The Organic Grid: Self- Organizing Computation on a Peer-to-Peer Network Presented by : Xuan Lin

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Transcript of The Organic Grid: Self- Organizing Computation on a Peer-to-Peer Network Presented by : Xuan Lin

  • Slide 1
  • The Organic Grid: Self- Organizing Computation on a Peer-to-Peer Network Presented by : Xuan Lin
  • Slide 2
  • Outline Introduction Motivation Organic Scheduling Scheme Experiment Evaluation Conclusion
  • Slide 3
  • Outline Introduction Motivation Organic Scheduling Scheme Experiment Evaluation Conclusion
  • Slide 4
  • Introduction Scientific Computations require large scale distributed computing. Traditional Grid vs. Desktop Grid Centralized vs. Decentralized Mobile agent. (Weak mobility, Strong mobility, Forced Mobility)
  • Slide 5
  • Outline Introduction Motivation Organic Scheduling Scheme Experiment Evaluation Conclusion
  • Slide 6
  • Motivation Many previous schemes assume reliable network. Centralized schemes suffer from poor scalability. Traditional scheduling schemes assume sufficient system information. Inspired by Local Activation, Long-range Inhibition (LALI)
  • Slide 7
  • Outline Introduction Motivation Organic Scheduling Scheme Experiment Evaluation Conclusion
  • Slide 8
  • Assumptions Independent-task application, data initially resides at one location. Each node initially has a friend lists.
  • Slide 9
  • A. General Approach Tree-structured overlay network is selected as the desirable pattern of execution. Empirically determined the simplest behavior that would organize the communication and task distribution among mobile agents. Augmented the basic behavior by introducing other desirable properties.
  • Slide 10
  • B. Basic Agent Behavior A computational task is encapsulated in an agent. A user starts the computation agent on his/her machine. (root of the tree) The agent starts one thread for computation. At the same time, the agent is prepared to receive requests.
  • Slide 11
  • B. Basic Agent Behavior (cont) -when get a request The agent dispatches a clone when get requests. (The requester will be a child). The clone will ask for its parent for subtasks.
  • Slide 12
  • B. Basic Agent Behavior (cont) -requester A thread begins to compute. Other threads are created-when required- to communicate with parents or other machines. If a requests is received, this child sends its own clone to the requester. It will become the parent of the requester. The requester will be a child of this node. Thus, the computation spreads.
  • Slide 13
  • B. Basic Agent Behavior (cont) An agent requests its parent for more subtasks if it completes its own subtasks. Every time a node obtain r results, it sends them to its parent.
  • Slide 14
  • B. Basic Agent Behavior (cont)
  • Slide 15
  • C. Maintenance of Child-lists Up to c active children and up to p potential children. (balance of deep and width of the tree) Active nodes are ranked by their performance (the rate the node sends result). Potential children are the ones which the current node has not yet been able to evaluate. A potential child is added to the active child-list once it has sent enough results to the current node.
  • Slide 16
  • C. Maintenance of Child-lists (cont) When the node has more than c active children, the slowest node (sc) will be kicked out. The sc is then given a list of other nodes, which it can contact to try and get back to the tree. The sc will also be put into a list which records o former children. (Avoid thrashing )
  • Slide 17
  • C. Maintenance of Child-lists (cont)
  • Slide 18
  • D. Restructuring of the Overlay Network Philosophy: Having best nodes close to the top enhances the extraction of subtasks from the root and minimizes the communication delay. The overlay network is constantly being restructured so that the nodes with the highest throughput migrate toward the root.
  • Slide 19
  • D. Restructuring of the Overlay Network (How to achieve that?) A node periodically informs its parent about its best-performing child.
  • Slide 20
  • D. Restructuring of the Overlay Network (cont) A sc is not simply discarded. The parent sends a list of its children in descending order of performance. The sc attempts to contact these nodes in turn.
  • Slide 21
  • E. Size of Result Burst R result-burst intervals r results (R+1)* r If r and R are too large, it will take too much time for the network to update.
  • Slide 22
  • F. Fault Tolerance What can we do when nodes lost connection? Every node keeps track of unfinished subtasks that were sent to children. Each node keeps a list of a ancestors.
  • Slide 23
  • F. Fault Tolerance (cont)
  • Slide 24
  • G. Cycles Failure could cause cycles. (How to find the cycle?) Every node checks its ancestor list on receiving it from its parents to see if itself is in the ancestor. (How to break the cycle?) Try to obtaining the address of some other agent on its data distribution or communication overlays.
  • Slide 25
  • G. Cycles (starvation) May cause starvation. If the agent is starved of work for more than a specified time, it self-destructs.
  • Slide 26
  • H. Termination Root sends out termination messages. The messages will spread down to leaves. Two scenarios: 1. If a node does not get such message, the situation will be the same as F. 2. n2 does not get the termination messages but it is in n1s friend-list. n1 terminate when it get informed. n2 will clone itself to n1 when it is informed by n1 ?????
  • Slide 27
  • I. Self-adjust of Task List Size In an ITA-type application, the utilization of a high-performance machine may be poor because it is only requesting a fixed number of subtasks at a time. So, agents request more or less according to its performance. (compare to last run) i(t), d(t)
  • Slide 28
  • J. Prefetching Motivation: A potential cause of slowdown in the basic scheduling scheme described earlier is the delay at each node due to its waiting for new subtasks. Using the self-adjustment function i(t) to prefetch. However, excessively prefetching will degrade the performance since prefetch will increase the amount of data that needs to be transferred at a time.
  • Slide 29
  • Outline Introduction Motivation Scheduling Scheme Experiment Evaluation Conclusion
  • Slide 30
  • Metric Total Computation Time Ramp-up Time The time required for subtasks to reach every single node. Topology Fast nodes should migrate to the root as close as possible.
  • Slide 31
  • Experiment Configuration Application: NCBIs nucleotide-nucleotide BLAST, the gene sequence similarity search tool. ( Match a 256KB sequence against 320 data chunks) A cluster of eighteen heterogeneous machines Introduced Delays in the application code. The machines ran the Aglets weak mobility agent environment on top of either Linux or Solaris.
  • Slide 32
  • Initial Topology
  • Slide 33
  • Initial Parameter
  • Slide 34
  • A. Comparison with Knowledge- Based Scheme
  • Slide 35
  • A. Comparison with Knowledge- Based Scheme (cont)
  • Slide 36
  • B. Effects of Child Propagation
  • Slide 37
  • B. Effects of Child Propagation (cont) 32% improvement in the running time
  • Slide 38
  • C. Result-Burst Size There is a qualitative improvement in the child- lists as the result-burst size increases. However, with very large result-bursts, it takes longer for the tree overlay to form and adapt, thus slowing down the experiment.
  • Slide 39
  • C. Result-Burst Size (cont)
  • Slide 40
  • Slide 41
  • D. Effects of prefetching Ramp-up Time is affected by prefetching and the minimum number of subtasks that each node requests.
  • Slide 42
  • D. Effects of prefetching (cont)
  • Slide 43
  • Slide 44
  • Slide 45
  • Prefecthing degrades the throughput when the No. of subtasks increases.
  • Slide 46
  • D. Effects of prefetching (cont)
  • Slide 47
  • E. Self-Adjustment
  • Slide 48
  • F. Number of Children
  • Slide 49
  • Two experiments: good initial configuration and star topology The total time are approximately the same. Children have to wait for a longer time for their requests to be satisfied.
  • Slide 50
  • Outline Introduction Motivation Scheduling Scheme Experiment Evaluation Conclusion
  • Slide 51
  • An autonomic scheduling algorithm in which multithreaded agents with strong mobility form a tree-structured overlay network. The approach can be adapted to many applications. Future work includes more experiments, and good design of initial friend-list.