On the Importance of Bandwidth Control Mechanisms for Scheduling on Large Scale Heterogeneous Platforms

We study three scheduling problems (file redistribution, independent tasks scheduling and broadcasting) on large scale heterogeneous platforms under the Bounded Multi-port Model. In this model, each node is associated to an incoming and outgoing bandwidth and it can be involved in an arbitrary number of communications, provided that neither its incoming nor its outgoing bandwidths are exceeded. This model well corresponds to modern networking technologies, it can be used when programming at TCP level and is also implemented in modern message passing libraries such as MPICH2. We prove, using the three above mentioned scheduling problems, that this model is tractable and that even very simple distributed algorithms can achieve optimal performance, provided that we can enforce bandwidth sharing policies. Our goal is to assert the necessity of such QoS mechanisms, that are now available in the kernels of modern operating systems, to achieve optimal performance. We prove that implementations of optimal algorithms that do not enforce prescribed bandwidth sharing can fail by a large amount if TCP contention mechanisms only are used. More precisely, for each considered scheduling problem, we establish upper bounds on the performance loss than can be induced by TCP bandwidth sharing mechanisms, we prove that these upper bounds are tight by exhibiting instances achieving them and we provide a set of simulations using SimGRID to analyze the practical impact of bandwidth control mechanisms

On the Importance of Bandwidth Control Mechanisms for Scheduling on Large Scale Heterogeneous Platforms

International audienceWe study three scheduling problems (file redistribution, independent tasks scheduling and broadcasting) on large scale heterogeneous platforms under the Bounded Multi-port Model. In this model, each node is associated to an incoming and outgoing bandwidth and it can be involved in an arbitrary number of communications, provided that neither its incoming nor its outgoing bandwidths are exceeded. This model well corresponds to modern networking technologies, it can be used when programming at TCP level and is also implemented in modern message passing libraries such as MPICH2. We prove, using the three above mentioned scheduling problems, that this model is tractable and that even very simple distributed algorithms can achieve optimal performance, provided that we can enforce bandwidth sharing policies. Our goal is to assert the necessity of such QoS mechanisms, that are now available in the kernels of modern operating systems, to achieve optimal performance. We prove that implementations of optimal algorithms that do not enforce prescribed bandwidth sharing can fail by a large amount if TCP contention mechanisms only are used. More precisely, for each considered scheduling problem, we establish upper bounds on the performance loss than can be induced by TCP bandwidth sharing mechanisms, we prove that these upper bounds are tight by exhibiting instances achieving them and we provide a set of simulations using SimGRID to analyze the practical impact of bandwidth control mechanisms

DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car

We present DeepPicar, a low-cost deep neural network based autonomous car platform. DeepPicar is a small scale replication of a real self-driving car called DAVE-2 by NVIDIA. DAVE-2 uses a deep convolutional neural network (CNN), which takes images from a front-facing camera as input and produces car steering angles as output. DeepPicar uses the same network architecture---9 layers, 27 million connections and 250K parameters---and can drive itself in real-time using a web camera and a Raspberry Pi 3 quad-core platform. Using DeepPicar, we analyze the Pi 3's computing capabilities to support end-to-end deep learning based real-time control of autonomous vehicles. We also systematically compare other contemporary embedded computing platforms using the DeepPicar's CNN-based real-time control workload. We find that all tested platforms, including the Pi 3, are capable of supporting the CNN-based real-time control, from 20 Hz up to 100 Hz, depending on hardware platform. However, we find that shared resource contention remains an important issue that must be considered in applying CNN models on shared memory based embedded computing platforms; we observe up to 11.6X execution time increase in the CNN based control loop due to shared resource contention. To protect the CNN workload, we also evaluate state-of-the-art cache partitioning and memory bandwidth throttling techniques on the Pi 3. We find that cache partitioning is ineffective, while memory bandwidth throttling is an effective solution.Comment: To be published as a conference paper at RTCSA 201

A WOA-based optimization approach for task scheduling in cloud Computing systems

Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this work, for the first time, we apply the latest metaheuristics WOA (the whale optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called IWC (Improved WOA for Cloud task scheduling) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks