Advanced Computer Networks: Part 2 Complex Networks, P2P Networks and Swarm Intelligence on Graphs

2 Contents Are they related to Computer Networks?  Complex Networks  P2P Networks  Swarm Intelligence on Graphs Scope of lectures Challenging topics  Direction of possible researches (Hot Issues)

3 Complex Networks Connection of Computer Networks

4 Complex Networks Realistic networks are Complex Networks  Biological Network: How the brain work efficiently?  Propagation Network: How viruses propagate through the computer?  Competitor network: How rumors spread out the human society?  Communication Network: How information transmission exchanges on the Internet ?

5 Biotech Industry in USA

6 Complex Networks What is a complex network?  Observes any form of user behavior Web surfing logs s transactions Communication over Blogs Friend lists Purchase history on e- commerce sites Any other kinds action that demonstrates user intent  It creates large scale graph from all this behavior data

7 Kinds of Networks Random Networks Small world networks Scale-free Networks

8 Complex Networks Graph representation of network  The network can be presented by a set V of nodes and a set E of edges, linking together as a graph denoted G=(V,E) Average path length  The distance between two nodes (dij) is equal to the total number of edges that connect through the shortest linkages  The average value of all distance over the network L is the average path length, N is the total number of nodes in the network

9 Complex Networks Degree and Degree distribution  Degree (undirected network): at node i, the number k i of edges connect to the k i edges of neighbor nodes The node of higher degree more significant influence than others in term of dynamics, information flows, data traffic  Degree distribution: a probability of a randomly picked node that have degree k is a constant determined by the given network Note: power-law distribution (logarithmic curve)

10 Complex Networks Betweenness centrality (BC)  It is a centrality measure of a vertex within a graph  The vertices that occur on many shortest paths between other vertices have higher betweenness value is the number of path between node i and j going through k is the number of path between node i and j

11 P2P Networks Could be One of Complex Networks? Application Views

12 P2P Networks Most of the traffic growth in the Internet is caused by P2P applications. P2P paradigm allows a group of computer users (employing the same networking software) to connect with each other to share resources.  Peers provide their resources such as processing power, disk storage, network bandwidth and files to be directly available to other peers.  They behave in a distributed manner without a central server.  As peers can act as both server and client then they are also called servent, which is different from the traditional client-server model.

13 P2P Networks P2P systems are adaptive network structures whose nodes can join and leave them autonomously.  Self-organisation, fault-tolerance, load balancing mechanisms and the ability to use large amounts of resources constitute further advantages of P2P systems.

14 P2P Networks At present, there are three-major architectures for P2P systems,  unstructured, hybrid and structured ones. Unstructured P2P systems  Gnutella, a node queries its neighbours (and the network) by flooding with broadcasts.  Unstructuredness supports dynamicity of networks, and allows nodes to be added or removed at any time.  Systems have no central index, but they are scalable, because flooding is limited by the messages’ time-to-live (TTL).  They allow for keyword search, but cannot guarantee a certain search performance.

15 P2P Networks Cluster-based hybrid P2P systems or hybrid P2P systems are a combination of fully centralised and pure P2P systems  Clustering represents the small-world concept because similar things are kept close together, and long distance links are added.  The concept allows fast access to locations in searching.  The most popular example for them is KaZaA. It includes features both from the centralized sever model and the P2P model.  Nodes with high storage and computing capacities are selected as super nodes.  The normal nodes (clients) are connected to the super nodes.  The super nodes communicate with each other via inter-cluster networks. In contrast, clients within the same cluster are connected to a central node.  The super nodes carry out query routing, indexing and data search on behalf of the less powerful nodes. Hybrid P2P systems provide better scalability than centralised systems, and show lower transmission latency (i.e. shorter network paths) than unstructured P2P systems.

16 P2P Networks Structured P2P systems, peers or resources are placed at specified locations based on specific topological criteria and algorithmic aspects facilitating search.  They typically use distributed hash table-based indexing.  Structured P2P systems have the form of self-organising overlay networks, and support node insertion and route look-up in a bounded number of hops. Chord, CAN, and Pastry are examples of such systems. Their features are load balancing, fault-tolerance, scalability, availability and decentralisation.

17 P2P Networks Content search  First, when searching with a specific keyword, the query message from the requesting node is repeatedly routed and forwarded to other nodes in order to look for the desired information.  Secondly, for advertisement-based search each node advertises its content by delivering advertisements and selectively storing interesting advertisements received from other nodes. Each node can locate the nodes with certain content by looking up its local advertisement repository. Thus, it can obtain such content by a one-hop search with modest search cost.

18 P2P Networks  Finally, for cluster-based search, nodes are grouped according to the similarity of their contents in clusters. When a client submits a query to a server, it is transmitted to all nodes whose addresses are kept by the server, and which may be able to provide resources possibly satisfying the query’s search criteria.

19 Information Search System Information Search Centralized System Decentralized System only CONTENT oriented search ! only CONTENT oriented search !

20 P2P Networks The content-based presentation of information in P2P networks has more benefits than the traditional client-server model.  Search effectiveness made possible.  The usually employed search method based on flooding works by broadcasting query messages hop-by-hop across networks. This approach is simple, but not efficient in terms of network bandwidth utilisation.  Distributed hash tables based search (DHT) is efficient in terms of network bandwidth, but causes considerable overhead with respect to index files.  DHT does not adapt to dynamic networks and dynamic content stored in nodes.  Exhibiting fault tolerance, self-organisation and low overhead associated with node creation and removal, conducting random walks is a popular alternative to flooding. Many search approaches in distributed search systems seek to optimise search performance. The objective of a search mechanism is to successfully return desired information to a querying user.

21 SI on Graphs How to monitor Complex Networks?

22 Swarm Intelligence on Graphs Swarm Intelligence  Ant colonies  Animal herding  Bird flocking  Fish schooling Algorithms  Ant colony optimization  Artificial immune systems  Particle swarm optimization

23 Swarm Intelligence on Graphs Multi-agent systems are very interesting on team coordination attracted by various researchers  not only engineering but also biological fields such as birds flocking, robotic swarming, schooling of fish, and so on. Important roles for group coordination are an information exchange and agent communication.

24 Swarm Intelligence on Graphs A major problem for cooperative control of agents,  it is aimed to design suitable protocols to reach a group consensus depending on their exchanged information. To make a group agreement,  consensus means all agents need to make a group decision or a group agreement depended on their shared state information. A consensus protocol is a communication rule for exchanging the state information between agent and its neighbors, as well as reaching consensus by distributed decision making.

25 Swarm Intelligence on Graphs  The discrete-time consensus protocol is described by  where x i denotes the information state of agent i, and 0<epsilon<1/Delta is a parameter, in which Delta is the maximum degree of network

26 Swarm Intelligence on Graphs  A group of agents is said to reach a global consensus if x j (t)=x i (t) for each pair (i,j), i,j =1, 2, …, n, and the common agreement value of all agents is called the group decision value  where w i is the left eigenvalue, x i (0) is the initial state, as well as  If an undirected graph trivially balanced

27 Scope of Lectures

28 Scope of Lectures: Week 1 Types of Topologies  How to create them? Network analysis  Degree and degree of distribution  Average path length  Betweeness Centrality Routing strategies

29 Scope of Lectures: Week 2 File sharing techniques  Gnutella  Napster  Freenet Content search and content replication  PageRank

30 Scope of Lectures: Week 3 Graph theory  Basic definitions  Connection of graphs  Matrices associated with Graph Consensus protocols SI Algorithms  Ant Colony Optimization  Particle Swarm Optimization

31 Directions of Recent Researches

32 Hot Issues: Complex Networks  Creating networks: Dynamic networks  Traffic flow models on complex network 1. How to reduce the congestion by the packet delivery capacity of each node is proportional to its degree 2. Packet delivery capacity of each node is proportional to the number of shortest path passing through the node  The shortest path is paths containing the smallest number of hops or links  Efficient Paths

33 Hot Issues: P2P Networks Creating networks: Dynamic networks Sharing files technology File replications Routing problems

34 Hot Issues: Swarm Intelligence on Graphs Swarm Intelligence Algorithms Multi-agent systems  Topology of the networked multi-agent system Direct/indirect communication techniques Consensus protocols  Wireless sensor networks Collective behaviors