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Efficient Dynamic Aggregation. Yitzhak Birk , Idit Keidar , Liran Liss, Assaf Schuster Technion. Dynamic Aggregation. Continuous monitoring of aggregate value over changing inputs Examples: More than 10% of sensors report of seismic activity Maximum temperature in data center

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efficient dynamic aggregation

Efficient Dynamic Aggregation

Yitzhak Birk, Idit Keidar,Liran Liss, Assaf Schuster


dynamic aggregation
Dynamic Aggregation
  • Continuous monitoring of aggregate value over changing inputs
  • Examples:
    • More than 10% of sensors report of seismic activity
    • Maximum temperature in data center
    • Average load in computation grid
the setting
The Setting
  • Large graph (e.g., sensor network)
    • Direct communication only between neighbors
  • Each node has a changing input
  • Inputs change more frequently than topology
    • Consider topology as static
  • Aggregate function f on multiplicity of inputs
    • Oblivious to locations
  • Aggregate result computed at all nodes
goals for dynamic aggregation
Goals for Dynamic Aggregation
  • Fast convergence
    • If from some time t onward inputs do not change …
      • Output stabilization time from t
      • Quiescence time from t
      • Note: nodes do not know when stabilization and quiescence are achieved
    • If after stabilization input changes abruptly…
  • Efficient communication
    • Zero communication when there are zero changes
    • Small changes  little communication
standard aggregation solution spanning tree
Standard Aggregation Solution: Spanning Tree

20 black, 12 white

Global communication!


7 black, 1 white


2 black

1 black

spanning tree value change
Spanning Tree: Value Change

19 black, 13 white

Global communication!

6 black, 2 white

the bad news
The Bad News
  • Virtually every aggregation function has instances that cannot be computed without communicating with the whole graph
    • E.g., majority voting when close to the threshold “every vote counts”
  • Worst case analysis: convergence, quiescence times are (diameter)
instance locality to the rescue
Instance-Locality to the Rescue
  • Although some instances require global computation, most can stabilize (and become quiescent) locally
    • In small neighborhood, independent of graph size
    • Shown empirically [Wolff,Schuster03, Liss,Birk,Wolf,Schuster04]
  • Formal instance-based locality in other contexts
    • Local fault mending [Kutten,Peleg95, Kutten,Patt-Shamir97]
    • Growth-restricted graphs [Kuhn, Moscibroda, Wattenhofer05]
    • MST [Elkin04]
per instance optimality too strong
“Per-Instance” Optimality Too Strong
  • Instance: assignment of inputs to nodes
  • For a given instance I, algorithm AIdoes:
    • if (my input is as in I) output f(I)else send message with input to neighbor
    • Upon receiving message, flood it
    • Upon collecting info from the whole graph, output f(I)
  • Convergence and output stabilization in zero time on I
  • Can you beat that?

Need to measure optimality per-class notper-instance

Challenge: capture attainable locality

veracity radius vr for one shot aggregation bklsw podc 06
Veracity Radius (VR) for One-Shot Aggregation [BKLSW,PODC’06]
  • Roughly speaking: the min radius r0 such that"r> r0: all r-neighborhoods have same result
  • Example: majority

Radius 1:

wrong result

Radius 2:

correct result


veracity radius captures the locality of one shot aggregation bklsw podc 06
Veracity Radius Captures the Locality of One-Shot Aggregation [BKLSW,PODC’06]
  • Class-based lower bound
    • Both output stabilization and quiescence
    • For every r, for every algorithm A, there is an instance I with VR(I)  r on which A takes  r time
  • I-LEAG (Instance-Local Efficient Aggregation on Graphs)
    • Quiescence and output stabilization proportional to VR
    • Per-class within a factor of optimal
    • Local: depends on VR, not graph size!
  • Note: nodes do not know VR or when stabilization and quiescence are achieved
    • Can’t expect to know you’re “done” in dynamic aggregation…
na ve dynamic aggregation
Naïve Dynamic Aggregation
  • Periodically,
    • Each node samples input, initiates I-LEAG
    • Each instance I of I-LEAG takes O(VR(I)) time, but sends (|V|) messages
  • Sends messages even when no input changes
    • Costly in sensor networks 
  • To save messages, must compromise freshness of result 
  • New lower bound
    • For algorithms that send zero messages when there are zero changes
  • Efficient multi-shot aggregation algorithm (MultI-LEAG)
    • Converges to correct result before sampling the inputs again
    • Sampling time may be proportional to graph size
  • Efficient dynamic aggregation algorithm (DynI-LEAG)
    • Sampling time is independent of graph size
    • Algorithm tracks global result as close as possible
dynamic lower bound
Dynamic Lower Bound
  • Previous sample (instance) also plays a role
    • Example (majority voting):
  • Multi-shot lower bound:max{VRprev,VR}
    • On quiescence and output stabilization
    • Assumes sending zero messages when there are zero changes

I2 (0 changes)

I1 (VR2)



I3 (VR=0)

dynamic aggregation take ii
Dynamic Aggregation: Take II
  • Initially, run local one-shot algorithm A
    • Store distance information travels in this instance, dist
  • Let D = A’s worst-case convergence time
  • Every D time, run a new iteration (MULTI-A)
    • If input did not change, do nothing
    • If input changed, run full information protocol up to dist
    • If new instance’s VR isn’t reached, invoke A anew
    • Update dist


  • (~ VRprev)


  • Matches max{VRprev,VR} lower bound
    • within same factor as A
a is for i leag
A is for I-LEAG
  • I-LEAG uses a pre-computed partition hierarchy
    • LPH: Local Partition Hierarchy – cluster sizes bounded both from above and from below (doubling sizes)
    • Spanning tree in each cluster, rooted at pivot
    • Computed once per topology
  • I-LEAG phases correspond to LPH levels
    • Active phase: full-information from cluster  pivot
    • Phase result communicated to cluster and its neighbors
    • Phase active only if there is a conflict in the previous level
    • Conflicts detected without new communication
multi leag
  • The Veracity Level (VL) of node v is the highest LPH level in which v’s cluster has a conflict (VL<logVR+1)
  • A multi-LEAG iteration’s phases correspond to LPH levels:
    • Phase level < VL: propagate changes (if any) to pivot
      • active only if there are changes
    • Phase level  VL: fall back to I-LEAG
      • active only if new VR is larger than previous
    • Cache partial aggregate results in pivot nodes
      • allows conflict detection between active and passive clusters
multi leag operation
MultI-LEAG Operation

Veracity Level

Pivot nodes

Physical nodes

multi leag operation1
MultI-LEAG Operation
  • Case I: No changes

… no conflicts

… no conflicts

… no changes to report

All is quiet…

input change
Input Change

no conflicts, no communication

New veracity level


abrupt change flips outcome1
Abrupt Change Flips Outcome

Clusters at VL recalculate, others forward up

abrupt change flips outcome2
Abrupt Change Flips Outcome

no conflicts, no communication

New Veracity level

multi leag observations
MultI-LEAG Observations
  • O(max{VRprev,VR}) output stabilization and quiescence
  • Message efficient:
    • Communication only in clusters with changes, only when radius < max{VRprev,VR}
  • Sampling time is O(Diameter)
    • Good for cheap periodic aggregation
    • Can we do closer monitoring?
dynamic aggregation take iii dyni leag
Dynamic Aggregation Take III: DynI-LEAG
  • Sample inputs every O(1) link delays
    • Close monitoring, rapidly converges to correct result
  • Run multiple MultI-LEAG iterations concurrently
  • Challenges:
    • Pipelining phases with different (doubling) durations
    • Intricate interaction among concurrent instancesE.g., which phase 4 updates are used in a given phase 5 ..
    • Avoiding state explosion for multiple concurrent instances
ruler pipelining
Ruler Pipelining
  • Partial iterations, fewer in every level
  • Changes only communicated once

Full iteration

Sampling interval

Phase 2

Partial iteration

Phase 1

Phase 0


  • Memory usage: O(log(Diameter))
vl and output estimation
VL and Output Estimation
  • Problem: correct output and VL of an iteration is guaranteed only after O(Diameter) time
    • cannot wait that long…
  • Solution: choose iteration with highest VL according to most recent information
    • Use this VL for new iterations and its output as MultI-LEAG’s current output estimation
  • Eventual convergence and correctness guaranteed
dyni leag operation
DynI-LEAG Operation

The influence of a conflict is proportional to its level

Phase below VL

Phase above VL





“Previous VL” = 2

  • Local operation is possible
    • in dynamic systems
    • that solve inherently global problems
  • MultI-LEAG delivers periodic correct snapshots at minimal cost
  • DynI-LEAG responds immediately to input changes with a slightly higher message rate