The Time-Critical Datacenter Migrating time-critical applications to commodity datacenters… … conversely, providing datacenter web- services with time-critical performance.
Whats a Time-Critical System? Not real time, but real fast! Financial calculators, military command and control… air traffic control (ATC) … foobooks.com! Technology Gap: Real-Time focuses on determinism, scale-up architectures
The French ATC System Mid to Late 90s Teams of 3-5 air traffic controllers on a cluster of desktop consoles of these console clusters in an air traffic control center Why study the French ATC?
ATC Subsystems Radar Image Weather Alert Track Updates Updates to Flight Plans Console to Console State Updates System Management and Monitoring ATC center to center Updates Multicast ubiquitous…
Two Kinds of Multicast Virtually Synchronous Multicast: very reliable, not particularly fast Unreliable Multicast: very fast, not particularly reliable Nothing in between!
Multicast in the French ATC Engineering Lessons: Structure application to tolerate partial failures Exploit natural hardware properties Can we generalize to modern systems? Research Direction: Time-Critical Reliability Can we design communication primitives that encapsulate these lessons?
Anatomy of a Cloned Service
Services An Amazon web-page is constructed by 100s of co-operating services* Multicast is used for: Updating Cloned Services Publish-Subscribe / Eventing Datacenter Management/Monitoring * Werner Vogels, CTO of amazon.com, at SOSP 2005
Multicast in the Datacenter A node is in many multicast groups: One for each service it hosts One for each topic it subscribes to One or more administration groups Large Numbers of Overlapping Groups!
Service Semantics Data Store Services: stale data can result in overselling / underselling loss of real- world dollars Cache Services: updated periodically by back-end data-stores
The Challenge Datacenter Blades are failure-prone: Crash failures Byzantine behavior Bursty Packet Loss : End-hosts kernels drop packets when subjected to traffic spikes.
A New Reliability Model Rapid delivery is more important than perfect reliability Probabilistic Timeliness Graceful Degradation
Wanted: a multicast primitive that 1. Scales to large numbers of arbitrarily overlapping multicast groups 2. Delivers multicasts quickly 3. Tolerates datacenter failure modes – bursty packet loss, node failures 4. Offers probabilistic properties 5. Gives up on lost data after a threshold period
Ricochet: Lateral Error Correction Receivers exchange error correction XORs of multicast traffic Works very well with multiple groups – scales upto a thousand groups per node Probabilistic Timeliness: probability distribution of delivery latencies
Predictive Total Ordering (Plato) Delivers messages to applications with no ordering delay in most cases Orders messages only if there is a high probability of out-of-order delivery across different nodes Probabilistic Timeliness: probability distribution of ordered delivery latency
Performance SRM takes seconds to recover lost packets Ricochet recovers almost all packets within ~70 milliseconds
Conclusion Move from R/T to T/C yields huge benefits! Ricochet is faster… slashes latency… scalable… Clean delivery delay curve a powerful design tool, replaced traditional hard (but conservative) limits Were open for business: Software and detailed paper available for download Give it a try… tell us what you think!