Indexing data-oriented overlay networks September 1 st, 2005 Indexing data-oriented overlay networks Presented by: Anwitaman Datta Joint work with Karl Aberer, Manfred Hauswirth, Roman Schmidt Ecole Polytechnique Fédérale de Lausanne (EPFL) Patrons: NCCR-MICS: Evergrow: Swiss National Centres of Competence in Research Mobile Information & Communication Systems EC FP6, IST priority “Complex System Research” Contract no (FET-IP) Ever-growing global scale-free networks, their provisioning, repair and unique functions.

Indexing data-oriented overlay networks September 1 st, 2005 Structured overlays ♫ Associate each peer with some part of the load, i.e., a partition of the key-space ♪ e.g. as in Distributed Hash Tables (DHT) ♫ Provide an efficient routing mechanism to locate peer responsible for a particular part of the key- space ♪ Various choice of topology possible

Indexing data-oriented overlay networks September 1 st, 2005 Structured overlay maintenance ♫ Dynamics ♪ Churn: Peers Join/Leave ♪ New data inserted ♫ Standard maintenance mechanisms ♪ Correspond to updating database index ♪ Traditionally: Overlay evolution has been studied for incremental peer population Challenge #1: Fast construction of structured overlay from scratch

Indexing data-oriented overlay networks September 1 st, 2005 ♫ Hash Tables give constant time look-ups ♪ At the cost of losing ordering information ♪ DHTs need log(n) network hops ♫ Can we preserve (semantic) ordering information? ♪ Skewed load-distribution Challenge #2: The structured overlay should deal with arbitrary skew of load Overlays for data-oriented applications

Indexing data-oriented overlay networks September 1 st, 2005 Toy example: Distributing skewed load 01 Load-distribution Key-space

Indexing data-oriented overlay networks September 1 st, 2005 ♫ Key-space can be divided in two partitions ♪ Assign peers proportional to the load in the two sub- partitions A globally coordinated recursive bisection approach Load-distribution

Indexing data-oriented overlay networks September 1 st, 2005 ♫ Recursively repeat the process to repartition the sub-partitions A globally coordinated recursive bisection approach Load-distribution

Indexing data-oriented overlay networks September 1 st, 2005 ♫ Partitioning of the key-space s.t. there is equal load in each partition ♪ Uniform replication of the partitions ♪ Important for fault- tolerance ♫ Note: A novel and general load- balancing problem. A globally coordinated recursive bisection approach 1 Load-distribution

Indexing data-oriented overlay networks September 1 st, 2005 Lessons from the globally coordinated algorithm ♫ The intermediate partitions may be such that they can not be perfectly repartitioned. ♪ There’s a fundamental limitation with any bisection based approach, as well as for any fixed key-space partitioned overlay network. ♫ Limit of dealing with load skews ♫ Nonetheless practical ♪ For realistic load-skews and peer populations Achieves an approximate load-balance.

Indexing data-oriented overlay networks September 1 st, step: Distributed proportional partitioning - for overlay construction ♫ Given: ♪ A mechanism to meet other random peers ♪ A parameter p for partitioning the space ♫ Proportional partitioning: Peers partition proportional to the load distribution ♪ In a ratio p:1-p ♪ Lets say: we call the sub-partitions as 0 and 1 ♫ Referential integrity: Obtain reference to the other partition ♪ Needed to enable overlay routing ♫ Sorting the load/keys: Peers exchange the locally stored keys in order to store only keys for its own partition. * 1 000,010,100 * 3 101,001 Random interaction 1: ,010,001 0: ,100 Routing table p id Keys (only part of the prefix is shown) Legend 01 partitioning

Indexing data-oriented overlay networks September 1 st, 2005 Heuristic 1: Autonomous partitioning (AUT) ♫ Make a priori probabilistic decision (parameterized by p) for a sub-partition ♪ proportionality constraint automatically met ♫ Find a peer from the other partition ♪ In order to meet referential integrity constraint ♫ Markovian asymptotic analysis of the process (for p = 0.5) ♪ 2 log(2) interactions (on an average) per peer

Indexing data-oriented overlay networks September 1 st, 2005 Heuristic 2: Eager partitioning ( for p = 0.5 ) ♫ Undecided peers initiate contact with other random peer ♪ If contacted peer is also undecided, contacting and contacted peers decide for different partitions (Balanced split) ♪ If contacted peer has already decided, contacting peer decides for the other partition (Unblanced split) ♫ Markovian asymptotic analysis of the process (for p = 0.5) ♪ log(2) interactions (on an average) per peer ♫ AUT is relatively inefficient ♪ AUT wastes interactions in order to find a suitable peer Challenge: Can we have a strategy which works for all values of p, and is as efficient as eager partitioning when p = 0.5?

Indexing data-oriented overlay networks September 1 st, 2005 Heuristic 2: Eager partitioning ( for p = 0.5 ) ♫ Undecided peers initiate contact with other random peer ♪ If contacted peer is also undecided, contacting and contacted peers decide for different partitions (Balanced split) ♪ Refer to each other ♪ If contacted peer has already decided, contacting peer decides for the other partition (Unblanced split) ♪ Contacting peer refers to the contacted peer ♫ Markovian asymptotic analysis of the process (for p = 0.5) ♪ log(2) interactions (on an average) per peer ♫ AUT is relatively inefficient ♪ Wastes interactions in order to meet referential integrity Challenge: Can we have a strategy which works for all values of p, and is as efficient as eager partitioning when p = 0.5?

Indexing data-oriented overlay networks September 1 st, 2005 AEP: Adaptive eager partitioning (w.l.g, p ≤ 0.5 ) ♫ Undecided peers initiate contact with other random peers ♪ If contacted peer is also undecided, perform Balanced split with probability: ♪ Since we need more peers (a fraction of 1-p ) in sub-partition 1 ♪ If the contacted peer has already decided for 0, contacting peer decides for 1 ♪ If the contacted peer has already decided for 1, contacting peer decides for 0 with a probability: ♪ 1 otherwise, since we need more peers in sub-partition 1

Indexing data-oriented overlay networks September 1 st, 2005 Adaptive eager partitioning: choice of parameters ♫ Markovian analysis of the interactions ♪ Parameterized equations for & ♫ 0 ≤ p ≤ 1-log(2) ♪ ♫ 1-log(2) ≤ p ≤ 0.5 ♪

Indexing data-oriented overlay networks September 1 st, 2005 AEP: Without global knowledge of p ♫ If we only have local estimates of p ♪ Error analysis: What’s the distribution of the estimates, and how does it affect the partitioning process? ♪ Introduces systematic skew ♪ Favors larger partition ♪ Compensating the skew

Indexing data-oriented overlay networks September 1 st, 2005 COR: Skew compensated for AEP

Indexing data-oriented overlay networks September 1 st, 2005 Algorithmic Issues: Overlay Construction ♫ Initiating the indexing process ♫ Synchronizing and terminating the process ♪ Synchronizing replicas ♫ Complexity ♪ Latency: O(log(n) 2 ) - linear for sequential processes ♪ Communication: O(n.log(n) 2 ) - same as in sequential processes

Indexing data-oriented overlay networks September 1 st, 2005 Simulation results ♫ Discrete time simulation ♪ Mathematica based proprietary simulator ♫ Workloads ♪ Uniform, Pareto, Normal, real text collection from IR apps. (EU project: Alvis) ♫ Evaluation ♪ Deviation w.r.to what is obtained by the globally coordinated algorithm ♪ Measured in terms of the Euclidian Distance

Indexing data-oriented overlay networks September 1 st, 2005 Simulation results: How useful is the theory? Theory vs. Heuristic (256 peers) deviation Load distribution Load-distribution U: Uniform P: Pareto N: Normal A: Alvis IR proj. text

Indexing data-oriented overlay networks September 1 st, 2005 Load-distribution U: Uniform P: Pareto N: Normal Quality of load-balancing w.r.to peer population Peer populations deviation Expts: Population & Load distribution

Indexing data-oriented overlay networks September 1 st, 2005 Scalability Load-distribution U: Uniform P: Pareto N: Normal A: Alvis IR proj. text Interactions required per peer for overlay construction interactions Expts: Population & Load distribution

Indexing data-oriented overlay networks September 1 st, 2005 From theory to practice: PlanetLab experiments ♫ PlanetLab Testbed ♪ 400+ computers spread over various organizations and continents ( lab.org) ♫ Java implementation integrated with P-Grid ♪ P-Grid is a full-fledged P2P software ( ♫ Workload ♪ Text from IR applications studied under EU project Alvis ( Bootstrap the peers and form an unstructured network Structured overlay constructionExperiments evaluating search performance Churn Simulation vs. Expts SimExpt deviation peers Expt period "All models are wrong, but some are useful." - George E.P. Box

Indexing data-oriented overlay networks September 1 st, 2005 Bandwidth consumption Overlay construction phase Overlay operational phase ♪ Construction process involves sorting keys. ♪ Initially it has higher bandwidth requirement. ♪ (Later) In operational phase, the queries dominate the bandwidth consumption. Expt period

Indexing data-oriented overlay networks September 1 st, 2005 Overlay performance ♪ Overlay construction was complete and peers discovered all their replicas ♪ Plots show absolute query latency ♪ In terms of overlay hops, experiments match theory ♪ Churn leads to larger deviation, but 95% to 100% success rate Expt period query latency Churn No churn

Indexing data-oriented overlay networks September 1 st, 2005 Related work ♫ Mostly sequential construction ♪ Recent work on fast overlay construction [SPAA 2005] ♪ Does not deal with load-balancing ♫ Load-balancing ♪ Mostly addresses uniform load-distribution case ♪ Some work on skewed loads [e.g., VLDB 2004] ♪ Incremental load/peer population changes ♪ No dynamic adaptation of replication

Indexing data-oriented overlay networks September 1 st, Java implementation source-code available for download ♫lso: Range query IEEE P2P 2005