Grid Resource Management by means of Ant Colony Optimization

1. Grid Resource Management by means of Ant Colony Optimization Gustavo Sousa Pavani and Helio Waldman Optical Networking Laboratory – OptiNet Communications Department – Decom School of Electrical and Computer Engineering – FEEC State University of Campinas – UNICAMP. http://www.optinet.fee.unicamp.br

2. Introduction • Resource management in grids is a very challenging task, since the resources to be shared are very heterogeneous: CPU time, storage space or data, and network bandwidth • Need for networks that are dynamically configurable in order to support large transfers of data • Use of a GMPLS control plane for managing network resources • Use of the Ant Colony Optimization meta-heuristics to tackle the problem of grid scheduling, by the adaptation of the AntNet framework, which is a ACO-based algorithm, to this problem • Use of ant-like mobile agents to act on behalf of the user to make scheduling decisions • Considered the allocation of processing power and provisioning of lightpaths Gustavo Sousa Pavani

3. Proposed Algorithm – ACO • ACO is used to refer to the class of algorithms that are inspired in the process of foraging for food by natural ants for the optimization of hard-to-solve problems • It is characterized by ant-like mobile agents that cooperate and stochastically explore a network, iteratively building solutions based on their own memory and on the traces (pheromone levels) left by other agents • The AntNet framework is an ACO-based algorithm used for routing of packets in telecommunication networks • The original AntNet framework uses the delay introduced by each hop as the metric for routing. This is not applicable in circuit-switching networks, where the main metric is generally the number of hops. Indeed, the minimization of the number of hops of a connection following some criteria is a very good heuristics to reduce the overall blocking probability in a network Gustavo Sousa Pavani

4. Proposed Algorithm – Outline • At regular intervals, a forward ant is launched from a random source node to another random destination node. In its trip, the forward ant will select the next hop i using a random scheme that accounts the path selection probabilities, given by the pheromone levels in each neighbor link, and a heuristics value, calculated from the congestion of each neighbor links • During its trip, the forward ant gathers the label of each node where it passes by, putting it in its memory. When it arrives at the destination node, it becomes a backward ant, collects the availability information and returns to the source node using the same path followed by the forward ant, but in the opposite direction. At each intermediate node i, it updates the routing table, comparing its memory with the local parametric model • If the ant does not reach its destination node in a number of pre-established hops it is dropped. This avoids lost ants circulating forever in the network • In a hop-by-hop basis, the lightpath is routed evaluating the entry of the pheromone routing table that matches the destination. The neighbor with the higher level of pheromone is chosen as the next hop Gustavo Sousa Pavani

5. Proposed Algorithm – Adaptations to AntNet Data Structures 1 • At each node i, we have the following data structures: • Pheromone-routing table Ti: It is a matrix containing a row for each destination of the network and a column for each neighbor node, for storing the pheromone values. The sum of each row must be equal to 1 • Example of the pheromone routing table of node 4: Neighbor Nodes 1 2 5 Destinations 3 4 Gustavo Sousa Pavani

6. Proposed Algorithm – Adaptations to AntNet Data Structures 2 • Statistical parametric model Mi: It is a matrix containing the triplet <d,d,Ed> for each destination d of the network. These values are updated using an exponential model within the non-sliding window of w observations • Example of the statistical parametric model of node 4: Neighbor Nodes 1 2 5 Destinations 3 4 Gustavo Sousa Pavani

7. Proposed Algorithm – Adaptations to AntNet Data Structures 3 • Availability vector Ai: It is a vector containing an availability metric for each destination of the network. The rationale behind this structure is to allow for the load-balancing of the server farms • Example of the availability vector of node 4: 1 2 5 Destinations 3 4 Gustavo Sousa Pavani

8. Proposed Algorithm – Routing of Forward Ants • Congestion level for each neighbor link: • Probability of a forward ant to choose the neighbor node: •  gives the emphasis between pheromone level (long-term memory) and instantaneous congestion state (short-term memory) Gustavo Sousa Pavani

9. Proposed Algorithm – Updating by Backward Ants 1 • At each intermediate node i through source node, the backward ant updates the parametric model Miand routing table Ti of the intermediate nodes k [(i+1),d[ , i.e., it treats these nodes as destinations. However, this is done only if the evaluation of sub-path traced by the ant is good enough, i.e., its number of hops is less than the superior estimate Isup. This allows for the updating of good paths found by ants that are not intended to those destinations. The update is always done for the destination node d at i Gustavo Sousa Pavani

10. Proposed Algorithm – Updating by Backward Ants 2 • First, the parametric model Mik is updated. After, an adaptive reinforcement r is calculated for updating the routing table: • The obtained r is limited to 0.9 to avoid stagnation and its value is “squashed” • Now, if the neighbor node m is on the path, then it receives a positive reinforcement: • The other nodes receives a negative reinforcement: Gustavo Sousa Pavani

11. Proposed Algorithm –Loop Avoidance and Management • Memory of the forward ant works as tabu list for selecting the next hop • Dead-end: all neighbors of the node where the forward ant is being processed are already visited • Ignore the heuristic correction given by the congestion information • Use only pheromone values • Remove all nodes that belongs to the cycle from the ant’s memory Gustavo Sousa Pavani

12. PATH PATH Proposed Algorithm – Path Computation Calculated hop-by-hop! A D B C Gustavo Sousa Pavani

13. NSFNet Network • 14 nodes and 21 bi-directional links • Node 1 as user, other nodes as resources 10 14 1 11 13 4 8 2 5 7 12 3 9 6 Gustavo Sousa Pavani

14. Simulation • Homogeneous Poissonian traffic with uniform spatial profile • Node 1 as the only source of lightpath requests • Fixed duration of the lightpath: 14.4 sec (equivalent to a 1.8 GB file transfer at a 10 Gbps rate) • Fixed processing time of each job: 12 hours • Homogeneous processors • Same number of processors per node • First-fit wavelength assignment algorithm • Blocking can be caused by lack of resources on the: • Optical network • Server farm Gustavo Sousa Pavani

15. Results – Blocking Probability Gustavo Sousa Pavani

16. Results – Total Grid Workload • The optical network can limit the total grid workload (W = 2 and 4) for higher loads Load = 4 Erlangs W = 8 Gustavo Sousa Pavani

17. Results – The Effect of the Ant Launching Rate • The increase on the ant launching rate can enhance the system performance in terms of blocking probability Gustavo Sousa Pavani

18. Results – The Average Overhead on Control Channels due to the Ants Gustavo Sousa Pavani

19. Conclusions • Alternative for grid scheduling when the optical network resources have to be dynamically controlled. • Integrated management of processing and network resources. Load balancing of the: • Processing workload • Optical network • Use of a GMPLS control plane for lightpath provisioning • Advantages of the proposed algorithm over link-state protocols • Some scenarios proposed where the performance of the grid was limited by either networking resources or processing power Gustavo Sousa Pavani

20. Any questions? pavani@decom.fee.unicamp.br http://www.optinet.fee.unicamp.br/~pavani Gustavo Sousa Pavani