Real-Time Graph Analysis for Time-series Linked Data
High Performance GraphDB for Big Linked Data
Exact-Differential Cloning of Simulation
Benchmark Suit for Large-Scale Traffic Simulation
Analyzing large-scale traffics by simulation needs repeating execution many times with various patterns of scenarios or parameters. Such repeating execution brings about big redundancy because the change from a prior scenario to a later scenario is very minor in most cases, for example, blocking only one of roads or changing the speed limit of several roads. We propose a new redundancy reduction technique, called exact-differential simulation, which enables to simulate only changing scenarios in later execution while keeping exactly same results as in the case of whole simulation.
Large-Scale Agent-Based Traffic Simulation on Cloud
Using the Cloud for large-scale distributed simulations, such as agent-based traffic simulations, sounds like a good idea, as it is possible to provision and release easily processing nodes (e.g., virtual machines) in the Cloud. However, the question is complex as it involves users’ objectives, such as, time to process the simulation and cost of the simulation, and because the workload evolves in distributed simulations, in each node and the whole system, and this impact the resource provisioning plans.
This paper proposes two main contributions: (i) a method for efficient utilization of computational resources for distributed agent-based simulations, providing a mechanism that adapts the resource provisioning to users’ objectives and workload evolution; and (ii) a staged asynchronous migration technique to limit the migration overhead when the number of workers change. Our preliminary experimental results on a 24 hour scenario of traffic in the city of Tokyo show that our system outperforms a static provisioning by 12% in average and 23% during periods when workload changes a lot.
Large-Scale P2P Network Simulation
There have been P2P systems with simultaneous millions of nodes on Internet. However existing simulators and techniques cannot simulate such a large scale. Even parallelized simulators have not solved the scale problem. They provide large memory and hold a large number of nodes but the speed of simulation degrades significantly than sequential simulation due to much overhead of inter-server synchronization. We propose a simulation technique for large-scale P2P systems based on an optimistic parallel discrete event simulation model. The technique employs low cost synchronization techniques that are effective for P2P simulation.