Outline. Simulating overlay networks with PeerSim. Introduction: P2P Systems. What is PeerSim? Peersim components. ! Introduction to Peersim

Transcription

1 Simulating overlay networks with PeerSim Andrea Marcozzi 08/03/2007 Dipartimento di Scienze dell Informazione, Università di Bologna! Introduction to Peersim What is Peersim Peersim components! Aggregation What is aggregation Average in Peersim! SLAC Tags What is SLAC SLAC in Peersim Outline Introduction: P2P Systems! Peer-to-Peer systems are potentially huge (composed of millions of nodes);! Nodes join and leave the network continuously;! Evaluating a new protocol in a real environment is not an easy task What is PeerSim?! PeerSim is an open source P2P systems simulator developed at the Department of Computer Science, University of Bologna! It has been developed with Java! Available on Source Forge (peersim.sf.net)! Its aim is to cope with P2P systems properties! High Scalability (up to 1 million nodes)! Highly configurable! Architecture based on pluggable components

2 The Peersim Simulation Engine! Peersim has two simulation modes: Event-driven: a set of events (messages) are scheduled at different simulation time and the node protocols are run accordingly to messages delivery times Cycle-driven: sequential simulation, in each cycle each protocol s actions are executed by every node (we will focus on this) Network Representation (I) Node n-2 n-1 n Node Protocol 0 (Linkable) Network Protocol 1 (Application)! A node is a stack of protocols Network Representation (II) Network Representation (III)! Network: global array which contains all the network nodes! Node: each node s state and actions are described through a stack of protocols. Protocol are accessed through a Pid! Linkable: interface used to access and manage node s view! CDProtocol: interface used to describe node s actions at each cycle. A generic node can both perform local actions (CDProtocol) and manage the local view (Linkable)! Control: performs the global initialization and performances analysis

3 Main Interfaces: Protocol Main Interfaces: Linkable! The CDProtocol interface is used to define cycle-driven protocols, that is the actions performed by each node at each simulation cycle! Each node can run more than one protocol! Protocols are executed sequentially! Linkable is used to manage node s view. Typical actions are: Add neighbor Get neighbor Node s degree Remove neighbor Main Interfaces: Control! Control is used to define operations that require global network knowledge as: Initialization of the network and protocols Network dynamism Statistical analysis (observer) Peersim tree! SRC contains the source code of Peersim and of some example protocols (aggregation, newscast)! The parameters for the simulation are specified by a Configuration File

4 Example: AverageFunction 1. AverageFunction Aggregation 0. Newscast By Aggregation we mean calculating a certain function (eg. average) over a number of values distributed over a network Example: AverageFunction (II)! Each node has a numeric value! The aggregate value has to be known by each node! Local value of node i contains current estimate of the average! Node i select random peer j: i and j exchange estimates i and j update local estimate with the average 01 #random.seed simulation.cycles control.shf Shuffle 04 network.size protocol.0 example.newscast.simplenewscast 07 protocol.0.cache protocol.1 example.aggregation.averagefunction 09 protocol.1.linkable 0 10 order.protocol init.0 peersim.dynamics.wirekout 13 init.0.protocol 0 14 init.0.k init.1 example.aggregation.peakdistributioninitializer 17 init.1.protocol 1 18 init.1.value control.ob0 aggregation.averageobserver 21 control.ob0.protocol 1 Configuration File General settings Protocols settings Control settings

5 Config File: General Settings 01 #random.seed simulation.cycles control.shf Shuffle 04 network.size 50000! Line 01: define the seed to be used. If not defined uses a random seed;! Line 02: defines the number of cycles! Line 03: if defined, shuffles the Network array at the beginning of each cycle! Line 04: defines the Network size Config File: Protocol Settings 06 protocol.0 example.newscast.simplenewscast 07 protocol.0.cache protocol.1 example.aggregation.averagefunction 09 protocol.1.linkable 0 10 order.protocol 0 1! Line 06: define protocol 0 as Newscast! Line 07: define protocol 0 s variable cache (it is the view size)! Line 08: define protocol 1 as the application calculating average aggregation! Line 09: indicates that the linkable is Newscast (0)! Line 10: indicated the order for protocols execution Config File: Control Settings Aggregation: execution 12 init.0 peersim.dynamics.wirekout 13 init.0.protocol 0 14 init.0.k init.1 example.aggregation.peakdistributioninitializer 17 init.1.protocol 1 18 init.1.value control.ob0 aggregation.averageobserver 21 control.ob0.protocol 1! Lines 12-14: protocol 0 initialization! Lines 16-18: protocol 1 initialization! Lines 20-21: protocol 1 observer On the ReadMe file in the Peersim home directory there are the instructions for running the example

6 Peersim Scheduler! Hence a Peersim simulation is scheduled in this way: Initializers execution while (time < cycles) for each node in the Network CDProtocol actions are executed end foreach Control actions are executed end while Implementing AverageFunction 01. public void nextcycle(node node, int protocolid) { 02. int linkableid = FastConfig.getLinkable(protocolID); 03. Linkable linkable = (Linkable) node.getprotocol(linkableid); 04. if (linkable.degree() > 0) { 05. Node peer = linkable.getneighbor(commonstate.r.nextint(linkable.degree())); // Failure handling 08. if (!peer.isup()) return; AverageFunction neighbor = (AverageFunction)peer.getProtocol(protocolID); 11. double mean = (this.value + neighbor.value) / 2; 12. this.value = mean; 13. neighbor.value = mean; } } SLAC: Introduction SLAC (Selfish Link and behavioural Adaptation to produce Cooperation)! SLAC is an evolutionary algorithm whose aim is to promote Cooperation in P2P systems! Inspired by Computational Sociology (Tag systems)! Tested with the Prisoner s Dilemma Simple 2 player game Represents contradiction between selfishness of the players and global benefit

7 The Prisoner s Dilemma (PD) Player Strategy DC CC DD CD! D = Defect; C = Cooperate! PD Constraints: DC > CC > DD > CD 2CC > DC+CD Player What are Tags?! Studied by computational sociology (Holland 1992)! Tags are observable markings (hair, clothes )! Tags evolve just like any other artificial trait! Limiting interactions between agents with similar tags leads to cooperative altruistic behaviour Tag Systems agents! Each agent in a Tag system is identified by a Tag (it may be a set of bits)! Each agent has a behaviour (it may simply be to cooperate or defect)! Each agent has an utility! Mutation of the tag: Changing some traits (i.e. some bits) Mutation - Selfish optimization: agents copy tags and behaviour of those performing better (with higher utility) - Periodically tags and strategies are mutated with a certain probability! Mutation of the strategy: Flipping the strategy bit (from cooperation to defection or vice-versa)

8 Tag systems evolutionary algorithm LOOP some number of generations LOOP for each agent (a) in the population Select a game partner agent (b) Agent (a) and (b) invoke their strategies receiving the appropriate payoff END LOOP Reproduce agents in proportion to their average payoff Apply mutation to tag and strategy of each reproduced agent with low probability END LOOP From Tags to Networks (P2P)! Agents are nodes! Tags are represented by the node s view (we consider this as a kind of tag) Interaction is between neighbors! Behaviour defined by the application File sharing (share files or free riding) Prisoner s Dilemma (cooperate or defect)! Utility evaluated at application level File sharing: number of files downloaded PD: outcome given by the payoffs matrix SLAC! SLAC is an attempt to move tags in P2P networks! Nodes performs some application task (file sharing, PD) and get some utility! The nodes move to find better neighbors The links and the strategy of the nodes with better performances (higher utility) are copied The topology of the network evolves! Groop-like selection between clusters of nodes Groups of good guys spread Selfish nodes become isolated SLAC Pseudocode Each node i periodically with a certain probability: j := selectrandompeer(); if U j >U i drops all its current links links to node j and copy j s strategy and links mutate (with low probability) links and strategy end if Mutation: i drops all its links and connects to a random node Random Peer given by Newscast

9 Copy and Rewire - Mutation SLAC simulations! SLAC has been implemented in Peersim on top of Newscast (random sampling for utility comparison)! The abstract application used for testing SLAC is the PD Each node has a strategy (C / D) At each Peersim cycle nodes play a single round with a random neighbor! At the end of each cycle, SLAC is invoked SLACER (I) SLACER (II)! SLACER is a variant of SLAC! In SLAC when a node moves, it first drops all its link! In SLACER when a node moves, it drops its links with a certain probability W! If W = 1, then SLACER == SLAC! Drop probability value leads to different kind of topology: W = 1, highly partitioned network (disconnected); W = 0.9, SmallWorld-like network Low W, random network (more robust, less cooperation)

10 Three protocol: Newscast, SLACER, PDapp SLAC(ER) architecture #random.seed simulation.cycles 300 control.shf Shuffle network.size 4000 Configuration File (I) General settings Cooperative neighbor Random node (movement) 2. PDapp (Application) 1. SLACER 0. Newscast (Peer Sampling Service) utility protocol.0 example.newscast.simplenewscast protocol.0.cache 20 protocol.1 slacer.slacer protocol.1.degree 20 protocol.1.linkable 0 protocol.1.application 2 protocol.1.reprprob 0.20 protocol.1.mutation protocol.1.tagmutation 0.01 protocol.2 slacer.pdapp.pdprotocol protocol.2.linkable 1 Protocols settings init.0 peersim.dynamics.wirekout init.0.protocol 0 init.0.k 20 init.1 slacer.slacerinitializer init.1.protocol 1 init.1.isolateprob 1 init.1.copyprob 1 init.2 slacer.pdapp.pdstrategyinitializer init.2.protocol 2 init.2.quantity 0 Configuration File (II) Control settings SLAC experiments (I) init.3 peersim.dynamics.wirekout init.3.protocol 1 init.3.k 20 control.ob0 slacer.booleanstrategyobserver control.ob0.protocol 2 control.ob1 slacer.pdapp.pdobserver control.ob1.protocol 2 Cycles=4000; ReprRate=0.20; Single run

11 SLAC experiments (II) Internet resources Cycles for high cooperation (93%)! Peersim resources page: These slides on: Other similar slides: For any questions: References! Hales, D. and Arteconi, S. (2006) Slacer: A self-organizing protocol for coordination in p2p networks. IEEE Intelligent Systems! Jelasity, M., Montresor, A. and Babaoglu, O. (2005) Gossip-based aggregation in large dynamic networks. In ACM Transaction on Computer Systems (TOCS). Volume 23, Issue 3! Marcozzi, A.; Hales, D.; Jesi, G.; Arteconi, S.; Babaoglu, O. (2005) Tag-Based Cooperation in Peer-to-Peer Networks with Newscast. In Self- Organization and Autonomic Informatics (I), Volume 135 Frontiers in Artificial Intelligence and Applications, Edited by: H. Czap, R. Unland, C. Branki and H. Tianfield, IOS Press, Netherlands - Some of them available as UBLCS technical report at: