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Local Computations in Large-Scale Networks. Idit Keidar Technion. Material. I. Keidar and A. Schuster: “ Want Scalable Computing? Speculate! ” SIGACT News Sep 2006. http://www.ee.technion.ac.il/people/idish/ftp/speculate.pdf

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Local computations in large scale networks

Local Computations in Large-Scale Networks

Idit Keidar



  • I. Keidar and A. Schuster: “Want Scalable Computing? Speculate!”SIGACT News Sep 2006.http://www.ee.technion.ac.il/people/idish/ftp/speculate.pdf

  • Y. Birk, I. Keidar, L. Liss, A. Schuster, and R. Wolff: “Veracity Radius - Capturing the Locality of Distributed Computations”. PODC'06.http://www.ee.technion.ac.il/people/idish/ftp/veracity_radius.pdf

  • Y. Birk, I. Keidar, L. Liss, and A. Schuster: “Efficient Dynamic Aggregation”. DISC'06. http://www.ee.technion.ac.il/people/idish/ftp/eff_dyn_agg.pdf

  • E. Bortnikov, I. Cidon and I. Keidar: “Scalable Load-Distance Balancing in Large Networks”. DISC’07. http://www.ee.technion.ac.il/people/idish/ftp/LD-Balancing.pdf

Brave new distributed systems
Brave New Distributed Systems

  • Large-scale

    Thousands of nodes and more ..

  • Dynamic

    … coming and going at will ...

  • Computations

    … while actually computing something together.

This is the new part.

Today s huge dist systems
Today’s Huge Dist. Systems

  • Wireless sensor networks

    • Thousands of nodes, tens of thousands coming soon

  • P2P systems

    • Reporting millions online (eMule)

  • Computation grids

    • Harnessing thousands of machines (Condor)

  • Publish-subscribe (pub-sub) infrastructures

    • Sending lots of stock data to lots of traders

Not computing together yet
Not Computing Together Yet

  • Wireless sensor networks

    • Typically disseminate information to central location

  • P2P & pub-sub systems

    • Simple file sharing, content distribution

    • Topology does not adapt to global considerations

    • Offline optimizations (e.g., clustering)

  • Computation grids

    • “Embarrassingly parallel” computations

Emerging dist systems examples
Emerging Dist. Systems – Examples

  • Autonomous sensor networks

    • Computations inside the network, e.g., detecting trouble

  • Wireless mesh network (WMN) management

    • Topology control

    • Assignment of users to gateways

  • Adapting p2p overlays based on global considerations

  • Data grids (information retrieval)

Autonomous sensor networks
Autonomous Sensor Networks

The data center is too hot!

Let’s turn on the sprinklers (need to backup first)

Let’s all reduce power

Autonomous sensor networks1
Autonomous Sensor Networks

  • Complex autonomous decision making

    • Detection of over-heating in data-centers

    • Disaster alerts during earthquakes

    • Biological habitat monitoring

  • Collaboratively computing functions

    • Does the number of sensors reporting a problem exceed a threshold?

    • Are the gaps between temperature reads too large?

Wireless mesh networks1
Wireless Mesh Networks

  • Infrastructure (unlike MANET)

  • City-wide coverage

  • Supports wireless devices

  • Connections to Mesh and out to the Internet

    • “The last mile”

  • Cheap

    • Commodity wireless routers (hot spots)

    • Few Internet connections

Decisions decisions
Decisions, Decisions

  • Assigning users to gateways

    • QoS for real-time media applications

    • Network distance is important

    • So is load

  • Topology control

    • Which links to set up out of many “radio link” options

    • Which nodes connect to Internet (act as gateways)

    • Adapt to varying load

Centralized solutions don t cut it
Centralized Solutions Don’t Cut It

  • Load

  • Communication costs

  • Delays

  • Fault-tolerance

Classical dist solutions don t cut it
Classical Dist. Solutions Don’t Cut It

  • Global agreement / synchronization before any output

  • Repeated invocations to continuously adapt to changes

  • High latency, high load

  • By the time synchronization is done, the input may have changed … the result is irrelevant

  • Frequent changes -> computation based on inconsistent snapshot of system state

  • Synchronizing invocations initiated at multiple locations typically relies on a common sequencer (leader)

    • difficult and costly to maintain

Locality to the rescue
Locality to the Rescue!


  • Nodes make local decisions based on communication (or synchronization) with some proximate nodes, rather than the entire network

  • Infinitely scalable

  • Fast, low overhead, low power, …

The locality hype
The Locality Hype

  • Locality plays a crucial role in real life large scale distributed systems

John Kubiatowicz et.al, on global storage:

“In a system as large as OceanStore,

locality is of extreme importance…

C. Intanagonwiwat et.al, on sensor networks:

“An important feature of directed

diffusion is that … are determined

by localized interactions...”

N. Harvey et.al, on scalable DHTs:

“The basic philosophy of SkipNet

is to enable systems to preserve

useful content and path locality…”

What is locality
What is Locality?

  • Worst case view

    • O(1) in problem size [Naor & Stockmeyer,1993]

    • Less than the graph diameter [Linial, 1992]

    • Often applicable only to simplistic problems or approximations

  • Average case view

    • Requires an a priori distribution of the inputs

To be continued…

Interesting problems have inherently global instances
Interesting Problems Have Inherently Global Instances

  • WMN gateway assignment: arbitrarily high load near one gateway

    • Need to offload as far as the end of the network

  • Percentage of nodes whose input exceeds threshold in sensor networks: near-tie situation

    • All “votes” need to be counted

Fortunately, they don’t happen too often

Speculation is the key to locality
Speculation is the Key to Locality

  • We want solutions to be “as local as possible”

  • WMN gateway assignment example:

    • Fast decision and quiescence under even load

    • Computation time and communication adaptive to distance to which we need to offload

  • A node cannot locally know whether the problem instance is local

    • Load may be at other end of the network

  • Can speculate that it is (optimism )

Computations are never done
Computations are Never “Done”

  • Speculative output may be over-ruled

  • Good for ever-changing inputs

    • Sensor readings, user loads, …

  • Computing ever-changing outputs

    • User never knows if output will change

      • due to bad speculation or unreflected input change

    • Reflecting changes faster is better

  • If input changes cease, output will eventually be correct

    • With speculation same as without

Summary prerequisites for speculation
Summary: Prerequisites for Speculation

  • Global synchronization is prohibitive

  • Many instances amenable to local solutions

  • Eventual correctness acceptable

    • No meaningful notion of a “correct answer” at every point in time

    • When the system stabilizes for “long enough”, the output should converge to the correct one

The challenge find a meaningful notion for locality
The Challenge: Find aMeaningful Notion for Locality

  • Many real world problems are trivially global in the worst case

  • Yet, practical algorithms have been shown to be local most of the time !

  • The challenge: find a theoretical metric that captures this empirical behavior

Reminder na ve locality definitions
Reminder: Naïve Locality Definitions

  • Worst case view

    • Often applicable only to simplistic problems or approximations

  • Average case view

    • Requires an a priori distribution of the inputs

Instance locality

  • Formal instance-based locality:

    • Local fault mending [Kutten,Peleg95, Kutten,Patt-Shamir97]

    • Growth-restricted graphs [Kuhn, Moscibroda, Wattenhofer05]

    • MST [Elkin04]

  • Empirical locality: voting in sensor networks

    • Although some instances require global computation, most can stabilize (and become quiescent) locally

    • In small neighborhood, independent of graph size

    • [Wolff,Schuster03, Liss,Birk,Wolf,Schuster04]

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

Local complexity bklsw 06
Local Complexity [BKLSW’06]

  • Let

    • G be a family of graphs

    • P be a problem on G

    • M be a performance measure

    • Classification CG of inputs to P on a graph G into classes C

    • For class of inputs C, MLB(C) be a lower bound for computing P on all inputs in C

  • Locality: GGCCGIC : MA(I)  const  MLB(C)

  • A lower bound on a single instance is meaningless!

The trick is in the classification
The Trick is in The Classification

  • Classification based on parameters

    • Peak load in WMN

    • Proximity to threshold in “voting”

  • Independent of system size

  • Practical solutions show clear relation between these parameters and costs

  • Parameters not always easy to pinpoint

    • Harder in more general problems

    • Like “general aggregation function”

Veracity radius capturing the locality of distributed computations

Veracity Radius – Capturing the Locality of Distributed Computations

Yitzhak Birk, Idit Keidar, Liran Liss, Assaf Schuster, and Ran Wolf

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)

Local aggregation intuition
Local Aggregation – Intuition

  • Example – Majority Voting:

  • Consider a partition in which every set has the same aggregate result (e.g., >50% of the votes are for ‘1’)

  • Obviously, this result is also the global one!












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


Introducing slack
Introducing Slack

  • Examine “neighborhood-like” environments that:

    • (1) include an a(r)-neighborhood for some a(r)<r

    • (2) are included in an r-neighborhood

  • Example: a(r)=max{r-1,r/2}

r = 2:

wrong result

Global result:


Vr yields a class based lower bound


only b’s


only a’s

n1 a’s



n1 a’s



n2 b’s

n2 b’s

VR Yields a Class-Based Lower Bound

  • VR for both input assignments is  r

  • Node v cannot distinguish between I and I’ in fewer than r steps

  • Lower bound of r on both output stabilization and quiescence

  • Trivially tight bound for output stabilization

Veracity radius captures the locality of one shot aggregation bklsw podc 06
Veracity Radius Captures the Locality of One-Shot Aggregation [BKLSW,PODC’06]

  • 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…

Local partition hierarchy
Local Partition Hierarchy

  • Topology static

    • Input changes more frequently

  • Build structure to assist aggregation

    • Once per topology change

    • Spanning tree, but with locality properties

Minimal slack lph for meshes with a r max r 1 r 2

Mesh edge:

Level 0 edge:

Level 1 edge:

Level 2 edge:

Level 0 pivot:

Level 1 pivot:

Level 2 pivot:

Minimal Slack LPH for Mesheswith a(r)=max(r-1,r/2)

The i leag algorithm
The I-LEAG Algorithm

  • Phases correspond to LPH levels

  • Communication occurs within a cluster only if there are nodes with conflicting outputs

    • All of the cluster’s nodes hold the same output when the phase completes

    • All clusters’ neighbors know the cluster’s output

  • Conflicts are detected without communication

    • I-LEAG reaches quiescence once the last conflict is detected

I leag s operation majority voting
I-LEAG’s Operation(Majority Voting)

  • Legend:




Tree edge:




Node’s output is its input

Startup communication among tree neighbors
Startup: Communication AmongTree Neighbors

  • Legend:




Tree edge:



Recall neighbor values

will be used in all phases

Phase 0 conflict detection
Phase 0 Conflict Detection

  • Legend:











Phase 0 conflict resolution
Phase 0 Conflict Resolution

Updates sent by clusters that had conflicts

  • Legend:




Tree edge:



Phase 1 conflict detection
Phase 1 Conflict Detection

  • Legend:





Tree edge:






No new Communication

Phase 1 conflict resolution
Phase 1 Conflict Resolution

Updates sent by clusters that had conflicts

  • Legend:




Tree edge:



Phase 2 conflict detection
Phase 2 Conflict Detection

Using information sent at phase 0

  • Legend:




Tree edge:



No Communication

Phase 2 conflict resolution
Phase 2 Conflict Resolution

This region has been idle since phase 0

  • Legend:




Tree edge:



No conflicts found,

no need for resolution

Simulation study
Simulation Study

  • VR also explains the locality of previous algorithms

Efficient dynamic aggregation

Efficient Dynamic Aggregation

Yitzhak Birk, Idit Keidar, Liran Liss, and

Assaf Schuster

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 

Dynamic aggregation at two timescales
Dynamic Aggregation at Two Timescales

  • 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

Dynamic aggregation conclusions
Dynamic Aggregation: Conclusions

  • 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

Scalable load distance balancing

Scalable Load-Distance Balancing

Edward Bortnikov, Israel Cidon, Idit Keidar

Load distance balancing
Load-Distance Balancing

  • Two sources of service delay

    • Network delay (depends on distance to server)

    • Congestion delay (depends on server load)

    • Total = Network + Congestion

  • Input

    • Network distances and congestion functions

  • Optimization goal

    • Minimize the maximum total delay

  • NP-complete, 2-approximation exists

Distributed setting
Distributed Setting

  • Synchronous

  • Distributed assignment computation

    • Initially, users report location to the closest servers

    • Servers communicate and compute the assignment

  • Requirements:

    • Eventual quiescence

    • Eventual stability of assignment

    • Constant approximation of the optimal cost (parameter)

Impact of locality
Impact of Locality

  • Extreme global solution

    • Collect all data and compute assignment centrally

    • Guarantees optimal cost

    • Excessive communication/network latency

  • Extreme local solution

    • Nearest-Server assignment

    • No communication

    • No approximation guarantee (can’t handle crowds)

  • No “one-size-fits-all”?

Workload sensitive locality
Workload-Sensitive Locality

  • The cost function is distance-sensitive

    • Most assignments go to the near servers

    • … except for dissipating congestion peaks

  • Key to distributed solution

    • Start from the Nearest-Server assignment

    • Load-balance congestion among near servers

  • Communication locality is workload-sensitive

    • Go as far as needed …

    • … to achieve the required approximation

Skewed load1
Skewed Load



Skewed load2
Skewed Load


Tree clustering
Tree Clustering

  • As long as some cluster has improvable cost

    • Double it (merge with hierarchy neighbor)

  • Clusters aligned at 2i indices

  • Simple, O(log N) convergence time

Ripple clustering
Ripple Clustering

  • Adaptivemerging

    • Better cost in practice

  • As long as some cluster is improvable

    • Merge with smaller-cost neighbors

  • Conflicts possible

    • A B C

    • A B C

    • Random tie-breaking to resolve

    • Many race conditions (we love it -)

Scalability cost
Scalability: Cost

cost =

Euclidian distance +

linear load