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Adaptive Overload Control for Busy Internet Servers

Matt Welsh and David CullerUSITS 2003

Presented by: Bhuvan Urgaonkar

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Internet Services Today Massive concurrency demands

Yahoo: 1.2 billion+ pageviews/day AOL web caches: 10 billion hits/day

Load spikes are inevitable Peak load is orders of magnitude greater than average Traffic on September 11, 2001 overloaded many news sites Load spikes occur exactly when the service is most

valuable!• In this regime, overprovisioning is infeasible

Increasingly dynamic Days of the “static” web are over Majority of services based on dynamic content

• E-commerce, stock trading, driving directions etc.

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Problem Statement Supporting massive concurrency is hard

Threads/processes don’t scale very well

Static resource containment is inflexible How to set a priori resource limits for widely varying

loads? Load management demands a feedback loop

Replication alone does not solve the load management problem Individual nodes may still face huge variations in

demand

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Proposal: The Staged Event-Driven Architecture

SEDA: A new architecture for Internet services A general-purpose framework for high concurrency and load

conditioning Decomposes applications into stages separated by queues

Enable load conditioning Event queues allow inspection of request streams Can perform prioritization or filtering during heavy load Apply control for graceful degradation

• Perform load shedding or degrade service under overload

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Staged Event-Driven Architecture

Decompose service into stages separated by queues Each stage performs a subset of request processing Stages internally event-driven, typically nonblocking Queues introduce execution boundary for isolation

Each stage contains a thread pool to drive stage execution Dynamic control grows/shrinks thread pools with demand

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Per-stage admission control

Admission control done at each stage Failure to enqueue a request => backpressure on

preceding stages Application has flexibility to respond as appropriate Less conservative than single AC

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Response time controller

90th percentile response time over some interval passed to the controller

AIMD heuristic used to determine token bucket rate Exact scheduling mechanisms unspecified Future work: Automatic tuning of parameters

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Overload management Class based differentiation

Segragate request processing for each class into its own set of stages

Or, have a common set of stages but make the admission controller aware of the classes

Service degradation SEDA signals occurrence of overload to applications If application wants it may degrade service

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Arashi: A SEDA-based email service

A web-based email serviceo Managing folders, deleting/refiling mails, search etc

Client workload emulates several simultaneous users, user behavior derived from traces of the UCB CS IMAP server

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Controller operation

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Overload control with increased user load

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Increased user load (contd)

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Overload control under a massive load spike

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Per-stage AC Vs Single AC

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Advantages of SEDA Exposure of the request stream

Request level performance made available to application

Focused, application-specific admission control Fine-grained admission control at each stage Application can provide own admission control policy

Modularity and performance isolation Inter-stage communication via event passing enables

code modularity

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Shortcomings Biggest shortcoming: Heuristic based

May work for some applications, fail for others Not completely self-managed

Response time targets supplied by administrator Controller parameters set manually

Limited to apps based on the SEDA approach Evaluation of overheads missing Exact scheduling mechanisms missing

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Some thoughts/directions… Formal ways to reason about the goodness of

resource management policies Also, the distinction between transient and

drastic/persistent overloads

Policy issues: Revenue maximization and predictable application performance Designing Service Level Agreements Mechanisms to implement them

Application modeling and workload prediction

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Overload control: a big picture

Unavoidable overload

Avoidable overload

Underload

Detection of overloads Formal and rigorous ways of defining the goodness of “self-managing” techniques UO and AO involve different actions (e.g. admission control versus reallocation). Are they fundamentally different?

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Knowing where you are! Distinguish avoidable overloads from

unavoidable overloads Need accurate application models, workload predictors

Challenges: multi-tiered applications, multiple resources, dynamically changing appl behavior

Simple models based on networks of queues? How good would they prove?

Resource allocations Workload(predicted)

Performance Goal

MODEL

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Workload prediction: a simple example

A static application model Find cpu and nw usage distributions by offline

profiling Use the 99th percentiles as cpu, nw requirements

When the application runs “for real” We don’t get to see what the tail would have been So … resort to some prediction techniques E.g., a web server:

• record # requests N• record # requests serviced M• extrapolate to predict the cpu, nw requirements

of N requests

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Service-level agreements We may want…

Workload Response time w1 r1 w2 r2

… wN rN

Is this possible to achieve? Maybe not. How about:

Response time Revenue/requestr1 $$1

…rN $$N