Search

With automated driving forthcoming, lane-changing for Connected and Automated Vehicles (CAVs) has received wide attention. The main challenge is that lane-changing requires not only local CAV control but also interactions with the surrounding traffic. Nevertheless, the Line-of-Sight (LoS) sensing range of the CAVs imposes severe limitations on lane-changing safety, and the lane-changing decision that is made based only on self-interest ignores its impact on the traffic flow efficiency. To overcome these difficulties, this article proposes a Digital Twin (DT) empowered mobile edge computing (MEC) architecture. With MEC, the sensing and computing capabilities of the CAVs can be strengthened to guarantee real-time safety. The virtualization and offline learning capabilities of the DT can be leveraged to enable the CAVs to learn from the experience of the physical MEC network and make lane-changing decisions via a ‘foresight intelligent’ approach. A case study of lane-changing is provided where the DT is constituted by a cellular automata based road traffic simulator coupled with a LTE-V based MEC network simulator. Deep reinforcement learning is adopted to train the lane-changing strategy and results validate the effectiveness of our proposed architecture. [ABSTRACT FROM AUTHOR]

Scalability of distributed deep learning (DL) training with parameter server (PS) architecture is often communication constrained in large clusters. There are recent efforts that use a layer by layer strategy to overlap gradient communication with backward computation so as to reduce the impact of communication constraint on the scalability. However, the approaches could bring significant overhead in gradient communication. Meanwhile, they cannot be effectively applied to the overlap between parameter communication and forward computation. In this article, we propose and develop iPart, a novel approach that partitions communication and computation in various partition sizes to overlap gradient communication with backward computation and parameter communication with forward computation. iPart formulates the partitioning decision as an optimization problem and solves it based on a greedy algorithm to derive communication and computation partitions. We implement iPart in the open-source DL framework BigDL and perform evaluations with various DL workloads. Experimental results show that iPart improves the scalability of a cluster of 72 nodes by up to 94 percent over the default PS and 52 percent over the layer by layer strategy. [ABSTRACT FROM AUTHOR]

Cloud computing has a key requirement for resource configuration in a real-time manner. In such virtualized environments, both virtual machines (VMs) and hosted applications need to be configured on-the-fly to adapt to system dynamics. The interplay between the layers of VMs and applications further complicates the problem of cloud configuration. Independent tuning of each aspect may not lead to optimal system wide performance. In this paper, we propose a framework, namely CoTuner, for coordinated configuration of VMs and resident applications. At the heart of the framework is a model-free hybrid reinforcement learning (RL) approach, which combines the advantages of Simplex method and RL method and is further enhanced by the use of system knowledge guided exploration policies. Experimental results on Xen-based virtualized environments with TPC-W and TPC-C benchmarks demonstrate that CoTuner is able to drive a virtual server cluster into an optimal or near-optimal configuration state on the fly, in response to the change of workload. It improves the systems throughput by more than 30 percent over independent tuning strategies. In comparison with the coordinated tuning strategies based on basic RL or Simplex algorithm, the hybrid RL algorithm gains 25 to 40 percent throughput improvement. [ABSTRACT FROM PUBLISHER]

Cloud elasticity allows dynamic resource provisioning in concert with actual application demands. Feedback control approaches have been applied with success to resource allocation in physical servers. However, cloud dynamics make the design of an accurate and stable resource controller challenging, especially when application-level performance is considered as the measured output. Application-level performance is highly dependent on the characteristics of workload and sensitive to cloud dynamics. To address these challenges, we extend a self-tuning fuzzy control (STFC) approach, originally developed for response time assurance in web servers to resource allocation in virtualized environments. We introduce mechanisms for adaptive output amplification and flexible rule selection in the STFC approach for better adaptability and stability. Based on the STFC, we further design a two-layer QoS provisioning framework, DynaQoS, that supports adaptive multi-objective resource allocation and service differentiation. We implement a prototype of DynaQoS on a Xen-based cloud testbed. Experimental results on representative server workloads show that STFC outperforms popular controllers such as Kalman filter, ARMA and, Adaptive PI in the control of CPU, memory, and disk bandwidth resources under both static and dynamic workloads. Further results with multiple control objectives and service classes demonstrate the effectiveness of DynaQoS in performance-power control and service differentiation. [ABSTRACT FROM PUBLISHER]

Resource discovery is critical to the usability and accessibility of grid computing systems. Distributed Hash Table (DHT) has been applied to grid systems as a distributed mechanism for providing scalable range-query and multi-attribute resource discovery. Multi-DHT-based approaches depend on multiple DHT networks with each network responsible for a single attribute. Single-DHT-based approaches keep the resource information of all attributes in a single node. Both classes of approaches lead to high overhead. In this paper, we propose a Low-Overhead Range-query Multi-attribute (LORM) DHT-based resource discovery approach. Unlike other DHT-based approaches, LORM relies on a single compound graph-based DHT network and distributes resource information among nodes in balance by taking advantage of the compound graph structure. Moreover, it has high capability to handle the large-scale and dynamic characteristics of resources in grids. Experimental results demonstrate the efficiency of LORM in comparison with other resource discovery approaches. LORM dramatically reduces maintenance and resource discovery overhead. In addition, it yields significant improvements in resource location efficiency. We also analyze the performance of the LORM approach rigorously by comparing it with other multi-DHT-based and single-DHT-based approaches with respect to their overhead and efficiency. The analytical results are consistent with experimental results, and prove the superiority of the LORM approach in theory. [ABSTRACT FROM AUTHOR]

Understanding server capacity is crucial to system capacity planning, configuration, and QoS-aware resource management. Conventional stress testing approaches measure server capacity offline in terms of application-level performance metrics like response time and throughput. They are limited in measurement accuracy and timeliness. In a multitier website, resource bottleneck often shifts between tiers as client access pattern changes. This makes the problem of online capacity measurement even more challenge. This paper presents an online measurement approach based on low-level hardware performance metrics such as instructions execution rate and cache access behavior. Such metrics together define a system internal running state. The measurement approach uses machine learning techniques to infer application-level performance at each tier from a set of selected hardware performance counters. A coordinated predictor is induced over individual tier-wide models to make global system performance prediction and identify the bottleneck when the system becomes overloaded. Experiments were conducted on a two-tier Tomcat/MySQL-configured website using TPC-W benchmarks. Experimental results demonstrated that this approach was able to achieve an overload prediction accuracy of higher than 90 percent for a priori known input traffic mix and over 85 percent accuracy even for traffic causing frequent bottleneck shifting. It costs less than 0.5 percent runtime overhead for data collection and no more than 50 ms for each online decision making. [ABSTRACT FROM AUTHOR]

Internet traffic often exhibits a structure with rich high-order statistical properties like self-similarity and long-range dependency (LRD). This greatly complicates the problem of server performance modeling and optimization. Existing tools like queuing models in most cases only hold in mean value analysis under the assumption of simplified traffic structures. In this paper, we present a filter model to characterize the relationship among the factors of server capacity, request scheduling, and service quality for general input traffic. By the model, a server scheduler operates as an finite-duration impulse response (FIR) filter that transforms request processes into workload processes with the objective of minimizing load variation or overload probability, and meanwhile, without violating request response deadlines as defined in service-level agreements. We present a design and analysis of the filter for traffic with strong LRD in the frequency domain. Most Internet traffic has monotonically decreasing strength of variation functions over frequency. For this type of input traffic, we prove that optimal schedulers must have a convex structure. Uniform resource allocation is an extreme case of the convexity and is proved to be optimal for Poisson traffic. We integrate the convex structural principle with the Generalized Processor Sharing (GPS) discipline and show that the enhanced GPS policy improves the service quality significantly. Furthermore, we show that the presence of LRD in the input traffic results in shift of variation strength from high frequency to lower frequency bands and consequently leads to a degradation of the service quality. [ABSTRACT FROM PUBLISHER]