Recent research, such as the Active Virtual Network Management Prediction (AVNMP) system, aims to use simulation models running ahead of real time to predict resource demand among network nodes. If accurate, such predictions can be used to allocate network capacity and to estimate quality of service. Future deployment of active-network technology promises to complicate prediction algorithms because each "active" message can convey its own processing logic, which introduces variable demand for processor (CPU) cycles. This paper describes a means to augment AVNMP, which predicts message load among active-network nodes, with adaptive models that can predict the CPU time required for each "active" message at any activenetwork node. Typical CPU models cannot adapt to heterogeneity among nodes. This paper shows improvement in AVNMP performance when adaptive CPU models replace more traditional non-adaptive CPU models. Incorporating adaptive CPU models can enable AVNMP to predict active-network resource usage farther into the future, and lowers prediction overhead. INTRODUCTIONGrowing availability of processing power and bandwidth in communication networks encourages innovative approaches to network management. One specific innovative idea envisions injecting simulation models into network nodes, and then running those models in parallel with the operational network, but ahead in time, in order to predict traffic and resource use. If the models predict accurately, then network management systems can better allocate capacity in anticipation of varying demands and network operators can better estimate the quality of service (QoS) that customers can expect. This paper describes one approach, the Active Virtual Network Management Prediction (AVNMP) system [1], which aims to predict network traffic. AVNMP uses active-network technology [2] to inject simulation models into network nodes, and to run those models concurrently with corresponding applications. AVNMP then compares estimated performance against measured
International audienceDistributing applications over PC clusters to speed-up or size-up the execution is now commonplace. Yet efficiently tolerating faults of these systems is a major issue. To ease the addition of checkpoint-based fault tolerance at the application level, we introduce a Model for Low-Overhead Tolerance of Faults (MoLOToF) which is based on structuring applications using fault-tolerant skeletons. MoLOToF also encourages collaborations with the programmer and the execution environment. The skeletons are adapted to specific parallelization paradigms and yield what can be called fault-tolerant algorithmic skeletons. The application of MoLOToF to the SPMD parallelization paradigm results in our proposed FT-SPMD framework. Experiments show that the complexity for developing an application is small and the use of the framework has a small impact on performance. Comparisons with existing system-level checkpoint solutions, namely LAM/MPI and DMTCP, point out that FT-SPMD has a lower runtime overhead while being more robust when a higher level of fault tolerance is required
Active Network technology envisions deployment of virtual execution environments within network elements, such as switches and routers. As a result, nonhomogeneous processing can be applied to network traffic associated with services, flows, or even individual packets. To use such a technology safely and efficiently, individual nodes must provide mechanisms to enforce resource limits. To provide effective enforcement mechanisms, each node must have a meaningful understanding of the resource requirements for specific network traffic. In Active Network nodes, resource requirements typically come in three categories: bandwidth, memory, and processing. Well-accepted metrics exist for expressing bandwidth (bits per second) and memory (bytes) in units independent of the capabilities of particular nodes. Unfortunately, no well-accepted metric exists for expressing processing (i.e., CPU time) requirements in a platformindependent form. This paper investigates a method to express the CPU time requirements of Active Applications (similar to distributed, mobile agents) in a form that can be meaningfully interpreted among heterogeneous nodes in an Active Network. The model consists of two parts: a node model and an application model. For modeling applications, the paper describes and evaluates a semi-stochastic state-transition model intended to represent the CPU usage requirements of Active Applications. Using measurement data, the general model is instantiated for two Active Applications, ping and multicast. The model instances are simulated, and the simulation results are compared against real measurements. For both Active Applications, the simulated and measured CPU time usage compare within 5% for the mean and the 90 th and 95 th percentiles. The 99 th percentiles compare within 7%. The paper also evaluates three different scaling factors that might be used to transform a model accurate on one node into terms that prove accurate on another node.
Multi-modeling and co-simulation are one of the solutions for dealing with complex systems. In this paper, we propose to apply the AA4MM framework to the co-simulation of a smart space heating "complex" environment with 2 objectives. Our first contribution is the development of AA4MM-FMI (Agent and Artifact for Multiple Models coordination for FMI). AA4MM is a modeling and simulation framework that can be used to implement the "master" in a multiple FMU (Functional Mock-up Unit) co-simulation of the FMI (Functional Mock-up Interface) standard with a fully decentralized view and intrinsic coordination. Our second contribution is to apply and evaluate this solution by creating a hierarchy of smart space models that are exploiting house geometry information. Each individual room is represented as an independent FMU taking inputs of the room's geometric information (surface area, volume, insulation, etc.), target temperature, and neighboring rooms. The neighboring rooms are connected to each other using the AA4MM-FMI framework. This project aims to couple an electrical heating-based simulation with networking event-based simulations to gain intuition for how house geometry affects efficient heating and network connectivity. Using this smart space house heating problem, we are able to test AA4MM-FMI (our novel "master") for FMI and multi-simulation.
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