Running real time distributed simulations under Linux and CERTI

This paper presents some experiments and some results to enforce real time distributed simulations in accordance with the High Level Architecture (HLA). Simulations were run by using CERTI, an open source middleware, as the Run Time Infrastructure (RTI). Models were distributed over computers under various available versions of the 2.6 Linux kernel. Studies and experiments relied on a real case study. The chosen case study was the simulation of an "in formation" flight of observation satellites. This case study brings up some real applicative needs in real time distributed simulations and real configurations of simulators and models. Two simulations of "in formation" flight of satellites were studied. The study consisted in modeling the behaviour of the simulators and in running these models by using various kernel or middleware operating mechanisms and services. Time measurements were performed at each test giving some results on the ability of the simulation to meet its real time requirements

Limitations and Solutions for Real-Time Local Inter-Domain Communication in Xen

As computer hardware becomes increasingly powerful, there is an ongoing trend towards integrating complex, legacy real-time systems using fewer hosts through virtualization. Especially in embedded systems domains such as avionics and automotive engineering, this kind of system integration can greatly reduce system weight, cost, and power requirements. When systems are integrated in this manner, network communication may become local inter-domain communication (IDC) within the same host. This paper examines the limitations of inter-domain communication in Xen, a widely used open-source virtual machine monitor (VMM) that recently has been extended to support real-time domain scheduling. We find that both the VMM scheduler and the manager domain can significantly impact real-time IDC performance under different conditions, and show that improving the VMM scheduler alone cannot deliver real-time performance for local IDC. To address those limitations, we present the RTCA, a Real-Time Communication Architecture within the manager domain in Xen, along with empirical evaluations whose results demonstrate that the latency of communication tasks can be improved dramatically from ms to μs by a combination of the RTCA and a real-time VMM scheduler

Algorithms for Hierarchical and Semi-Partitioned Parallel Scheduling

We propose a model for scheduling jobs in a parallel machine setting that takes into account the cost of migrations by assuming that the processing time of a job may depend on the specific set of machines among which the job is migrated. For the makespan minimization objective, the model generalizes classical scheduling problems such as unrelated parallel machine scheduling, as well as novel ones such as semi-partitioned and clustered scheduling. In the case of a hierarchical family of machines, we derive a compact integer linear programming formulation of the problem and leverage its fractional relaxation to obtain a polynomial-time 2-approximation algorithm. Extensions that incorporate memory capacity constraints are also discussed

Structure-Aware Dynamic Scheduler for Parallel Machine Learning

Training large machine learning (ML) models with many variables or parameters can take a long time if one employs sequential procedures even with stochastic updates. A natural solution is to turn to distributed computing on a cluster; however, naive, unstructured parallelization of ML algorithms does not usually lead to a proportional speedup and can even result in divergence, because dependencies between model elements can attenuate the computational gains from parallelization and compromise correctness of inference. Recent efforts toward this issue have benefited from exploiting the static, a priori block structures residing in ML algorithms. In this paper, we take this path further by exploring the dynamic block structures and workloads therein present during ML program execution, which offers new opportunities for improving convergence, correctness, and load balancing in distributed ML. We propose and showcase a general-purpose scheduler, STRADS, for coordinating distributed updates in ML algorithms, which harnesses the aforementioned opportunities in a systematic way. We provide theoretical guarantees for our scheduler, and demonstrate its efficacy versus static block structures on Lasso and Matrix Factorization