Masking vs. Tolerating Failures in Distributed Systems

Below are the differences between Masking and Tolerating Failures in Distributed Systems:

Aspect	Failure Masking	Failure Tolerance
Visibility of Failures	Hidden from users and system components	Failures may be visible but are managed
System Design	Relies on redundancy and replication	Focuses on robustness and recovery mechanisms
User Experience	Aims for uninterrupted user experience	Accepts possible degradation in performance or functionality
Techniques	– Replication – Load Balancing – Checkpointing and Rollback	– Error Detection and Correction – Graceful Degradation – Redundancy and Failover
Examples	Distributed databases (e.g., Google Spanner) – Telecommunications networks	– RAID storage systems – E-commerce websites (e.g., Amazon) – Distributed computing (e.g., Hadoop)
Use Cases	– Financial systems (e.g., online banking) – Telecommunications	– E-commerce websites – Distributed computing systems

What is the Difference Between Masking and Tolerating Failures in Distributed Systems?

In distributed systems, dealing with failures is a critical aspect of design and implementation. Since these systems consist of multiple interconnected components, the likelihood of failures increases. Two primary approaches to handling these failures are masking and tolerating them. This article explores the differences between these approaches, their techniques, and their use cases.

Important Topics to Understand the difference Between Masking and Tolerating Failures