Common Misconceptions About Distributed Algorithms

A distributed algorithm is a method or set of instructions that is carried out by multiple computers across a network to accomplish a shared goal. Unlike traditional algorithms that run on a single machine, distributed algorithms need to coordinate the actions and state of independent processes across potentially unreliable networks.

Designing robust distributed algorithms presents unique challenges compared to centralized systems. Network issues like unstable connections, timing delays, and partial failures are common. The distributed nature also introduces coordination overhead and complexity in synchronizing events across distance and time.

These challenges lead to misconceptions about the capabilities and limitations of distributed systems. Common pitfalls include underestimating the impact of unreliable networks, ignoring coordination costs, not handling asynchrony properly, failing to accommodate changing network topologies, and lacking fault tolerance.

Common Pitfalls when Designing Distributed Algorithms

Assuming Reliable Networks

An easy trap when designing distributed algorithms is assuming the network connecting the processes will be reliable with stable latency, no faults, and guaranteed message delivery. However these assumptions rarely hold in real-world deployments leading to fragile systems.

Packet loss, variable delays, partitions, throttling, congestion, queueing, and other transient network issues are unavoidable. Temporary computer or software crashes also routinely occur, preventing node coordination when critical messages are missed.

By explicitly handling cases where messages are delayed or lost and providing redundancy, distributed algorithms can degrade gracefully and avoid catastrophic failures stemming from faulty base assumptions.

Ignoring Coordination Overhead

Coordinating the actions of distributed components inherently incurs overhead from messaging delays and reconciliation logic. Sending state updates to remote processes takes time depending on network distance and conditions. Additional processing may be needed to handle versioning, duplicate messages, and disagreement.

Failing to properly factor in this coordination overhead leads to slower than expected performance, congestion when demand increases, and issues under contention. Systems may work during small tests but struggle to scale in production.

Carefully evaluating expected coordination costs under load and designing with efficiency in mind is key to preventing unwanted surprises. Proper benchmarking, load testing, and realistic simulations are also important.

Not Accounting for Timing Issues

Timing inconsistencies easily emerge between distant processes due to factors like clock skew, irregular message delays, and differences in execution speed. These asynchrony issues grow more pronounced in global deployments crossing data centers.

Lacking robust logic to reconcile out-of-order and delayed messages will result in algorithms that seem to work most of the time but fail unpredictably. Common symptoms include stalls under contention, inconsistent state, repeated actions, and race conditions.

Distributed systems should make few timing assumptions, idempotently handle stale or redundant data, include logic to resynchronize dangling state, and avoid relying on perfect event ordering guarantees.

Handling Changing Topologies

Most distributed algorithms and consistency protocols logically arrange nodes in well-defined network topologies to coordinate processing and communication. Example arrangements include leader-follower, rings, trees, meshes, and gossip clusters.

However, real infrastructure continuously evolves with nodes joining and leaving. Connections also form dynamically based on location, identities, attributes, load, and cost. Topology stability can never be taken for granted.

Mature distributed systems include discovery mechanisms to map current network organization and participants. Node roles, neighbors lists, and communication paths automatically recalibrate as resources come and go to prevent fragmentation.

Dealing with Failures

Node crashes, software hangs, hardware faults, operator errors, and other failures happen regularly at scale and must be handled. Unlike monolithic applications where process death is usually fatal, distributed systems can continue functioning despite localized crashes.

But first, algorithms must include failure detection to identify unresponsive neighbors and trigger replacement election routines. State and responsibilities from the failed node must also shift seamlessly to available backups.

By architecting distributed computation across redundant and interchangeable resources, system health remains high despite ongoing component attrition as nodes cycle in, fail, and revive.

Examples of Common Distributed Algorithms

Leader Election

Leader election algorithms allow collections of peer nodes with equivalent roles to automatically nominate a single leader or primary. Tasks that benefit from a stable coordinator like job scheduling/dispatching, locking arbitration, message routing, and state machine replication leverage elected leaders.

Nodes run local leader election code when networks initialize and whenever an existing leader leaves. Typical election techniques involve priority queue sorting, heartbeat-based liveness tracking, and node ID comparisons to safely select a new stable leader.

Consensus Protocols

Consensus systems allow groups of distributed processes to agree on values like application state or the sequencing of commanded actions. Keeping dispersed nodes precisely in sync is challenging due to misordered messaging and various faults.

Common distributed consensus algorithms include Paxos, Raft, Viewstamped Replication, Zab, and various incrementally updated quorum techniques. These leverage voting, logical timeouts, and replicated request/commit logs to achieve coordination.

Distributed Mutual Exclusion

Mutual exclusion locks prevent simultaneous access to shared resources. In distributed environments, traditional locking risks deadlock and starvation. Algorithms like Maekawa voting and lock server coordinators allow processes to take turns safely accessing globally shared data.

Token passing schemes also work by assigning a single ephemeral key for entry serialization. Lamport ordering uses logical timestamps to totally order distributed events and derive a global lock access sequence.

Gossip Protocols

Gossip algorithms provide a light-weight approach for sharing state and messages among cloud scale collections of nodes. Each node routinely selects random peer targets to exchange available data.

Knowledge spreads epidemically through the cluster as transmitted information reaches additional nodes with each round of gossip. Useful for publishing presence, disseminating jobs, aggregating monitoring data, and database eventually consistent replication.

Design Principles for Robust Distributed Algorithms

Embrace Unreliability

Rather than vainly fight against unavoidable network glitches, latency variation, hardware faults, and unreliable infrastructure, adaptive algorithms plan for trouble and degrade gracefully. Adding slack, redundancy, error correction, and randomized retry logic inoculates against real world uncertainty.

Minimize Coordination

Excessive synchronization coupling risks magnifying and propagating local disturbances into global cascading failures. Loosely coupling distributed components allows faster isolated responses while maintaining overall system health. Prioritizing asynchronous autonomous actions minimizes coordination points.

Localize Information

Algorithms reliant on global knowledge perform poorly at scale and raise availability concerns. With compute logically distributed, data should follow placement. Nodes make decisions primarily using nearby state snapshots. Sparing access to distant aggregates prevents hotspotting and partitions.

Adapt to Changing Conditions

Distributed systems exist within turbulent operating contexts including shifting topologies, load balancing, failover, recovering nodes, infrastructure migrations, capacity adjustments, software updates, and configuration drift.

Adaptive self-stabilizing protocols with gossip-style information diffusion automatically recalibrate to current network organization and participants. This allows online cluster reconfiguration minus costly pauses for manual restarts.

Testing and Debugging Distributed Systems

Logging and Instrumentation

Rich metrics expose overall system health and performance trends while detailed application logs capture lower level operation. Timestamping entries allows correlation between dispersed event streams. Tracing distributed request flows is also invaluable.

Common logging solutions like Splunk, Elasticsearch, Kafka, and Datadog index streamed data allowing complex historical analysis. Teams build custom charts and dashboards to surface key aggregates for monitoring.

Simulations and Testbeds

Simulated environments emulate larger cluster sizes by replicating processes inside containers or virtual machines. Controlled lab deployments stress test failure scenarios like network delays, unexpected restarts, node termination, and various faults.

Chaos engineering tooling deliberately injects failures into production to validate recovery procedures. Short-lived perturbations uncover weaknesses and training automation before dangerous true failures.

Formal Verification

Mathematically proving algorithm correctness using formal methods remains rare but gains traction for mission critical apps. Theorem provers apply symbolic models and logical deductive reasoning to strictly check safety claims and liveness guarantees.

Research initiatives also look to better catalog distributed system failure modes to enable exhaustive verification. As algorithms grow more complex, formal proofs provide confidence in sound designs.

Future Directions

Self-Stabilization

Self-stabilization research focuses on designing special classes of distributed algorithms able to automatically converge safely to legitimate state despite initializing in arbitrary system configurations. This offers robustness by safely recovering from unsafe transitory conditions.

Biology-Inspired Designs

Biological systems like ant colonies, the brain, bacterial films, embryogenesis, natural swarms, the immune system, and fungal networks have solved massively distributed coordination challenges. By translating organic strategies into software algorithms, designs can better scale resiliently.

Quantum Algorithms

Emerging quantum computing hardware promises breakthroughs in modeling nature, machine learning data analysis, search and optimization, number theory, and secure communications. Distributed quantum algorithms are also active research areas as the technology matures.