleware) Middleware consists of parallel programming libraries

12. Cluster Computing Systems An example of a cluster computing system.