1. Consistency in Distributed Systems
By Amanda Carbonari and Thaddeus M. Diede
9/22/2018
CS 455: Introduction to distributed systems
Dept. of computer science, Colorado state university

2. Why is Consistency Important?
Consistency – The state of shared and replicated resources amongst the servers and nodes of a distributed computing system
Agreement on the value by all components: consistent state.
Fundamental feature in a distributed system design
Reliability, scalability, system performance
Consistency issues arose out of employing remote replication for
Increased reliability
Better load balancing
9/22/2018
CS 455: Introduction to distributed systems Dept. of computer science, Colorado state university

3. Problem Characterization
Viewed from client side or server side
Fundamental types of consistency (server side):
Strong – Perpetual consistency, immediate consistency
Optimistic – Implements values locally, then propagates values remotely. Conflicts resolved using revision histories
Causal – Updates first at nodes with causal relationship to datum, uses eventual consistency for non-causal members.
Sequential – Similar to strong, specifies a strict order of instructions to be propagated.
Cache – The value read is the most recent value stored on the local or nearest system.
9/22/2018
CS 455: Introduction to distributed systems Dept. of computer science, Colorado state university

4. Trade-off Space for Solutions in Consistency
CAP Theorem and PACELC extension
CAP: Designers must choose between two of the three properties
PACELC: evaluates tradeoff space depending on system operation (normal or partitioned)
Consistency vs. Availability
Increasing consistency reduces availability by introducing more latency in the system
Consistency vs. Scalability
Some consistency models require more memory or messages as the system scales up, hindering scalability
9/22/2018
CS 455: Introduction to distributed systems Dept. of computer science, Colorado state university

5. Dominant Approaches to Consistency
Approaches vary by system type
Paxos
Server to client distributed systems
Servers can fail and restart, must reload information
Node types: Acceptors, Proposers, Learners
Issue: Live lock problem
General Distributed Shared Memory (DSM) Algorithm
Sequential, causal, cache models available
User selects which to use
For each process, all writes in a cycle are written to one message to minimize network load
Issues: scalability and fault tolerance
9/22/2018
CS 455: Introduction to distributed systems Dept. of computer science, Colorado state university

6. Insights Gleaned There is no single general solution
PAXOS – server to client interaction
The trade-offs require development of creative solutions for each application
The modern network-collaboration tools rely heavily on consistency
The accuracy, reliability, and scalability of these services can be seen in the effect they have had on contemporary culture, politics, and economics
Virtual communities – Reddit, Facebook
Disaster response – Google Person Finder
Public mobilization – Twitter, FireChat
9/22/2018
CS 455: Introduction to distributed systems Dept. of computer science, Colorado state university

7. The Problem Space in the Future
The future of consistency will depends on advances in . . .
Distributed Systems
Clock synchronization, scalability, reduced network overhead, and in-memory processing
Computer Architecture
Faster CPU or IO speeds and cheaper memory
The use of distributed systems
Current Trend: “put everything on the cloud”
Possible directions
Mainstream use: home computer systems, computer stick, etc.
Gaming: multiplayer online virtual reality games (Samsung Oculus Rift)
9/22/2018
CS 455: Introduction to distributed systems Dept. of computer science, Colorado state university

8. Trade-off Space and Solutions in the Future
Similar to current trade-off space
Scalability and Fault Tolerance
Low Latency
Solutions
Flexible “out of the box” algorithms:
Allow users to choose model for system
Only include beneficial models for that system
Achieving consistency in some areas will be easier, some will be harder
Distributed home computer system = easy
Virtual Reality Multiplayer Online gaming = difficult
9/22/2018
CS 455: Introduction to distributed systems Dept. of computer science, Colorado state university

9. Conclusion There will not be single general solution for all systems
CAP/PACELC Theorem
Developers must determine solution per system based on trade-offs
System Requirements
Consistency Models
Weaker consistency models tend to be the dominant approach
Provide more flexibility with system implementation
OVERALL: CAP will always dictate solutions in distributed system consistency
9/22/2018
CS 455: Introduction to distributed systems Dept. of computer science, Colorado state university

10. Questions?
9/22/2018
CS 455: Introduction to distributed systems Dept. of computer science, Colorado state university