trust-sensitive scheduling on the open grid
DESCRIPTION
Trust-Sensitive Scheduling on the Open Grid. Jon B. Weissman with help from Jason Sonnek and Abhishek Chandra Department of Computer Science University of Minnesota Trends in HPDC Workshop Amsterdam 2006. Background. Public donation-based infrastructures are attractive - PowerPoint PPT PresentationTRANSCRIPT
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
5
10
15
20
25
30
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.