measurement-based server selection within the application-layer anycasting architecture mostafa h....
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Measurement-Based Server Selection within the Application-Layer
Anycasting Architecture
Mostafa H. AmmarCollege of Computing
Georgia Institute of TechnologyAtlanta, GA
Contributors
Samrat BhattacharjeeZongming FeiEllen Zegura
Server Replication
Improves service scalability
Server Selection ProblemHow does a client determine which
of the replicated servers to access
Interested in Wide-Area Replication
Server Selection Alternatives
Designated Server (e.g., nearest)
Round robin assignment (e.g., DNS rotator)
Explicit list with user selection
Server-cluster techniques (Netdispatcher, Local Director)
Other Interesting Work
DSS -- BUSPAND -- BerkeleyMirror Characterization -- CMUIDMaps -- UMich, UCLA et al ...
Anycasting
Network-Layer Anycasting in RFC 1541 Anycast IP addresses Network-layer metrics Per-packet selection
Application-Layer Anycasting
Group of servers identified by Anycast Name
Clients request service from group identified by name
Automatic connection to a “good” server
An Architecture
Resolver
Orange Server Group
Green Server Group
Green Service?
Go to server y
Server y
Resolver
“Close” to clientMaintains
Anycast group membership Selection-enabling information
Client may provide filter that tells resolver how to select
DNS-like hierarchy of resolvers
Web Server Selection
An instantiation of architectureCriterion: Best Response Time
[client request, last byte received] includes path and server delays
Problem: Maintaining response time estimate
for each server in anycast group at resolver
Response Time Estimation Alternatives
ProbePushUser-Experience
Overview of Approach
Resolver probes for path-dependent response time (RT)
Server measures and pushes path-independent processing time (TUFR)
Lighter-weight push more frequent than heavier-weight probe
Probe result used to calibrate pushed valueOscillation prevention measures
Resolvers Probe for RT and Associated TUFR
Resolver
Orange Server Group
Green Server Group
SF = RT/TUFR
RT &TUFR
Probe for well-known representative “dummy”file maintained by server.
TUFR written in file by server
Servers Push TUFR
Resolver
Orange Server Group
Green Server Group
RT =SF x TUFR
TUFR
Resolver and Server Interaction
Content Server
Push Daemon
Resolver Probe
Anycast Resolver
Server Resolver
PerformanceUpdates
Probes
ServerPushes
ProbeUpdates
Server Push Process
Typical server response cycleassign process to handle queryparse querylocate requested filerepeat until file is written read from file write to network
Measure and smooth time until first read (TUFR)
Push if significant change
Resolver Probe Process
Request dummy file from server
Measure response time
Hybrid Push/Probe Technique
Resolver: request dummy file from serverMeasure response time (RT)Dummy file contains most recent TUFREach probe: compute scaling factor SF = RT/TUFREach Push: estimate response time
RT = SF x TUFR
Evaluation of Hybrid Technique
Resolver: UMD, Server: GT Probe 1/50 accesses, Push max 1/4 sec
Wide-Area Experiments
4
3
5
3
4
51
5
5
3
UCLA
WU
UMD
GT
Servers: UCLA, GTx2, WU,Clients: UMDx4, GTx16,Resolvers: UMD, GT
Anycasting VS Random Selection
Summary of Experiments
50% improvement using nearest serverAnother 50% using AnycastingMore predictable Service
Algorithm Ave. Resp.Time (Sec).
StandardDev. (Sec.)
Random 2.13 6.96Nearest 1.12 2.47Anycasting 0.49 0.69
What if Anycasting is popular?
Avoiding Oscillations
Indicating “best” server when queried can result in oscillations
Use set of equivalent best serversHysteresis to join and leave setChoose randomly among set
Effect of Oscillation Prevention Technique
Server Load
Basic Technique Basic technique withoscillation prevention
Worried about Scalability?
Me Too! Multicast pushed data Control frequency of push/probe --
CMU’s results are encouraging Resolver can track “most promising”
servers only Limit number of Anycast Groups Users pay premium for service
Concluding Remarks
Appropriate guidance of clients to servers is an important infrastructure function
Client-perceived as well as global performance can be improved with the appropriate selection technology
Emerging services and network environment makes problem more challenging and more important