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Real-time stream data processing at scale 20

Real-time stream data processing at scale

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20th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2019

作者： Hoseinyfarahabady, M. Reza Jannesari, Ali Bao, Wei Tari, Zahir Zomaya, Albert Y. University of Sydney Center for Distributed and High Performance Computing School of Computer Science NSW Australia Department of Computer Science Iowa State University IA United States RMIT University School of Science MelbourneVIC Australia

ISBN: (纸本)9781728126166

A typical scenario in a stream data-flow processing engine is that users submit continues queries in order to receive the computational result once a new stream of data arrives. The focus of the paper is to design a dynamic CPU cap controller for stream data-flow applications with real-time constraints, in which the result of computations must be available within a short time period, specified by the user, once a recent update in the input data occurs. It is common that the stream data-flow processing engine is deployed over a cluster of dedicated or virtualized server nodes, e.g., Cloud or Edge platform, to achieve a faster data processing. However, the attributes of incoming stream data-flow might fluctuate in an irregular way. To effectively cope with such unpredictable conditions, the underlying resource manager needs to be equipped with a dynamic resource provisioning mechanism to ensure the real-time requirements of different applications. The proposed solution uses control theory principals to achieve a good utilization of computing resources and a reduced average response time. The proposed algorithm dynamically adjusts the required quality of service (QoS) in an environment when multiple stream & data-flow processing applications concurrently run with unknown and volatile workloads. Our study confirms that such a unpredictable demand can negatively degrade the system performance, mainly due to adverse interference in the utilization of shared resources. Unlike prior research studies which assumes a static or zero correlation among the performance variability among consolidated applications, we presume the prevalence of shared-resource interference among collocated applications as a key performance-limiting parameter and confront it in scenarios where several applications have different QoS requirements with unpredictable workload demands. We design a low-overhead controller to achieve two natural optimization objectives of minimizing QoS violation amount an

关键词： Quality of service

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Proactive Antifragility: A New Paradigm for Next-Generation Cyber Defence at the Edge 5

Proactive Antifragility: A New Paradigm for Next-Generation ...

引用

IEEE 5th international conference on Collaboration and Internet computing (CIC)

作者： Uzunov, Anton, V Nepal, Surya Chhetri, Mohan Baruwal Def Sci & Technol Grp Adelaide SA Australia Cyber Secur Cooperat Res Ctr Joondalup WA Australia CSIRO Data61 Canberra ACT Australia

ISBN: (纸本)9781728167398

Recent advances in the Internet of Things (IoT) and, more generally, distributed computing research have given rise to various edge-oriented computing paradigms including Edge-, Fog-, and Mobile Cloud-computing. Systems adhering to these paradigms exhibit a number of characteristics such as high levels of heterogeneity and dynamicity, leading to high complexity, that exacerbate the problems associated with traditional cyber defence. These problems are further exacerbated when edge-oriented systems operate in adversarial environments, e.g. the Internet of Battle Things (IoBT). Current generations of cyber defence approaches straddling attack prevention, detection, response and tolerance are arguably insufficient for edge-oriented systems operating in IoBT environments, especially given the constant increase in cyber attack sophistication. In this paper, we propose proactive antifragility as a new paradigm for the next generation of cyber defence approaches capable of taking into account the aforementioned system characteristics and environmental challenges. We propose a conceptualization of proactive antifragility, and then outline associated challenges and research directions indicating which existing approaches can be re-used or advanced/developed further in order for proactive antifragility to be achieved "at the edge".

关键词： Internet of Things Edge computing Fog computing Cyber Security Cyber Resilience Antifragility

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Control of Multiple PV Integrated parallel Inverters for Microgrid Applications

Control of Multiple PV Integrated Parallel Inverters for Mic...

引用

international conference on Power, Instrumentation, Control and computing (PICC)

作者： Yashi Singh Bhim Singh Sukumar Mishra Indian Institute of Technology Delhi New Delhi

ISBN: (数字)9781728175904

ISBN: (纸本)9781728175911

To enhance the accessibility and reliability for a distributed generation system (DGS), a grid-tied photovoltaic (PV) generation system based on multiple parallel connected PV-inverters is developed for microgrid application in this work. This microgrid is designed to operate in the grid tied mode (GTM) as well as a standalone mode (SAM) and vice-versa. Here, a grid-forming inverter (GFI) technology is utilized for system with high penetration of renewables. The single stage PV-battery interfaced GFI inverter in parallel with a PV array supported inverters is considered to operate in the GTM as well as in (SAM) of operation. In SAM, the grid forming inverter maintains constant voltage and frequency across the load The control strategies of each unit are independent and using multi-loop controllers with switching logic. In the GTM, each PV panel in DGS is interfaced with an inverter with independent maximum power point tracking (MPPT) to harvest maximum power. The battery supports the PV units during peak load periods and maintains the power balance in the microgrid. Controlled charging and discharging profile of the battery is achieved by a DC-DC bidirectional converter (DBC). The control strategy of microgrid is verified with simulated results in detail.

关键词： Photovoltaic systems Power quality Microgrids Switches Inverters Batteries Voltage control

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Efficient Execution of Dynamic Programming Algorithms on Apache Spark

Efficient Execution of Dynamic Programming Algorithms on Apa...

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IEEE international conference on Cluster computing

作者： Mohammad Mahdi Javanmard Zafar Ahmad Jaroslaw Zola Louis-Noël Pouchet Rezaul Chowdhury Robert Harrison Stony Brook University USA University at Buffalo USA Colorado State University USA

ISBN: (数字)9781728166773

ISBN: (纸本)9781728166780

One of the most important properties of distributed computing systems (e.g., Apache Spark, Apache Hadoop, etc) on clusters and computation clouds is the ability to scale out by adding more compute nodes to the cluster. This important feature can lead to performance gain provided the computation (or the algorithm) itself can scale out. In other words, the computation (or the algorithm) should be easily decomposable into smaller units of work to be distributed among the workers based on the hardware/software configuration of the cluster or the cloud. Additionally, on such clusters, there is an important trade-off between communication cost, parallelism, and memory requirement. Due to the scalability need as well as this trade-off, it is crucial to have a well-decomposable, adaptive, tunable, and scalable program. Tunability enables the programmer to find an optimal point in the trade-off spectrum to execute the program efficiently on a specific cluster. We design and implement well-decomposable and tunable dynamic programming algorithms from the Gaussian Elimination Paradigm (GEP), such as Floyd-Warshall's all-pairs shortest path and Gaussian elimination without pivoting, for execution on Apache Spark. Our implementations are based on parametric multi-way recursive divide-&-conquer algorithms. We explain how to map implementations of those grid-based parallel algorithms to the Spark framework. Finally, we provide experimental results illustrating the performance, scalability, and portability of our Spark programs. We show that offloading the computation to an OpenMP environment (by running parallel recursive kernels) within Spark is at least partially responsible for a 2-5× speedup of the DP benchmarks.

关键词： Heuristic algorithms Scalability Clustering algorithms Cluster computing Partitioning algorithms Sparks Kernel

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SpotTune: Leveraging Transient Resources for Cost-efficient Hyper-parameter Tuning in the Public Cloud

SpotTune: Leveraging Transient Resources for Cost-efficient ...

引用

international conference on distributed computing Systems

作者： Yan Li Bo An Junming Ma Donggang Cao Yasha Wang Hong Mei Ministry of Education Key Lab of High-Confidence Software Technology (Peking University) Beijing China

ISBN: (数字)9781728170022

ISBN: (纸本)9781728170039

Hyper-parameter tuning (HPT) is crucial for many machine learning (ML) algorithms. But due to the large searching space, HPT is usually time-consuming and resource-intensive. Nowadays, many researchers use public cloud resources to train machine learning models, convenient yet expensive. How to speed up the HPT process while at the same time reduce cost is very important for cloud ML users. In this paper, we propose SpotTune, an approach that exploits transient revocable resources in the public cloud with some tailored strategies to do HPT in a parallel and cost-efficient manner. Orchestrating the HPT process upon transient servers, SpotTune uses two main techniques, fine-grained cost-aware resource provisioning, and ML training trend predicting, to reduce the monetary cost and runtime of HPT processes. Our evaluations show that SpotTune can reduce the cost by up to 90% and achieve a 16.61x performance-cost rate improvement.

关键词： Training Cloud computing Computational modeling Machine learning Market research Transient analysis Tuning

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Proceedings of the 48th international conference on parallel Processing, ICPP 2019

Proceedings of the 48th International Conference on Parallel...

引用

48th international conference on parallel Processing, ICPP 2019

ISBN: (纸本)9781450362955

The proceedings contain 106 papers. The topics discussed include: a parallel graph algorithm for detecting mesh singularities in distributed memory ice sheet simulations;machine learning for fine-grained hardware prefetcher control;AVR: reducing memory traffic with approximate value reconstruction;incorporating probabilistic optimizations for resource provisioning of data processing workflows;stage delay scheduling: speeding up DAG-style data analytics jobs with resource interleaving;unleashing the scalability potential of power-constrained data center in the microservice era;dynamic load balancing in hybrid switching data center networks with converters;when power oversubscription meets traffic flood attack: re-thinking data center peak load management;and HyperPRAW: architecture-aware hypergraph restreaming partition to improve performance of parallel applications running on high performance computing systems.

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Service Level Objectives via C++11 Attributes 24th

Service Level Objectives via C++11 Attributes

引用

international European conference on parallel and distributed computing (Euro-Par)

作者： Griebler, Dalvan De Sensi, Daniele Vogel, Adriano Danelutto, Marco Fernandes, Luiz Gustavo Pontificia Univ Catolica Rio Grande do Sul Sch Technol Porto Alegre RS Brazil Univ Pisa Dept Comp Sci Pisa Italy Tres De Maio Fac Lab Adv Res Cloud Comp Tres De Maio Brazil

ISBN: (纸本)9783030105495;9783030105488

In recent years, increasing attention has been given to the possibility of guaranteeing Service Level Objectives (SLOs) to users about their applications, either regarding performance or power consumption. SLO can be implemented for parallel applications since they can provide many control knobs (e.g., the number of threads to use, the clock frequency of the cores, etc.) to tune the performance and power consumption of the application. Different from most of the existing approaches, we target sequential stream processing applications by proposing a solution based on C++ annotations. The user specifies which parts of the code to parallelize and what type of requirements should be enforced on that part of the code. Our solution first automatically parallelizes the annotated code and then applies self-adaptation approaches at run-time to enforce the user-expressed objectives. We ran experiments on different real-world applications, showing its simplicity and effectiveness.

关键词： parallel programming Adaptive and autonomic computing Power-aware computing Domain-specific language

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Succinct representations in collaborative filtering: A case study using wavelet tree on 1,000 cores 20

Succinct representations in collaborative filtering: A case ...

引用

20th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2019

作者： Peng, Xiangjun Wang, Qingfeng Sun, Xu Gong, Chunye Wang, Yaohua User-Centric Computing Group University of Nottingham Ningbo China China Department of Computer Science and Technology National University of Defense Technology China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology China

ISBN: (纸本)9781728126166

User-Item (U-I) matrix has been used as the dominant data infrastructure of Collaborative Filtering (CF). To reduce space consumption in runtime and storage, caused by data sparsity and growing need to accommodate side information in CF design, one needs to go beyond the U-I Matrix. In this paper, we took a case study of Succinct Representations in Collaborative Filtering, rather than using a U-I Matrix. Our key insight is to introduce Succinct Data Structures as a new infrastructure of CF. Towards this, we implemented a User-based K-Nearest-Neighbor CF prototype via Wavelet Tree, by first designing a Accessible Compressed Documents (ACD) to compress U-I data in Wavelet Tree, which is efficient in both storage and runtime. Then, we showed that ACD can be applied to develop an efficient intersection algorithm without decompression, by taking advantage of ACD's characteristics. We evaluated our design on 1,000 cores of Tianhe-II supercomputer, with one of the largest public data set ml-20m. The results showed that our prototype could achieve 3.7 minutes on average to deliver the results. © 2019 IEEE.

关键词： Collaborative filtering

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A Study of Internet Map Building Recognition Based on AI Technology

A Study of Internet Map Building Recognition Based on AI Tec...

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international conference on Computer and Communications (ICCC)

作者： Zhiheng Gao Siping Yuan Yunping Gong Emerging Information Technology Research Center Research Institute of China Telecom Co. Ltd. Guangzhou China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

A solution for building objects recognition from Internet map is proposed. Map pages are separately screenshot with customized CSS style and spliced together. Building contours are detected with Suzuki's contour tracing algorithm. Annotation points of text labels are detected with Hough Transform. Text labels in the image are first detected with AdvancedEAST algorithm, and then recognized as characters with CRNN algorithm, which is fine-tuned with specific data set in our works. A fast processing architecture design based on distributed parallel computing is proposed to efficiently accomplish the whole recognition process.

关键词： Image recognition Text recognition Buildings Transforms parallel processing Internet Object recognition

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DSANA: A distributed machine learning acceleration solution based on dynamic scheduling and network acceleration

DSANA: A distributed machine learning acceleration solution ...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Runhua Zhang Guowei Shen Liangyi Gong Chun Guo Guizhou University China Guiyang Beihang University China Beijing Guizhou University China Guiyang Tsinghua University China Beijing

ISBN: (纸本)9781728176505

distributed machine learning(DML) has become a feasible solution to deal with the growing training data and models. Reviewing the existing architecture of DML, Parametric server(PS) architecture stands out in iterative convergence algorithms and widely deployed in practice, thanks to flexible expansion and so on. Under this architecture, the parameter synchronization mode based on Bulk Synchronous parallel(BSP) has become one of the research hotspots. As for the BSP mode, each iteration efficiency is determined by the slowest node in the cluster, therefore, the straggler problem becomes the main reason for reducing the efficiency of DML training, which is even more prominent in the heterogeneous cloud services. Existing works mainly focus on the straggler problem, and the importance of communication is usually ignored. However, inefficient communication is also one of the reasons for the inefficiency of DML iterations. In this paper, we propose DSANA, which first alleviates certain straggler problems by dynamically scheduling computation tasks. Secondly, DSANA improves the overlap of computation/communication by dividing larger transmission parameters, thus further improving the iteration efficiency of DML training. We conduct comparison experiments with the classic iterative algorithm PageRank on four different-scale data sets in two cloud service scenarios. The experimental results show that DSANA can improve the training efficiency to 36.6%$\sim$ 56.4% compared with the baseline solution.

关键词： Training Machine learning algorithms Processor scheduling Training data Computer architecture Machine learning Dynamic scheduling

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