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Multi-Class Latency Bounded Web Services. Vikram Kanodia and Edward Knightly Rice Networks Group http://www.ece.rice.edu/networks. Motivation. Poor end-to-end performance of web traffic. Excessive latencies due to overloaded servers a dominant factor.

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multi class latency bounded web services

Multi-Class Latency Bounded Web Services

Vikram Kanodia and Edward Knightly

Rice Networks Group

http://www.ece.rice.edu/networks

motivation
Motivation
  • Poor end-to-end performance of web traffic.
    • Excessive latencies due to overloaded servers a dominant factor.
  • Present day web servers provide only FCFS.
  • Need Mechanisms to:
    • Reduce server latency; and
    • Control server latency.
steps towards web qos 1
Steps Towards Web QoS - 1
  • SBAC – Session Based Admission Control [CherkPhaal99].
    • Blocks sessions if load above a certain threshold.
  • Pros:
    • Prevents server from going into overload.
  • Cons:
    • Only ensures better service to all admitted requests.
    • Cannot ensure that requested service is met.
steps towards web qos 2
Steps Towards Web QoS - 2
  • Operating system hooks:
    • Mechanisms to support resource reservation among different domains at OS level.
      • Resource Containers [BangDrsch99].
      • Eclipse/BSD operating system [Silber99].
  • Prioritizing incoming requests provides class differentiation [BhattiFried99].
  • Distributed server architecture for better throughput [Vpai98].
what is lacking
What is Lacking ?
  • No mechanism to meet a requests’ targeted delay.
  • No class based service model:
    • Multiple user classes.
    • Each class has a different response time target.
    • All classes contending for the same resource.
  • No means of statistically quantifying the service received .
key challenges
Key Challenges
  • Net service rate is a complex, unknown function of CPU / disk/ cache behavior.
  • Very difficult to model a requests’ service demand in terms of low level system resources.
  • Interaction between requests belonging to different classes difficult to predict a priori.
  • All present day web QoS schemes coupled tightly with server architecture .
first cut baseline scheme
First Cut: Baseline Scheme
  • Latency targeted service model:
    • Single user class with a targeted delay to be met by some percentage of all serviced requests.
  • Goals:
    • Illustrate an abstraction of the server resources into a simple queuing model.
    • Highlight key issues for managing multi-class web services.
    • Use for experimental comparisons.
baseline scheme problem formulation
Baseline Scheme: Problem formulation
  • Assumption: Stationary and homogeneous arrivals.
  • Some maximum service rate which satisfies QoS requirements.
    • All arrival greater than the maximum service rate need to be be blocked.
  • How to determine the maximum service rate ?
baseline scheme m m 1 model
Baseline Scheme: M/M/1 model
  • Approximate a class’ service by an M/M/1 queue with an unknown service rate.
    • Abstracts the low level server resources into a virtual server.
  • Unknown Service rate is given by:
baseline scheme admission control
Baseline Scheme: Admission Control
  • A new request leads to an increase in load to l’.
    • Delay violation probability under load l’:
  • If P( D > d*) is greater than the targeted fraction of requests meeting the delay target , block the new request.
limitations of baseline scheme
Limitations of Baseline Scheme
  • No support for multiple service classes
    • M/M/1 models each class as independent of other classes.
      • Cannot capture inter class interference.
  • Assumption of independent and exponentially distributed service times is faulty.
    • Does not account for highly variable service time.
    • Ignores temporal correlation among different requests for the same document.
solution
Solution
  • LMAC : Latency Targeted Multi-Class Admission Control
  • Service model:
    • A minimum fraction of accepted requests will be serviced within the class delay target.
    • Mechanism to characterize and control inter-class relationships.
    • Decouples access control from actual server. architecture or the operating system.
our technique envelopes
Our Technique: Envelopes
  • Envelopes: arrival/service rates over intervals of time.
  • Deterministic [Cruz95] and statistical [QK99,CK00] envelopes are used to manage network QoS.
  • Envelopes represent net service received in the presence of other concurrent requests being processed by the server at the same time.
what do envelopes buy us
What do Envelopes Buy Us ?
  • A general yet accurate way of describing a class’ service and demand.
  • A higher level of abstraction of low level system resources.
  • Capture effects of temporal correlation and high variability in requests and server latencies.
  • Model relationship among different user classes in a tractable manner.
measured based service envelope
Measured Based Service Envelope
  • Envelope is service received versus interval length when backlogged.
  • Given the number of concurrently backlogged requests:
    • Compute the request latency mean and variance.
    • Use gaussian approximation to get the targeted percentile delay.
lmac algorithm
LMAC Algorithm
  • Ensure that a arrival maintains the latency target of its own class
    • Maintain a maximum horizontal distance between the requests and service envelopes less than the targeted latency.
  • How to ensure that the service of other classes is not disrupted ?
lmac algorithm cont
LMAC Algorithm (cont.)
  • To ensure that other classes do not suffer:
    • Assume that the new arrival has strict priority over all other requests.
      • This is a worst case assumption.
    • For all other classes, the request workload remains the same, but there is a reduction in service.
simulation details
Simulation Details
  • Simulations performed using a simulator which approximates the behavior of OS management for CPU, disk, caching etc.
  • Use a trace generated from the CS departmental server logs at Rice University.
  • Assume arrival rate is poisson with a given mean rate.
experiment 1
Experiment 1
  • Targeted delay of 1 second for 95 percentile of all admitted requests.
  • Demonstrates overload protection properties similar to SBAC.
experiment 2
Experiment 2
  • Single class-single node case.
  • Baseline scheme does meet its delay target, but is too conservative.
multi class performance
Multi-Class Performance
  • In the absence of any server level support :
    • Performance of each class bounded by the most stringent class.
  • To investigate a true multi-class scenario:
    • Devise an artificial resource allocation policy.
experiment 3 cont
Experiment 3 (cont.)
  • Class A:
    • Arrival rate 300 reqs/sec, target delay .5 sec
  • Class B:
    • Arrival rate 200 reqs/sec, target delay 1 sec
conclusions
Conclusions
  • Scheme to ensure that a minimum fraction of all accepted requests meet latency targets.
  • A way to model system resources into a high level server:
    • Makes our approach general and independent of OS/ server architecture.
    • Ability to exploit additional features within the server architecture for higher utilization.
future work
Future Work
  • Address Heterogeneous Content
    • Content with different service demands , e.g dynamic content.
  • Perform experiments with additional traces.
  • Incorporate LMAC into a real server and test its performance.