Biologically-Inspired Design Patterns for Distributed Computing Systems

1. Design Patterns from Biology for Distributed Computing Andres J. Ramirez

2. Paper Information • Authors • Babaoglu, Canright, Di Caro, et al. (about 11 different dudes.) • Published • ACM Transactions on Autonomous and Adaptive Systems, Vol 1, No 1, September 2006.

3. Presentation Outline • What is a design pattern? • Current challenges in designing software systems. • Parallelism to biological systems. • Presentation of design patterns extracted from biological systems. • Experimentation and validation. • Conclusion.

4. What is a design pattern? • Various definitions proposed: • “Each pattern describes a problem which occurs over and over again in our environment, and then describes the core of the solution to that problem, in such a way that you can use this solution a million times over, without ever doing it the same way twice.” [Chris Alexander] • “Each design pattern systematically names, explains and evaluates an important and recurrent design in object-oriented system.” [Gamma et al.] • “A recurring solution to a standard problem.” [Schmidt] • Overall, most are rather similar.

5. Design Pattern Presentation • Bare minimum format: • Pattern name. • Problem description. • Solution to the problem. • Consequences of applying the design pattern.

6. Current Challenges • Distributed environments are common place now • Extremely dynamic. • Unreliable. • Large scale. • Traditional approaches for designing distributed systems are not applicable.

7. Biological Systems • Effectively organize large numbers of unreliable and dynamically changing components (cells, molecules, individuals) into structures that implement a wide range of functions. • These structures exhibit: • Robustness to failure. • Adaptability to changing conditions. • Lack of reliance on an explicit central coordinator.

8. Why patterns from biology? • Biological entities evolve to solve a particular problem, usually related to survival issues. • This solution, by the notion of evolution, must be well tested and reliable to be in existence today. • Similarities exist between distributed computing systems and biological systems. • Solutions from one domain can transfer onto the other.

9. Key Idea • Abstract design patterns from biological systems and apply them in distributed systems. • Serve as a bridge between biological systems and computer systems. • How do they accomplish this? • Formulate the patterns as local communication strategies over arbitrary communication topologies.

10. Design Pattern Presentation in Paper • Name • Handle for the pattern. Key. • Context • Defined by the system model (more in a bit.) • Problem • Possible functionality we are trying to achieve.

11. Design Pattern Presentation in Paper • Solution • An algorithm which produces the desired output based on the problem. • Example • Sort of a case study. • Design Rationale • The inspiration from biology.

12. System Model • Basic Abstraction • Network where nodes communicate via message passing. • Additional Assumptions • Basic components are nodes. • Computing devices which maintain states and perform computations. • Neighbors • Only “visible” neighbors can send messages to each other. • Asynchronous message passing • No message delivery time bound.

13. System Model • Nodes are unreliable • Nodes may fail. • Can leave and join at any time. • Communication mediums are unreliable • Messages can be lost. • Side note: No mention of corrupted message passing? • Three Generals Problem does not seem to be addressed. • Maybe animals are more trustworthy than humans?

14. Topology Issues • The topology here is given by the graph defined by the neighbor relation. … typical topology definition from graph theory. • Two particular networks seen in this work: • Overlay Networks • Mobile Ad Hoc Networks (MANETs)

15. Overlay Networks • Promising paradigm for building applications over large-scale wide-area networks. • Service Clouds is an example. • Logical structures built on top of a physical network with a routing service. • Any node can send to any other node granted it knows the target nodes network address.

16. Mobile Ad Hoc Networks • Set of wireless mobile devices which self-organize into a network without relying on a fixed infrastructure. • All nodes are treated equal. • Neighbor relations are dependent on the wireless connections between nodes. • Defined by transmission power and physical proximity.s

17. The actual Design Patterns • Plain Diffusion • Replication • Stigmergy • Chemotaxis (composite) • Reaction Diffusion (composite)

18. Plain Diffusion • Problem: • Bring the system to a state where each node contains the average value of all the values in the system. • Assign a gradient to each link that is proportional to the change in values when following the link. • Solution: • Rely on message passing. • For each link, each node periodically subtracts a fixed proportion from its current value and sends it along the given link. On the receiving side, add the message to current value.

19. Plain Diffusion • Solution presented maintains the sum of all the values in the system constant. • All the node values will quickly approach the average value. • Gradients are generated in this process.

20. Plain Diffusion • Design Rationale • A form of diffusion. • Equalizing the concentration of some substance or some abstract quantity like heat. • Present in many biological and physical systems. • Known to be efficient at convergence. This will be important when testing in a distributed environment.

21. Replication • Problem: • Propagate novel information to all other nodes. • Assign the maximal value present in the network to all the nodes. • Find a node which contains a document matching a given query.

22. Replication • Solution: • Nodes receive messages from neighbors and forward them according to application specific rules. • Flooding is an easy but expensive example. • Messages can stand for the maximum value (thus solving problem 2) • Messages can stand for the query until a match is found (thus solving problem 3)

23. Replication • Design Rationale: • Replication is common place in nature • Growth processes, signal propagation in certain neural networks, epidemic spreading. • Messages can be seen as infective agents which propagate through the system invading hosts (nodes.)

24. Stigmergy • Problem: • Assuming that the links between nodes have weights attached, find the shortest path between two given nodes. • Nodes need not be directly connected. • Redistribute items found in one node over a small number of nodes where similar items are held at the same node. • Does not really address when all the items are the same? Does it even matter?

25. Stigmergy • Solution: • Let every node contain a set of variables called stigmergic variables. • Nodes generate messages and send and received these based on application dependent policies. • Reception of a message will trigger an action. • Defined by the message itself and the stigmergic variables of the node. • Stigmergic variables are updated and then the message (also updated) is forwarded. • Essentially, distributed reinforcement learning.

26. Stigmergy • In the first problem, the estimated cost for a particular path is represented by the stigmergic variables. • As it progresses, the variables are updated with more exact costs. • In the second problem, clusters form by assigning items to the messages and determining whether the message is forwarded or not based on the stigmergic variables.

27. Stigmergy • Design Rationale: • Typically seen in distributed self-organizing behaviors in diverse social systems. • Nest building, labor division, path finding. • Classic example, ants.

28. Chemotaxis • Note: Composite pattern based on plain diffusion. • Problem: • Finding a short path from a given node to regions of the network where the concentration of a diffusive substance Is maximal • Does not seem to incorporate finding the shortest path?

29. Chemotaxis • Solution: • Just follow the maximal gradient. • Start at any given node • Select link with highest gradient • Repeat until local maximum concentration is found. • Greedy Algorithm! Not necessarily the shortest path, and not necessarily where the highest diffusive substance is found.

30. Chemotaxis • Design Rationale: • Cells or organisms might direct their movements according to the concentration gradients of one or more chemicals in the environment. • Responsible for the development of certain multicellular organisms and pattern formations.

31. Reaction-Diffusion • Not a pattern, a framework covering a large set of patterns. • A strong generalization of the plain diffusion pattern • Simultaneous diffusion of one or more materials. Also removal. • Nothing else on this framework, pattern, etc.

32. Evaluating Design Patterns • Insensitivity: • Self-repairing • Self-organizing • Adaptive • Intelligent • Quantifying the notion of good and bad in a sense of merit. • Dependent on too many things, domain specific, not perfectly defined. • Insensitive systems show little variation in the figure of merits as the environment varies.

33. Evaluating Plain Diffusion • Distributed Aggregation Problem • Calculating global functions over the set of locally known quantities. • We saw these problems earlier. • Simplify the task of controlling, monitoring and optimizing distributed applications, among other things. • Building block for other patterns. • In the paper, the average is found.

34. Evaluating Plain Diffusion • Algorithm: • Each node p has two threads, active and passive. • Active thread: periodically initiates an information exchange with peer node q selected at random. Message contains state of p. • Passive thread: waits for a message and replies with the local state. • Symmetric information exchange, constant update of values sent and received. • The update is defined by what the problem is trying to solve. In this example, take the average of the two messages. • Could also do a maximum, etc.

35. Evaluating Plain Diffusion • How good is this solution? • Value at each node will converge to the true global average. • IF the underlying overlay network remains connected. • Just how fast does it converge? • Exponential. • Very high precision estimates are achieved in a few cycles regardless of network size. • It is scalable!

36. Evaluating Plain Diffusion • Simulation done on PeerSim. • Count protocol -> number of nodes in the network. • Average calculation over a starting set of numbers. • One node has value 1, rest 0. Obtain? • 1/N. • Why do this? • Very sensitive to failures. • Tests scalability and robustness.

37. Evaluating Plain Diffusion • Converged to a specific value exponentially, as predicted. • What about failures? • If crashed node has a smaller value than the actual global average, estimated average will increase. • N will decrease. • Opposite case? Opposite results. • Crashes have the most impact in the first few iterations. • Churn? Adding and removing nodes (N remains constant though.) • Estimates still reliable.

38. Evaluating Replication • Distributed Search. • Idea is to spread queries throughout nodes. • Typical, simple, stupid solution? • Flood the network. • Clone the queries received at a node and propagate to all neighbors. • Huge overhead. • Opposing objectives. Higher efficiency vs lower overhead. • Can we do better?

39. Evaluating Replication • Design the algorithm for an unstructured overlay network. • No relation between the information stored at a node and its position in the overlay network. • Learn from proliferation • Replication strategy inspired by the immune system. • Basically acts as a rate limit on propagated messages. • B cells, after being stimulated by an antigen, proliferate generating antibodies. • After this, basically a gang of antibodies do several drive-bys on the antigens and you are no longer sick!

40. Evaluating Replication • Treat the query as the antibody and the searched items as the antigens. • Search can be started at any node. • Send query messages to k neighbors. • Receive a message? • Calculate the similarity between query and local contents. • Higher the similarity, more messages sent out. • Only new neighbors.

41. Evaluating Replication • Restricted proliferation shown to be more effective than random walks. • Even though some fluctuations were present in the results, restricted proliferation performed roughly 50% better than restricted random walk. • Key notion? • Guiding message replication to areas of more promise yields better results.

42. No more! • I am sure I have bored you by now. • General experiment results of the remaining patterns exhibit better performance and insensitivity to traditional approaches seen in distributed computing. • Want some more specifics, look at the paper. • You did do that already, right? • Good.

43. Conclusions • Biological systems have evolved through millions of years to reach their current point. • Evolution happens for a reason, it is a search for a solution to survival. • We can extract some of this behavior and apply it with success to distributed computing systems. • Great amounts of parallelism between the two.

44. Conclusions • Solutions are not perfect, but they are good. • Few patterns extracted, certainly more are possible. • Translate ideas from large, varied and seemingly unrelated systems into one language • Applicable to our domain.