Towards Efficient Load Balancing in Structured P2P Systems

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Towards Efficient Load Balancing in Structured
P2P Systems

• Yingwu Zhu, Yiming Hu
• University of Cincinnati

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Outline

• Motivation and Preliminaries
• Load balancing scheme
• Evaluation

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Why Load Balancing?

• Structured P2P systems, e.g., Chord,Pastry
• Object IDs and Node IDs are produced by using a
  uniform hash function.
• Results in O(log N) load imbalance, in the number
  of objects stored at each node.
• Skewed distribution of node capacity
• Nodes may carry loads proportional to their
  capacities.
• Other problems different object sizes,
  non-uniform dist. of object IDs.

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Virtual Servers (VS)

• First introduced in Chord/CFS.
• A VS is responsible for a contiguous region of
  the ID space.
• A node can host multiple VSs.

Node A
Node B
Node C
Chord Ring
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Virtual Sever Reassignment

• Virtual server is the basic unit of load
  movement, allowing load to be transferred between
  nodes.
• L Load, T Target Load.

Node A
T50
Node B
T35
Heavy
Node C
T15
Chord Ring
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Virtual Sever Reassignment

• Virtual server is the basic unit of load
  movement, allowing load to be transferred between
  nodes.
• L Load, T Target Load.

Node A
T50
Node B
T35
Heavy
Node C
T15
Chord Ring
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Virtual Sever Reassignment

• Virtual server is the basic unit of load
  movement, allowing load to be transferred between
  nodes.
• L Load, T Target Load.

Node A
T50
Node B
T35
Node C
T15
Chord Ring
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Advantages of Virtual Servers

• Flexible load is moved in the unit of a virtual
  server.
• Simple
• VS movement is supported by all structured P2P
  systems.
• Simulated by a leave operation followed by a join
  operation.

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Current Load Balancing Solutions

• Some use the concept of virtual server
• However
• Either ignore the heterogeneity of node
  capabilities.
• Or transfer loads without considering proximity
  relationships between nodes.
• Or both.

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Goals

• Goals
• To maintain each nodes load less than its target
  load (maximum load a node is willing to take).
• High capacity nodes take more loads.
• Load balancing is performed in proximity-aware
  manner, to minimize the overhead of load movement
  (bandwidth usage) and allow more efficient and
  fast load balancing.
• Load depends on the particular P2P systems.
• E.g., storage, network bandwidth, and CPU cycles.

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Assumptions

• Nodes in system are cooperative.
• Only one bottlenecked resource, e.g., storage or
  network bandwidth.
• The load of each virtual server is stable over
  the timescale when load balancing is performed.

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Overview of Design

• Step1 Load balancing information (LBI)
  aggregation, e.g., load and capacity info.
• Step2 Node classification. E.g., heavy nodes,
  light nodes, neutral nodes.
• Step3 Virtual server assignment (VSA).
• Step4 Virtual server transferring (VST).
• Proximity-aware load balancing
• VSA is proximity-aware.

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LBI Aggregation and Node Classification

• Rely on a fully decentralized, self-repairing,
  and fault-tolerant K-nary tree built on top of a
  DHT (distributed hash table).
• Each K-nary tree node is planted in a DHT node.
• ltL, C, Lmingt represents the load, capacity and
  the minimum load of virtual servers, respectively.

lt62, 48, 2gt
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LBI Aggregation and Node Classification

• Relying on a fully decentralized, self-repairing,
  and fault-tolerant K-nary tree built on top of a
  DHT.
• Each K-nary tree node is planted in a DHT node.
• ltL, C, Lmingt represents the load, capacity, and
  the minimum load of virtual servers.

lt62, 48, 2gt
Ti (L/C?)Ci
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Virtual Server Assignment
VSA happens earlier between logically closer nodes

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Virtual Server Assignment

• DHT identifier space-based VSA
• VSA happens earlier between logically closer
  nodes.
• Proximity-ignorant, because logically close nodes
  in DHT do NOT mean they are physically close
  together.

1 Nodes in same colors are physically close to
each other. 2 H heavy nodes, L light
nodes. 3 Vi virtual servers.
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Proximity-Aware VSA

• Nodes in same colors are physically close to each
  other.
• H heavy node, L light node, Vi virtual
  server.
• VSs are assigned between physically close nodes.

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Proximity-Aware VSA

• Use landmark clustering to generate proximity
  information, e.g. landmark vectors.
• Use space-filling curves (e.g., Hilbert curve)
  Landmark vectors ? Hilbert numbers as DHT keys.
• Heavy nodes and light nodes each puts/maps their
  VSA info. into the underlying DHT with the
  resulting DHT keys align physical closeness with
  logical closeness.
• Each virtual server independently reports the VSA
  info. which is mapped into its responsible
  region, rather than its nodes own VSA info.

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Proximity-Aware Virtual Server Assignment
VSA happens earlier between physically closer
nodes
Final rendezvous point
Physically close

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Experimental Setup

• A K-nary tree built on top of a DHT (Chord),
  e.g., k2, and 8, respectively.
• Two node capacity distributions
• Gnutella-like capacity profile, 5-level
  capacities.
• Zipf-like capacity profile.
• Two load distributions of virtual servers
• Gaussian dist. and Pareto dist.
• Two transit-stub topologies (5,000 nodes)
• ts5k-large and ts5k-small.

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High Capacity Nodes Carry More Loads
Gaussian load distribution Gnutella-like
capacity profile
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High Capacity Nodes Carry More Loads
Pareto load distribution Zipf-like capacity
profile
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Proximity-Aware Load Balancing
More loads are moved within shorter distances by
proximity-aware load balancing.
Gaussian load distribution and Gnutella-like
capacity profile
Pareto load distribution and Zipf-like capacity
profile
CDF of Moved Load Distribution in ts5k-large
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Benefit of Proximity-Aware Scheme

• Load movement cost

LM(d) denotes the load moved in the distance
of d hops.

• Benefit

• Results
• For ts5k-large B 37-65
• For ts5k-small B 11-20

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Other Results

• Quantify the overhead of K-nary tree
  construction
• Link stress, node stress.
• The latencies of LBI aggregation and VSA, bound
  in O(logN) time.
• The effect of pairing threshold in rendezvous
  points.

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Conclusions

• Current load balancing approaches using virtual
  servers have limitations
• Either ignore node capacity heterogeneity.
• Or transfer loads without considering proximity
  relationships between nodes.
• Or both.
• Our solution
• A fully decentralized, self-repairing, and
  fault-tolerant K-nary is built on top of DHTs for
  performing load balancing.
• Nodes carry loads in proportion to their
  capacities.
• The first work to address load balancing issue in
  a proximity-aware manner, thereby minimizing the
  overhead of load movement and allowing more
  efficient load balancing.

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Questions?