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Toward online virtual network function placement in Software Defined Networks

Toward online virtual network function placement in Software...

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international workshop on Quality of Service

作者： Bowu Zhang Jinho Hwang Timothy Wood Marist College IBM T.J Watson Research Center The George Washington University

Network function virtualization (NFV) and Software Defined Networks (SDN) separate and abstract network functions from underlying hardware, creating a flexible virtual networking environment that reduces cost and allows policy-based decisions. One of the biggest challenges in NFV-SDN is to map the required virtual network functions (VNFs) to the underlying hardware in substrate networks in a timely manner. In this paper, we formulate the VNF placement problem via Graph Pattern Matching, with an objective function that can be easily adapted to fit various applications. Previous work only considers off-line VNF placement as it is time consuming to find an appropriate mapping path while considering all software and hardware constraints. To reduce this time, we investigate the feasibility and effectiveness of path-precomputing, where paths are calculated prior to placement. Our approach enables online VNF placement in SDNs, allowing VNF requests to be processed as they arrive. An online placement approach (OPA) is proposed to place VNF requests on substrate networks. To the best of our knowledge, this is the first work in the literature that considers the online chaining VNF placement in SDNs. In addition, we present an application of OPA over cost minimization. Simulation results demonstrate that our online approach provides competitive performance compared with off-line algorithms.

关键词： Substrates Software Hardware optimization Servers Electronic mail

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Performance Portability of Molecular Docking Miniapp On Leadership Computing Platforms

Performance Portability of Molecular Docking Miniapp On Lead...

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Performance, Portability and Productivity in HPC (P3HPC)

作者： Mathialakan Thavappiragasam Aaron Scheinberg Wael Elwasif Oscar Hernandez Ada Sedova Oak Ridge National Laboratory Oak Ridge Tennessee Jubilee Development Cambridge Massachusetts

ISBN: (数字)9781665422871

ISBN: (纸本)9781665422888

Rapidly changing computer architectures, such as those found at high-performance computing (HPC) facilities, present the need for mini-applications (miniapps) that capture essential algorithms used in large applications to test program performance and portability, aiding transitions to new systems. The COVID-19 pandemic has fueled a flurry of activity in computational drug discovery, including the use of supercomputers and GPU acceleration for massive virtual screens for therapeutics. Recent work targeting COVID-19 at the Oak Ridge Leadership Computing Facility (OLCF) used the GPU-accelerated program AutoDock-GPU to screen billions of compounds on the Summit supercomputer. In this paper we present the development of a new miniapp, miniAutoDock-GPU, that can be used to evaluate the performance and portability of GPU-accelerated protein-ligand docking programs on different computer architectures. These tests are especially relevant as facilities transition from petascale systems and prepare for upcoming exascale systems that will use a variety of GPU vendors. The key calculations, namely, the Lamarckian genetic algorithm combined with a local search using a Solis-Wets based random optimization algorithm, are implemented. We developed versions of the miniapp using several different programming models for GPU acceleration, including a version using the CUDA runtime API for NVIDIA GPUs, and the Kokkos middle-ware API which is facilitated by C++ template libraries. A third version, currently in progress, uses the HIP programming model. These efforts will help facilitate the transition to exascale systems for this important emerging HPC application, as well as its use on a wide range of heterogeneous platforms.

关键词： Graphics processing units Proteins optimization Programming Leadership Hip COVID-19

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optimization of human control strategy with simultaneously perturbed stochastic approximation

Optimization of human control strategy with simultaneously p...

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IEEE international workshop on Intelligent Robots and Systems (IROS)

作者： Jingyan Song Yangsheng Xu Yeung Yam M.C. Nechyba Department of Systems Engineering & Engineering Management The Chinese University of Hong Kong Hong Kong NT China The Robotics Institute Carnegie Mellon University Pittsburgh PA USA Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong NT China

Modeling the dynamic human control strategy (HCS) is becoming an increasingly popular paradigm in a number of different research areas, ranging from robotics to intelligent vehicle highway systems. Usually, HCS models are derived empirically, rather than analytically, from real human input-output data. While these empirical models offer an effective means of transferring intelligent behaviors from humans to robots and other machines, the models are not explicitly optimized with respect to potentially important performance criteria. We therefore propose an iterative algorithm for optimizing an initially stable HCS model with respect to an independent, user-specified performance criterion. We first collect driving data from different individuals through a real-time graphic driving simulator. Next, we describe how we model each individual's control strategy through flexible cascade neural networks. Once we have initially stable HCS models, we propose simultaneously perturbed stochastic approximation (SPSA) to optimize these models with respect to a chosen performance criterion. Finally, we describe and discuss some experimental results with the proposed algorithm.

关键词： Humans Intelligent robots Vehicle dynamics Intelligent vehicles Road transportation Machine intelligence Iterative algorithms Graphics Neural networks Stochastic processes

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Mobile diagnosis [of vehicle mechatronic systems]

Mobile diagnosis [of vehicle mechatronic systems]

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IEEE international Vehicle Electronics Conference

作者： J. Luka F. Stubhan Research and Technology DaimlerChrysler Aerospace Stuttgart Germany Shanghai Institute of Metallurgy SIM DaimlerChrysler Laboratory Shanghai China

The increasing utilization of mechatronic systems in vehicles requires the integration of diagnosis functions which will be widely implemented as onboard software in the vehicle. These diagnosis functionalities will be based in the future on functional and mathematical models and will enable the depth of diagnosis to be improved. The ascertained data from the diagnosis functions describe the overall condition of the vehicle and are stored in nonvolatile memories. The development of mobile telephones capable of handling data and the increased availability of wireless networks build up the second basis of mobile diagnosis. Using a suitable IT infrastructure, data can be obtained from the vehicle by every PC. In addition to the data channel, which allows precise vehicle identification and describes the condition of the vehicle, customer complaints can be dealt with over the voice channel and serve as a fundamental source of information. Vehicle data as well as the information from the driver are compared with previously occurred faults in other vehicles. Case based reasoning (CBR) systems applications are identified. The result is a precise ascertainment of the fault cause, from which the defective vehicle components can be determined. Thus, service vehicles can carry with them correct replacement parts which can be inserted directly at the customers location in order to remove the fault. Mobile diagnosis allows an optimized diagnosis and repair process particularly if vehicles breakdown far away from a workshop.

关键词： Vehicle driving Mechatronics Power system modeling Vehicle safety Road vehicles Design optimization Electric breakdown Software algorithms Manufacturing automation Mathematical model

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COSTA: Cross-layer optimization for sketch-based software defined measurement task assignment

COSTA: Cross-layer optimization for sketch-based software de...

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international workshop on Quality of Service

作者： Zhiyang Su Ting Wang Mounir Hamd Hong Kong University of Science and Technology Hong Kong University of Science and Technology Kowloon HK

Sketch-based measurement provides traffic data summary in a memory-efficient way with provable accuracy bound. Recent advances in software defined networking (SDN) facilitate the development and implementation of sketch-based measurement applications. However, sketch-based measurement usually requires TCAMs which are precious resource in switch to match packet fields. The key challenge for sketch-based measurement is how to accept more concurrent measurement tasks with minimum resource usage. Existing proposals attempt to achieve this goal by exploring different task assignment algorithms. We argue that by sacrificing a small amount of accuracy, the resource usage can be decreased dramatically. In this paper, we propose COSTA, a novel system to improve the performance of the task assignment for sketch-based measurement. We utilize the cross-layer information between the application and the task assignment layers to formulate the problem as a mixed integer nonlinear programming problem. Due to its high computational complexity, we divide the initial problem into two stages and develop a two-stage heuristic to produce task assignment efficiently. In particular, we present an algorithm which guarantees (1 + α) approximation ratio to solve the second stage task assignment. Extensive experiments with three different measurement tasks and real packet traces demonstrate that COSTA significantly reduces the resource usage by up to 40% and accepts 30% more tasks.

关键词： Switches Software Software measurement Monitoring Programming Computational complexity Computer architecture

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Correctness-preserving Compression of Datasets and Neural Network Models

Correctness-preserving Compression of Datasets and Neural Ne...

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Software Correctness for HPC applications (Correctness)

作者： Vinu Joseph Nithin Chalapathi Aditya Bhaskara Ganesh Gopalakrishnan Pavel Panchekha Mu Zhang University of Utah Salt Lake City UT 84112 USA

ISBN: (数字)9780738110448

ISBN: (纸本)9781665422673

Neural networks deployed on edge devices must be efficient both in terms of their model size and the amount of data movement they cause when classifying inputs. These efficiencies are typically achieved through model compression: pruning a fully trained network model by zeroing out the weights. Given the overall challenge of neural network correctness, we argue that focusing on correctness preservation may allow the community to make measurable progress. We present a state-of-the-art model compression framework called Condensa around which we have launched correctness preservation studies. After presenting Condensa, we describe our initial efforts at understanding the effect of model compression in semantic terms, going beyond the top n% accuracy that Condensa is currently based on. We also take up the relatively unexplored direction of data compression that may help reduce data movement. We report preliminary results of learning from decompressed data to understand the effects of compression artifacts. Learning without decompressing input data also holds promise in terms of boosting efficiency, and we also report preliminary results in this regard. Our experiments centered around a state-of-the-art model compression framework called Condensa and two data compression algorithms, namely JPEG and ZFP, demonstrate the potential for employing model-and dataset compression without adversely affecting correctness.

关键词： Data models optimization Training Semantics Image coding Neural networks Bayes methods

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Workload characterization and optimization of high-performance text indexing on the Cell Broadband Engine™ (Cell/B.E.)

Workload characterization and optimization of high-performan...

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IEEE international workshop/Symposium on Workload Characterization

作者： Daniele Paolo Scarpazza Gordon W. Braudaway Multicore Computing Department IBM Thomas J. Watson Research Center Yorktown Heights NY USA

In this paper we examine text indexing on the Cell Broadband Enginetrade (Cell/B.E.), an emerging workload on an emerging multicore architecture. The Cell Broadband Engine is a microprocessor jointly developed by Sony Computer Entertainment, Toshiba, and IBM (herein, we refer to it simply as the "Cell"). The importance of text indexing is growing not only because it is the core task of commercial and enterprise-level search engines, but also because it appears more and more frequently in desktop and mobile applications, and on network appliances. Text indexing is a computationally intensive task. Multi-core processors promise a multiplicative increase in compute power, but this power is fully available only if workloads exhibit the right amount and kind of parallelism. We present the challenges and the results of mapping text indexing tasks to the Cell processor. The Cell has become known as a platform capable of impressive performance, but only when algorithms have been parallelized with attention paid to its hardware peculiarities (expensive branching, wide SIMD units, small local memories). We propose a parallel software design that provides essential text indexing features at a high throughput (161 Mbyte/s per chip on Wikipedia inputs) and we present a performance analysis that details the resources absorbed by each subtask. Not only does this result affect traditional applications, but it also enables new ones such as live network traffic indexing for security forensics, until now believed to be too computationally demanding to be performed in real time. We conclude that, at the cost of a radical algorithmic redesign, our Cell-based solution delivers a 4x performance advantage over recent commodity machine like the Intel Q6600. In a per-chip comparison, ours is the fastest text indexer that we are aware of.

关键词： Indexing Multicore processing Application software Computer architecture Microprocessors Search engines Home appliances Concurrent computing Parallel processing Hardware

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Multilayer network planning and operation using open-source tools (Tutorial)

Multilayer network planning and operation using open-source ...

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IEEE workshop on High Performance Switching and Routing

作者： Jose-Luis Izquierdo-Zaragoza Pablo Pavon-Marino Dept. Information and Communication Technologies Universidad Politécnica de Cartagena Cartagena Spain

Continuous advances in network technologies challenge network architects in their attempt to foresee what new technologies deserve resources and investments, what existing infrastructure to keep, and their interplay during migration processes. Moreover, topics like energy efficiency, disaster resilience, or SDN/NFV are being intensively considered and investigated during the last years. As a result, R&D efforts have led to a number of solutions. Unfortunately, it is often difficult for operators to reproduce such results on their networks, maybe due to divergent philosophies between commercial planning tools used in the industry and academic tools. A paradigm shift to an open-source view of network planning could emphasize the power of peer-review and transparency to promote innovation at an accelerated pace and lower cost. This tutorial would focus on Net2Plan (http://***), an open-source tool developed by the authors, and actively used in several courses (>200 students) and research projects since 2012. Built on top of a technology-agnostic multilayer network representation, Net2Plan is designed to assist users in the evaluation of built-in or original user-developed planning algorithms. In addition, users can analyze their designs using either reports or a simulation tool for in-operation scenarios like network resilience, connection-admission-control, time-varying traffic resource allocation, or even combinations of them. Besides, Net2Plan permits applying algorithms to orchestrate OpenFlow-enabled networks interacting with the northbound interface of OpenDaylight. Throughout the tutorial, attendees would have the opportunity to learn about the main features of Net2Plan, also discovering some of its applications. At the end of this tutorial, attendees would be guided through an intensive hands-on case study based on an IP-over-WDM network, e.g., including network dimensioning using built-in or user-developed algorithms, WDM line engineering (e.g., E

关键词： Planning Tutorials Nonhomogeneous media Open source software Algorithm design and analysis optimization Optical fiber networks

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Improving Parallel Exhaustive Subgroup Discovery with Early Search Space Pruning

Improving Parallel Exhaustive Subgroup Discovery with Early ...

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IEEE international Conference on High Performance Computing workshops (HiPCW)

作者： Jyoti Ashutosh Litoriya Sriram Kailasam School of Computing and Elect. Engg. Indian Institute of Technology Mandi India Dept. of Computer Sci. and Engg. and IT Jaypee University of Info. Tech. Solan India Genpact India Pvt. Ltd. Bangalore India Dept. of Computer Sci. and Engg. National Institute of Technology Warangal India

ISBN: (数字)9798331509118

ISBN: (纸本)9798331509125

In this digital era, vast amounts of data are generated from various applications, necessitating robust data mining algorithms to extract valuable insights. Subgroup discovery (SD) [1] is a widely used supervised descriptive data mining method that aims to find interesting subgroups of individuals (samples, objects) with high associations to a target property of interest. However, SD faces two major challenges. The first is the high computational expense of exploring the combinatorial search space of subgroups. Second, when testing multiple subgroups to identify a few significant associations, ensuring that each subgroup meets a quality threshold is not enough to control the occurrence of false positives. The latter issue has been addressed recently with permutation testing while for the former, exploring the search space of closed subgroups instead of all possible subgroups reduces the search space without loss of information. Pellegrina et al. [2] incorporated both these approaches and provided a sequential implementation TopKWY for discovering the top-k significant subgroups while controlling false discoveries. It is considered state-of-the-art. However, its huge computational requirement of assessing patterns against many permutations raises scalability concerns, while the specific optimizations in its algorithm for computing p-values do not generalize to other quality functions. Jyoti et al. [3] addressed both these concerns in their proposed framework ParaDiS . The algorithm of ParaDiS is parallel and distributed, employs efficient communication mechanisms for load sharing and generalizes to other quality functions. However, ParaDiS uses exhaustive search and therefore, suffers from performance bottlenecks. The existing heuristic techniques in SD neither control false discoveries nor consider the search space of closed subgroups. Further, these heuristics can miss potentially interesting subgroups w.r.t. quality.

关键词： Scalability High performance computing Conferences Aerospace electronics Space exploration Data mining optimization Faces Testing

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Communication Mechanisms for Cognitive Radio Networks

Communication Mechanisms for Cognitive Radio Networks

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IEEE workshop on Context Modeling and Reasoning

作者： Alexandru Popescu Yong Yao Markus Fiedler Adrian Popescu Dept. of Communications and Computer Systems School of Computing Blekinge Institute of Technology

ISBN: (纸本)9781467350754

Comprehensive communication in cognitive radio networks is an important research topic within the scope of empowering cognitive radio functionality in beyond-4G mobile networks. Providing communication for secondary users without interference with primary users is an ambitious task, which requires innovative management architecture designs and routing solutions. Operational challenges such as opportunistic spectrum access, solving problems related to spectrum and network heterogeneities and requests for the provisioning of Quality-of-Service to different applications must be resolved. As part of a novel management architecture, the paper advances a new approach to end-to-end communication in cognitive radio networks based on combining ad-hoc algorithms with spectrum mobility algorithms.

关键词： Routing Constraint optimization Communication mechanisms Spectrum access

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