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Multi-Class Latency Bounded Web Services Vikram Kanodia and Edward Knightly Rice Networks Group

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Presentation on theme: "Multi-Class Latency Bounded Web Services Vikram Kanodia and Edward Knightly Rice Networks Group"— Presentation transcript:

1 Multi-Class Latency Bounded Web Services Vikram Kanodia and Edward Knightly Rice Networks Group

2 Vikram Kanodia2 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.

3 Vikram Kanodia3 Web Hosting Example

4 Vikram Kanodia4 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.

5 Vikram Kanodia5 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].

6 Vikram Kanodia6 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.

7 Vikram Kanodia7 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.

8 Vikram Kanodia8 System Model

9 Vikram Kanodia9 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.

10 Vikram Kanodia10 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 ?

11 Vikram Kanodia11 Model for Baseline Scheme

12 Vikram Kanodia12 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:

13 Vikram Kanodia13 Baseline Scheme: Admission Control A new request leads to an increase in load to. Delay violation probability under load : If P( D > d*) is greater than the targeted fraction of requests meeting the delay target, block the new request.

14 Vikram Kanodia14 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.

15 Vikram Kanodia15 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.

16 Vikram Kanodia16 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.

17 Vikram Kanodia17 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.

18 Vikram Kanodia18 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.

19 Vikram Kanodia19 Model for LMAC

20 Vikram Kanodia20 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 ?

21 Vikram Kanodia21 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.

22 Vikram Kanodia22 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.

23 Vikram Kanodia23 Experiment 1 Targeted delay of 1 second for 95 percentile of all admitted requests. Demonstrates overload protection properties similar to SBAC.

24 Vikram Kanodia24 Experiment 2 Single class-single node case. Baseline scheme does meet its delay target, but is too conservative.

25 Vikram Kanodia25 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.

26 Vikram Kanodia26 Experiment 3: Setup

27 Vikram Kanodia27 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 ClassIsolationMulti-class with Sharing Throughput (reqs/sec) Delay (sec) Throughput (reqs/sec) Delay (sec) A147.467141.501 B92.912145.935

28 Vikram Kanodia28 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.

29 Vikram Kanodia29 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.

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