Fair%20Scheduling%20in%20Web%20Servers

1
Fair Scheduling in Web Servers

• CS 213 Lecture 17
• L.N. Bhuyan

2
Objective

• Create an arbitrary number of service quality
  classes and assign a priority weight for each
  class.
• Provide service differentiation for different use
  classes in terms of the allocation of CPU and
  disk I/O capacities

3
(No Transcript)
4
(No Transcript)
5
(No Transcript)
6
(No Transcript)
7
(No Transcript)
8
(No Transcript)
9
(No Transcript)
10
(No Transcript)
11
(No Transcript)
12
(No Transcript)
13
(No Transcript)
14
(No Transcript)
15
(No Transcript)
16
(No Transcript)
17
(No Transcript)
18
(No Transcript)
19
(No Transcript)
20
(No Transcript)
21
(No Transcript)
22
Differentiated Service in a Web Cluster Objective

• Create an arbitrary number of service quality
  classes and assign a priority weight for each
  class.
• Provide service differentiation for different use
  classes in terms of the allocation of CPU and
  disk I/O capacities
• Ref Demand Driven Service Differentiation in
  Cluster-based Network Servers, Infocom 2001, by
  Zhu, Tang and Yang

23
Target System
24
Service Differentiation

• Requests of higher classes receive better
  services than lower classes, especially when the
  system is heavily loaded
• Request from lower classes should not be
  sacrificed for requests from higher classes when
  the system load is light

25
Definitions

• Requests C1, C2, , CN
• Corresponding Weights W1, W2, , WN
• Stretch factors S1, S2, , SN
• stretch factor the ratio of the response time
  of a request to the service demand of that
  request
• Average arrive rate ?i
• Average processing rate µi
• Minimum resource requirement of class I, ?i ?i
  /µi

26
Optimal Problem

• Minimize
• F W1S1 W2S2 WNSN
• such that S1 S2 SN
• S1, S2, , SN K
• K is a stretch factor bound, where K gt 1 is
  a predefined threshold

27
Optimal Solution
28
Scheduling optimization for Resource-Intensive
Web requests

• In SPAA99
• By Zhu, Smith and Yang

29
Request Classification

• Static Data
• web pages, images, etc.
• does not consume much system resource

30
Request Classification (Cont.)

• Dynamic Data
• e-commerce, database searching, personalized
  information
• generated dynamically, place greater I/O and
  CPU demands
• 1 to 2 orders of magnitude longer processing
  time than static requests based on IBM Olympics
  and Alexandria Digital Library data

31
Flat Architecture

• Server nodes can process both static and dynamic
  requests

32
Master/Slave Architecture

• Server nodes are divided in two groups
• Slave group only processes dynamic requests
• Master group can handles both requests

33
How to partition a cluster

• Questions
• 1 Given p nodes, what is the optimal number of
  master nodes?
• 2 What percentage of dynamic requests should
  be processed on masters?
• Goal ensure the master/slave
• architecture outperforms flat
• architecture

34
M/S and Flat Models
35
Performance Metric

• Stretch factor the ratio of response time at a
  particular load to that at no load
• Average stretch factor is more suited than
  average response time for systems with highly
  variable task sizes. Average stretch factor
  indicates load of a system.

36
Evaluation results

• M/S up to 69 performance improvement over flat
• Separation of dynamic and static content
• Resource reservation up to 68 improvement
• Resource requirement sampling up to 23
  improvement

37
Performance Guarantees for Internet Services
(Gage)

• Environment Web hosting services
• multiple logical web servers (service
  subscriber) on a single physical web server
  cluster.
• Gage
• guarantee each web server with a pre specific
  performance
• a distinct number of URL requests to service
  per second

38
Components

• Each service subscriber maintain a queue
• Request classification
• determines the queue for each input request
• Request scheduling
• determines which queue to serve next to meet the
  QoS requirement for each subscriber.
• Resource usage accounting
• capture detailed resource usage associated with
  each subscribers service requests.

39
(No Transcript)
40
The Gage System

• QoS guarantee
• QoS is in terms of a fixed number of generic
  URL request which represents an average web site
  access
• Currently, assuming it is 10msec of CPU
  time, 10msec of disk I/O and 2000 bytes of
  network bandwidth
• Each subscribe is given a fixed number of
  generic requests.
• Other possible QoS metrics response time,
  delay jitter etc.
• Using TCP splicing

41
(No Transcript)
42
Request Scheduling

• Two decisions
• Which request should be serviced next
• according to each subscribers static resource
  reservation and dynamic resource usage
• Which RPN should service this request
• according to the load information on each RPN
  and also exploit access locality