Trust-Sensitive Scheduling on the Open Grid
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Transcript of Trust-Sensitive Scheduling on the Open Grid
Trust-Sensitive Scheduling on the Open Grid
Jon B. Weissmanwith help from Jason Sonnek and Abhishek
ChandraDepartment of Computer Science
University of MinnesotaTrends in HPDC Workshop
Amsterdam 2006
Background
• Public donation-based infrastructures are attractive– positives: cheap, scalable, fault tolerant
(UW-Condor, *@home, ...)
– negatives: “hostile” - uncertain resource availability/connectivity, node behavior, end-user demand => best effort service
Background
• Such infrastructures have been used for throughput-based applications– just make progress, all tasks equal
• Service applications are more challenging– all tasks not equal– explicit boundaries between user requests– may even have SLAs, QoS, etc.
Service Model
• Distributed Service– request -> set of independent tasks– each task mapped to a donated node– makespan
– E.g. BLAST service• user request (input sequence) + chunk of DB form
a task
BOINC + BLAST
workunit = input_sequence + chunk of DBgenerated when a request arrives
The Challenge
• Nodes are unreliable– timeliness: heterogeneity, bottlenecks, …– cheating: hacked, malicious (> 1% of SETi
nodes), misconfigured– failure– churn
• For a service, this matters
Some data- timeliness
Computation Heterogeneity
- both across and within nodes
Communication Heterogeneity
- both across and within nodes
PlanetLab – lower bound
The Problem for Today
• Deal with node misbehavior
• Result verification– application-specific verifiers – not general– redundancy + voting
• Most approaches assume ad-hoc replication– under-replicate: task re-execution (^ latency)– over-replicate: wasted resources (v throughput)
• Using information about the past behavior of a node, we can intelligently size the amount of redundancy
System Model
Problems with ad-hoc replication
Unreliable node
Reliable nodeTask x sent to group A
Task y sent to group B
Smart Replication• Reputation
– ratings based on past interactions with clients
– simple sample-based prob. (ri) over window
– extend to worker group (assuming no collusion) => likelihood of correctness (LOC)
• Smarter Redundancy– variable-sized worker groups– intuition: higher reliability clients => smaller groups
Terms• LOC (Likelihood of Correctness), g
– computes the ‘actual’ probability of getting a correct answer from a group of clients (group g)
• Target LOC (target)– the task success-rate that the system tries to ensure while
forming client groups– related to the statistics of the underlying distribution
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Trust Sensitive Scheduling
• Guiding metrics– throughput : is the number of successfully
completed tasks in an interval
– success rate s: ratio of throughput to number of tasks attempted
Scheduling Algorithms
• First-Fit– attempt to form the first group that satisfies target
• Best-Fit– attempt to form a group that best satisfies target
• Random-Fit– attempt to form a random group that satisfies target
• Fixed-size– randomly form fixed sized groups. Ignore client
ratings. • Random and Fixed are our baselines• Min group size = 3
Scheduling Algorithms
Scheduling Algorithms (cont’d)
Different Groupings
target = .5
Evaluation• Simulated a wide-variety of node
reliability distributions
• Set target to be the success rate of Fixed– goal: match success rate of fixed (which over-
replicates) yet achieve higher throughput– if desired, can drive tput even higher (but
success rate would suffer)
Comparison
gain: 25-250%open question: how much better could we have done?
Non-stationarity• Nodes may suddenly shift gears
– deliberately malicious, virus, detach/rejoin– underlying reliability distribution changes
• Solution– window-based rating (reduce from infinite)
• Experiment: “blackout” at round 300 (30% effected)
Role of target
• Key parameter• Too large
– groups will be too large (low throughput)• Too small
– groups will be too small (low success rate)• Adaptively learn it (parameterless)
– maximizing * s : “goodput”– or could bias toward or s
Adaptive algorithm
• Multi-objective optimization– choose target LOC to simultaneously
maximize throughput and success rate s1 2 s
– use weighted combination to reduce multiple objectives to a single objective
– employ hill-climbing and feedback techniques to control dynamic parameter adjustment
Adapting target
• Blackout example
Throughput (1=1, 2=0)
BF
-Uniform
BF
-Norm
Low
BF
-Norm
Hig
h
BF
-HeavyLow
BF
-HeavyH
igh
BF
-Bim
odal Min
AdaptMax0
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Xput comparison - BF
Min
Adapt
Max
Current/Future Work
• Implementation of reputation-based scheduling framework (BOINC and PL)
• Mechanisms to retain node identities (hence ri) under node churn
– “node signatures” that capture the characteristics of the node
Current/Future Work (cont’d)
• Timeliness– extending reliability to encompass time– a node whose performance is highly variable is less
reliable
• Client collusion– detection: group signatures– prevention:
• combine quiz-based tasks with reputation systems• form random-groupings
Thank you.