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Graceful Performance Degradation in Apache Storm 21st

Graceful Performance Degradation in Apache Storm

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21st international conference on parallel and distributed Computing, applications, and Technologies, PDCAT 2020

作者： HoseinyFarahabady, Mohammad Reza Taheri, Javid Zomaya, Albert Y. Tari, Zahir School of Computer Science Center for Distributed and High Performance Computing The University of Sydney SydneyNSW Australia Department of Mathematics and Computer Science Karlstad University Karlstad Sweden School of Science RMIT University MelbourneVIC Australia

ISBN: (纸本)9783030692438

The concept of stream data processing is becoming challenging in most business sectors where try to improve their operational efficiency by deriving valuable information from unstructured, yet, contentiously generated high volume raw data in an expected time spans. A modern streamlined data processing platform is required to execute analytical pipelines over a continues flow of data-items that might arrive in a high rate. In most cases, the platform is also expected to dynamically adapt to dynamic characteristics of the incoming traffic rates and the ever-changing condition of underlying computational resources while fulfill the tight latency constraints imposed by the end-users. Apache Storm has emerged as an important open source technology for performing stream processing with very tight latency constraints over a cluster of computing nodes. To increase the overall resource utilization, however, the service provider might be tempted to use a consolidation strategy to pack as many applications as possible in a (cloud-centric) cluster with limited number of working nodes. However, collocated applications can negatively compete with each other, for obtaining the resource capacity in a shared platform that, in turn, the result may lead to a severe performance degradation among all running applications. The main objective of this work is to develop an elastic solution in a modern stream processing ecosystem, for addressing the shared resource contention problem among collocated applications. We propose a mechanism, based on design principles of Model Predictive Control theory, for coping with the extreme conditions in which the collocated analytical applications have different quality of service (QoS) levels while the shared-resource interference is considered as a key performance limiting parameter. Experimental results confirm that the proposed controller can successfully enhance the p -99 latency of high priority applications by 67%, compared to the default round r

关键词： Storms

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MMDSSE: Multi-client and Multi-keyword Dynamic Searchable Symmetric Encryption for Cloud Storage

MMDSSE: Multi-client and Multi-keyword Dynamic Searchable Sy...

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Annual conference on Privacy, Security and Trust, PST

作者： Panyu Wu Zhenfu Cao Jiachen Shen Xiaolei Dong Yihao Yang Jun Zhou Liming Fang Zhe Liu Chunpeng Ge Chunhua Su Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Research Center for Basic Theories of Intelligent Computing Research Institute of Basic Theories Zhejiang Lab Hangzhou China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Changsha China Shandong University Jinan China University of Aizu Fukushima Japan

Since data outsourcing poses privacy concerns with data leakage, searchable symmetric encryption (SSE) has emerged as a powerful solution that enables clients to perform query operations on encrypted data while preserving their privacy. Dynamic SSE schemes have been proposed to handle update operations. However, it is shown that updates might increase the risk of information leakage. Meanwhile, to meet the requirement of real-world applications, it is desirable to have the searchable encryption scheme which supports both multiple clients and multi-keyword queries. To address these issues, this paper proposes MMDSSE, a multi-client forward secure dynamic SSE scheme that supports multi-keyword queries. MMDSSE allows the clients narrow down the results by providing an arbitrary subset of the entire archive, and thus suitable for cloud storage environment. Security analysis and experimental evaluations show that MMDSSE is secure and efficient.

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Confidential Serverless Made Efficient with Plug-In Enclaves 21

Confidential Serverless Made Efficient with Plug-In Enclaves

引用

ACM/IEEE 48th Annual international Symposium on Computer Architecture (ISCA)

作者： Li, Mingyu Xia, Yubin Chen, Haibo Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9781665433334

Serverless computing has become a fact of life on modern clouds. A serverless function may process sensitive data from clients. Protecting such a function against untrusted clouds using hardware enclave is attractive for user privacy. In this work, we run existing serverless applications in SGX enclave, and observe that the performance degradation can be as high as 5.6 x to even 422.6 x . Our investigation identifies these slowdowns are related to architectural features, mainly from page-wise enclave initialization. Leveraging insights from our overhead analysis, we revisit SGX hardware design and make minimal modification to its enclave model. We extend SGX with a new primitive-region-wise plugin enclaves that can be mapped into existing enclaves to reuse attested common states amongst functions. By remapping plugin enclaves, an enclave allows in-situ processing to avoid expensive data movement in a function chain. Experiments show that our design reduces the enclave function latency by 94.74-99.57%, and boosts the autoscaling throughput by 19-179 x.

关键词： Intel SGX Serverless Confidential Computing

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Applying Fuzzy Logic to Efficiently Manage Shared Edge Infrastructure

Applying Fuzzy Logic to Efficiently Manage Shared Edge Infra...

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IEEE international Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (WET ICE)

作者： Tidiane Sylla Mohamed Aymen Chalouf Leo Mendiboure Francine Krief Hasnaa Aniss Lylia Alouache Dept. ISA-GEII Univ. of Sciences Techniques and Technologies of Bamako Bamako Mali IRISA Lab University of Rennes 1 Lannion France COSYS-ERENA Univ. Gustave Eiffel Marne-la-Vallée France LaBRI Lab Bordeaux INP Bordeaux France ETIS UMR 8051 CNRS lab CY Cergy Paris University Cergy France

Edge Computing promise a bright future for Internet of Things (IoT). Edge Computing servers can be geographically distributed to be closer to users and ensure low latency communications and real-time data processing for the third-party applications using this infrastructure. However, user mobility and limited Edge servers capabilities (CPU, memory, bandwidth) may cause many services placement failures. That's why in this article we propose a new strategy aimed at enabling a fair sharing of available Edge resources through a dynamic and real-time IoT Service placement. Our strategy uses Fuzzy Logic to enable 1) quick and low-cost deployment of the solution and 2) real-time modification of the policies defined by the Edge operator. It takes advantage, among others, of a microservice decomposition to optimize the use of the Edge architecture. Experiments demonstrate the benefits of our approach in terms of placement reliability, execution time and resources utilization.

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AccDP: Accelerated Data-parallel distributed DNN Training for Modern GPU-Based HPC Clusters

AccDP: Accelerated Data-Parallel Distributed DNN Training fo...

引用

international conference on High Performance Computing

作者： Nawras Alnaasan Arpan Jain Aamir Shafi Hari Subramoni Dhabaleswar K Panda Department of Computer Science and Engineering The Ohio State University Columbus Ohio USA

Deep Learning (DL) has become a prominent machine learning technique due to the availability of efficient computational resources in the form of Graphics processing Units (GPUs), large-scale datasets and a variety of models. The newer generation of GPUs are being designed with special emphasis on optimizing performance for DL applications. Also, the availability of easy-to-use DL frameworks—like PyTorch and TensorFlow— has enhanced productivity of domain experts to work on their custom DL applications from diverse domains. However, existing Deep Neural Network (DNN) training approaches may not fully utilize the newly emerging powerful GPUs like the NVIDIA A100—this is the primary issue that we address in this paper. Our motivating analyses show that the GPU utilization on NVIDIA A100 can be as low as 43% using traditional DNN training approaches for small-to-medium DL models and input data size. This paper proposes AccDP—a data-parallel distributed DNN training approach—to accelerate GPU-based DL applications. AccDP exploits the Message Passing Interface (MPI) communication library coupled with the NVIDIA’s Multi-Process Service (MPS) to increase the amount of work assigned to parallel GPUs resulting in higher utilization of compute resources. We evaluate our proposed design on different small-to-medium DL models and input sizes on the state-of-the-art HPC clusters. By injecting more parallelism into DNN training using our approach, the evaluation shows up to 58% improvement in training performance on a single GPU and up to 62% on 16 GPUs compared to regular DNN training. Furthermore, we conduct an in-depth characterization to determine the impact of several DNN training factors and best practices—including the batch size and the number of data loading workers— to optimally utilize GPU devices. To the best of our knowledge, this is the first work that explores the use of MPS and MPI to maximize the utilization of GPUs in distributed DNN training.

关键词： Training Deep learning Productivity Performance evaluation Computational modeling Message passing Neural networks

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Application Characterization for Near Memory processing

Application Characterization for Near Memory Processing

引用

Euromicro conference on parallel, distributed and Network-Based processing

作者： Maryam S. Hosseini Masoumeh Ebrahimi Pooria Yaghini Nader Bagherzadeh Center for Pervasive Communications and Computing (CPCC) University of California Irvine KTH Royal Institute of Technology Sweden

Data movement between memory subsystem and processor unit is a crippling performance and energy bottleneck for data-intensive applications. Near Memory processing (NMP) is a promising solution to alleviate the data movement bottleneck. The introduction of 3D-stacked memories and more importantly hybrid memory systems enable the long-wished NMP capability. This work explores the feasibility and efficacy of having NMP on the hybrid memory system for a given set of applications. In this paper, we first redefine a set of NMP-centric performance metrics in order to analyze the efficacy of a given processing unit. Leveraging the proposed metrics, we characterize various sets of applications to assess the suitability of a processing unit in terms of performance. Specifically, in this work we motivate the efficiency of NMP subsystems to process memory-intensive applications when 3D-NVM technologies are employed.

关键词： Measurement Nonvolatile memory Memory management Performance analysis Central processing Unit

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Memory-Centric Communication Mechanism for Real-time Autonomous Navigation applications 20

Memory-Centric Communication Mechanism for Real-time Autonom...

引用

49th international conference on parallel processing (ICPP)

作者： Liu, Wei Gong, Yifan Wu, Hao Zhai, Jidong Jin, Jiangming TuSimple Beijing Peoples R China Tsinghua Univ BNRist Beijing Peoples R China

ISBN: (纸本)9781450388160

There has been a remarkable increase in the speed of AI development over the past few years. Artificial intelligence and deep learning techniques are blooming and expanding in all forms to every sector possible. With the emerging intelligent autonomous navigation systems, both memory allocation and data movement are becoming the main bottlenecks in inter-process communication procedures, especially in supporting various types of messages between multiple programming languages. To reduce significant memory allocation and data movement cost, we propose a novel memory-centric mechanism, which includes a virtual layer based architecture and a pre-record memory allocation algorithm. Furthermore, we implement a memory-centric communication framework named Z-framework based on the proposed mechanism to achieve high efficient IPC procedures in autonomous navigation systems. Experimental results show that Z-framework is able to gain up to 41% and 35% performance improvement compared with the approach used in ROS2, which is an industry standard and the state-of-the-art approach used in CyberRT, respectively.

关键词： Autonomous Navigation System Inter-process Communication Memory-centric techniques

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Performance Analysis of distributed processing System using Shard Selection techniques on Elasticsearch

Performance Analysis of Distributed Processing System using ...

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international conference on Computational Intelligence and Data Science (ICCIDS)

作者： Dhulavvagol, Praveen M. Bhajantri, Vijayakumar H. Totad, S. G. KLE Technol Univ Sch Comp Sci & Engn Hubballi 580031 India

In distributed systems multiple computers interact with each other over a network to perform data processing operations parallel over a network. distributed systems can be setup depending on the application and its data with different combination of replications and then the partitioning of the data can be performed using sharding technique to enhance the scalability, consistency and fault-tolerance issues. The gaps identified in existing sharding techniques are, it is difficult to determine which particular shard or partition need to be searched to retrieve the relevant document matching to the user query. And also to rank the documents which has highest relevance to the user query. The main goal is to develop a mechanism to handle large scale data processing and searching operations using efficient sharding technique. The proposed paper aims to enhance the performance of distributed processing systems by applying effective shard partitioning and efficient shard selection techniques and perform the comparative study analysis of shard selection techniques considering precision, MAP and cost measures. The results interpret that sharding technique provides efficient mechanism to handle large scale data. Effectiveness and efficiency of shard selection techniques interpret that Rank-S algorithm performance better compared to CORI and Redde algorithms by reducing the overall cost by 28%. Sharding technique is an efficient mechanism to process big data, and provides better scalability and fault-tolerance. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： CORI Redde Highsim Sharding Precision

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ETH: An Architecture for Exploring the Design Space of In-situ Scientific Visualization 34

ETH: An Architecture for Exploring the Design Space of In-si...

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34th IEEE international parallel and distributed processing Symposium (IPDPS)

作者： Abram, Gregory Adhinarayanan, Vignesh Feng, Wu-chun Rogers, David Ahrens, James Univ Texas Austin Texas Adv Comp Ctr Austin TX 78758 USA Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA Los Alamos Natl Lab Comp Computat & Stat Sci Div Los Alamos NM 87545 USA

ISBN: (纸本)9781728168760

As high-performance computing (HPC) moves towards the exascale era, large-scale scientific simulations are generating enormous datasets. Many techniques (e.g., in-situ methods, data sampling, and compression) have been proposed to help visualize these large datasets under various constraints such as storage, power, and energy. However, evaluating these techniques and understanding the trade-offs (e.g., performance, efficiency, and quality) remains a challenging task. To enable exploration of the design space across such tradeoffs, we propose the Exploration Test Harness (ETH), an architecture for the early-stage exploration of visualization and rendering approaches, job layout, and visualization pipelines. ETH covers a broader parameter space than current large-scale visualization applications such as ParaView and VisIt. It also promotes the study of simulation-visualization coupling strategies through a data-centric approach, rather than requiring coupling with a specific scientific simulation code. Furthermore, with experimentation on an extensively instrumented supercomputer, we study more metrics of interest than was previously possible. Importantly, ETH will help to answer important what-if scenarios and trade-off questions in the early stages of pipeline development, helping scientists to make informed choices about how to best couple a simulation code with visualization at extreme scale.

关键词： In-situ techniques High-Performance Computing Design-space Exploration Raycasting Energy Efficiency

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A C++ Library for Memory Layout and Performance Portability of Scientific applications 28th

A C++ Library for Memory Layout and Performance Portabilit...

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28th international European conference on parallel and distributed Computing , Euro-Par 2022

作者： Incardona, Pietro Gupta, Aryaman Yaskovets, Serhii Sbalzarini, Ivo F. Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany Center for Systems Biology Dresden Dresden01307 Germany Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI Dresden/Leipzig Germany

ISBN: (纸本)9783031312083

We present a C++14 library for performance portability of scientific computing codes across CPU and GPU architectures. Our library combines generic data structures like vectors, multi-dimensional arrays, maps, graphs, and sparse grids with basic, reusable algorithms like convolutions, sorting, prefix sum, reductions, and scan. The memory layout of the data structures is adapted at compile-time using tuples with optional memory mirroring between CPU and GPU. We combine this transparent memory mapping with generic algorithms under two alternative programming interfaces: a CUDA-like kernel interface for multi-core CPUs, Nvidia GPUs, and AMD GPUs, as well as a lambda interface. We validate and benchmark the presented library using micro-benchmarks, showing that the abstractions introduce negligible performance overhead, and we compare performance against the current state of the art. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphics processing unit

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