Presentation on theme: "Large-Scale Distributed Systems Andrew Whitaker CSE451."— Presentation transcript:
Large-Scale Distributed Systems Andrew Whitaker CSE451
Textbook Definition “A distributed system is a collection of loosely coupled processors interconnected by a communication network” Typically, the nodes run software to create an application/service e.g., 1000s of Google nodes work together to build a search engine
Why Not to Build a Distributed System (1) Must handle partial failures System must stay up, even when individual components fail Amazon.com
Why Not to Build a Distributed System (2) No global state Machines can only communicate with messages This makes it difficult to agree on anything “What time is it?” “Which happened first, A or B?” Theory: consensus is slow and doesn’t work in the presence of failure So, we try to avoid needing to agree in the first place A B
Reasons to Build a Distributed System (1) The application or service is inherently distributed Andrew Whitaker Joan Whitaker
Reason to Build a Distributed System (2) Application requirements Must scale to millions of requests / sec Must be available despite component failures This is why Amazon, Google, Ebay, etc. are all large distributed systems
Internet Service Requirements Basic goal: build a site that satisfies every user requests Detailed requirements: Handle billions of transactions per day Be available 24/7 Handle load spikes that are 10x normal capacity Do it with a random selection of mismatched hardware
An Overview of HotMail (Jim Gray) ~7,000 servers 100 backend stores with 300TB (cooked) Many data centers Links to Internet Mail gateways Ad-rotator Passport ~ 5 B messages per day 350M mailboxes, 250M active ~1M new per day. New software every 3 months (small changes weekly).
Availability Strategy #1: Perfect Hardware Pay extra $$$ for components that do not fail People have tried this “fault tolerant computing” This isn’t practical for Amazon / Google: It’s impossible to get rid of all faults Software and administrative errors still exist
Availability Strategy #2: Over- provision Step 1: buy enough hardware to handle your workload Step 2: buy more hardware Replicate
Benefits of Replication Scalability Guards against hardware failures Guards against software failures (bugs)
Replication Meets Probability p is probability that a single machine fails Probability of N failures is: 1-p^n Site unavailability
Availability in the Real World Phone network: 5 9’s % available ATMs: 4 9’s 99.99% available What about Internet services? Not very good…
2006: typical 97.48% Availability 97.48% Source: Jim Gray
What Gives? Why isn’t simple redundancy enough to give very high availability?
Failure Modes Fail-stop failure: A component fails by stopping It’s totally dead: doesn’t respond to input or output Ideally, this happens fast Like a light-bulb Byzantine failure: Component fails in an arbitrary way Produces unpredictable output
Byzantine Generals Basic goal: reach consensus in the presence of arbitrary failures Results: More than 2/3 of the nodes must be “loyal” 3t + 1 nodes with t traitors Consensus is possible, but expensive Lot’s of messages Many rounds of communication In practice, people assume that failures are fail- stop, and hope for the best…
Example of a non Fail-Stop Failure Server Load balancer Internet Load Balancer uses a “Least Connections” policy Server fails by returning an HTTP error 400 Net result: “failed” server becomes a black hole Amazon.com
Correlated Failures In practice, components often fail at the same time Natural disasters Security vulnerabilities Correlated manufacturing defects Human error…
Human error Human operator error is the leading cause of dependability problems in many domains Source: D. Patterson et al. Recovery Oriented Computing (ROC): Motivation, Definition, Techniques, and Case Studies, UC Berkeley Technical Report UCB//CSD , March Public Switched Telephone Network Average of 3 Internet Sites Sources of Failure
Understanding Human Error Administrator actions tend to involve many nodes at once: Upgrade from Apache 1.3 to Apache 2.0 Change the root DNS server Network / router misconfiguration This can lead to (highly) correlated failures
Learning to Live with Failures If we can’t prevent failures outright, how can we make their impact less severe? Understanding availability: MTTF: Mean-time-to-failure MTTR: Mean-time-to-repair Availability = MTTR / (MTTR + MTTF) Approximately MTTR / MTTF Note: recovery time is just as important as failure time!
Summary Large distributed systems are built from many flaky components Key challenge: don’t let component failures become system failures Basic approach: throw lots of hardware at the problem; hope everything doesn’t fail at once Try to decouple failures Try to avoid single points-of-failure Try to fail fast Availability is affected as much by recovery time as by error frequency