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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Mohak Chadha Paul Wieland Michael Gerndt Chair of Computer Architecture and Parallel Systems Technische Universität München Garching (near Munich) Germany

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

With the advent of AWS Lambda in 2014, Serverless Computing, particularly Function-as-a-Service (FaaS), has witnessed growing popularity across various application domains. FaaS enables an application to be decomposed into fine-grained functions that are executed on a FaaS platform. It offers several advantages such as no infrastructure management, a pay-per-use billing policy, and on-demand fine-grained autoscaling. However, despite its advantages, developers today encounter various challenges while adopting FaaS solutions that reduce productivity. These include FaaS platform lock-in, support for diverse function deployment parameters, and diverse interfaces for interacting with FaaS platforms. To address these challenges, we present gFaaS, a novel framework that facilitates the holistic development and management of functions across diverse FaaS platforms. Our framework enables the development of generic functions in multiple programming languages that can be seamlessly deployed across different platforms without modifications. Results from our experiments demonstrate that gFaaS functions perform similarly to native platform-specific functions across various scenarios. A video demonstrating the functioning of gFaaS is available from https://***/STbb6ykJFf0.

关键词： Productivity computer languages Serverless computing Software

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FedLess: Secure and Scalable Federated Learning Using Serverless Computing

FedLess: Secure and Scalable Federated Learning Using Server...

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IEEE International Conference on Big Data

作者： Andreas Grafberger Mohak Chadha Anshul Jindal Jianfeng Gu Michael Gerndt Chair of Computer Architecture and Parallel Systems Technische Universität München Garching (Near Munich) Germany

ISBN: (纸本)9781665445993

The traditional cloud-centric approach for Deep Learning (DL) requires training data to be collected and processed at a central server which is often challenging in privacy-sensitive domains like healthcare. Towards this, a new learning paradigm called Federated Learning (FL) has been proposed that brings the potential of DL to these domains while addressing privacy and data ownership issues. FL enables clients to learn a shared ML model while keeping the data local. However, conventional FL systems face challenges such as scalability, complex infrastructure management, and wasted compute and incurred costs due to idle clients. These challenges of FL systems closely align with the core problems that serverless computing and Function-as-a-Service (FaaS) platforms aim to solve. These include rapid scalability, no infrastructure management, automatic scaling to zero for idle clients, and a pay-per-use billing model. To this end, we present a novel system and framework for serverless FL, called FedLess. Our system supports multiple commercial and self-hosted FaaS providers and can be deployed in the cloud, on-premise in institutional data centers, and on edge devices. To the best of our knowledge, we are the first to enable FL across a large fabric of heterogeneous FaaS providers while providing important features like security and Differential Privacy. We demonstrate with comprehensive experiments that the successful training of DNNs for different tasks across up to 200 client functions and more is easily possible using our system. Furthermore, we demonstrate the practical viability of our methodology by comparing it against a traditional FL system and show that it can be cheaper and more resource-efficient.

关键词： Training Scalability Computational modeling Serverless computing Training data FAA Big Data

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Extending SLURM for Dynamic Resource-Aware Adaptive Batch Scheduling

Extending SLURM for Dynamic Resource-Aware Adaptive Batch Sc...

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International Conference on High Performance Computing

作者： Mohak Chadha Jophin John Michael Gerndt Chair of Computer Architecture and Parallel Systems Technische Universität München Garching (near Munich) Germany

ISBN: (纸本)9781665422925;9781665446501

With the growing constraints on power budget and increasing hardware failure rates, the operation of future exascale systems faces several challenges. Towards this, resource awareness and adaptivity by enabling malleable jobs has been actively researched in the HPC community. Malleable jobs can change their computing resources at runtime and can significantly improve HPC system performance. However, due to the rigid nature of popular parallel programming paradigms such as MPI and lack of support for dynamic resource management in batch systems, malleable jobs have been largely unrealized. In this paper, we extend the SLURM batch system to support the execution and batch scheduling of malleable jobs. The malleable applications are written using a new adaptive parallel paradigm called Invasive MPI which extends the MPI standard to support resource-adaptivity at runtime. We propose two malleable job scheduling strategies to support performance-aware and power-aware dynamic reconfiguration decisions at runtime. We implement the strategies in SLURM and evaluate them on a production HPC system. Results for our performance-aware scheduling strategy show improvements in makespan, average system utilization, average response, and waiting times as compared to other scheduling strategies. Moreover, we demonstrate dynamic power corridor management using our power-aware strategy.

关键词： Runtime Processor scheduling parallel programming System performance High performance computing Production Dynamic scheduling

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Towards Autoscaling with Guarantees on Kubernetes Clusters

Towards Autoscaling with Guarantees on Kubernetes Clusters

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Autonomic Computing and Self-Organizing systems Companion (ACSOS-C), IEEE International Conference on

作者： Stephen Burroughs Helge Dickel Martin van Zijl Vladimir Podolskiy Michael Gerndt Robi Malik Panos Patros ORKA Cloud and Adaptive Systems Lab University of Waikato New Zealand Chair of Computer Architecture and Parallel Systems Technical University of Munich Germany

Autoscaling is used by cloud providers, microser-vices, and edge computing applications to respond to dynamic load fluctuations. A critical direction of research has focused on providing guarantees under uncertainty that the auto scaling system will work as intended-both at design-time and more importantly, at runtime. In this work, we evaluate the efficacy of three complementary methods: A) deterministic finite automata, B) probabilistic process algebra and C) Proportional-Integral-Derivative (PID) control. We experimentally evaluate their efficacy in modelling and verify autoscaling on clusters managed by Kubernetes. Our results suggest that deterministic modelling can provide theoretically optimal guarantees for small deployments; probabilistic algebras are able to capture stochastic behaviours, but benefit from deterministic templates; and control theory benefits by providing a black-box approach for modelling, verification and control.

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Memory leak detection algorithms in the cloud-based infrastructure

arXiv

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arXiv 2021年

作者： Jindal, Anshul Staab, Paul Kulkarni, Pooja Cardoso, Jorge Gerndt, Michael Podolskiy, Vladimir Chair of Computer Architecture and Parallel Systems TU Munich Garching Germany Huawei Munich Research Center Huawei Technologies Munich Germany

A memory leak in an application deployed on the cloud can affect the availability and reliability of the application. Therefore, to identify and ultimately resolve it quickly is highly important. However, in the production environment running on the cloud, memory leak detection is a challenge without the knowledge of the application or its internal object allocation details. This paper addresses this challenge of detection of memory leaks in cloud-based infrastructure without having any internal knowledge by introducing two novel machine learning based algorithms: Linear Backward Regression (LBR) and Precog and, their two variants: Linear Backward Regression with Change Points Detection (LBRCPD) and Precog with Maximum Filteration (PrecogMF). These algorithms only use one metric i.e the system's memory utilization on which the application is deployed for detection of a memory leak. The developed algorithm's accuracy was tested on 60 virtual machines manually labeled memory utilization data provided by Huawei and it was found that the proposed PrecogMF algorithm achieves the highest accuracy score of 85%. The same algorithm also achieves this by decreasing the overall compute time by 80% when compared to LBR's compute time. The paper also presents the different memory leak patterns found in the various memory leak applications and are further classified into different classes based on their visual representation. © 2021, CC BY.

关键词： Regression analysis

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Real-Time Capability of DLR’S Beamforming Synthetic Aperture Radar Processing architecture

Real-Time Capability of DLR’S Beamforming Synthetic Apertur...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Maron Schlemon Martin Schulz Rolf Scheiber Marc Jäger Joel Amao Oliva German Aerospace Center (DLR) Microwaves and Radar Institute Germany Chair of Computer Architecture and Parallel Systems Technical University of Munich Germany

ISBN: (数字)9798350374513

ISBN: (纸本)9798350374520

Synthetic Aperture Radar (SAR) enables the generation of realistic and high-resolution 2D or 3D representations of landscapes. Typically, radar instruments are deployed in specially equipped, low-flying aircraft that capture a significant amount of raw data, necessitating image reconstruction processing. However, the aircraft’s limited onboard processing capabilities (power, size, weight, cooling, and communication bandwidth to ground stations) and the need to generate multiple SAR products, such as slant-range and geo-coded images during a single flight, require efficient onboard processing and transmission to the ground station. This paper outlines the processing architecture of the digital beamforming SAR (DBFSAR) employed by the German Aerospace Center (DLR) and the specific measures implemented to enable onboard processing. We elucidate the essential software optimizations and their integration into the SAR onboard routines, facilitating (near) real-time capability under certain conditions. Furthermore, we share the insights gained from our work and discuss their applicability to other processing scenarios with limited resource availability.

关键词： Three-dimensional displays Array signal processing Instruments computer architecture Radar imaging Real-time systems Software

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FedLesScan: Mitigating Stragglers in Serverless Federated Learning

arXiv

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arXiv 2022年

作者： Elzohairy, Mohamed Chadha, Mohak Jindal, Anshul Grafberger, Andreas Gu, Jianfeng Gerndt, Michael Abboud, Osama Chair of Computer Architecture and Parallel Systems Technische Universität München Garching Munich Germany Huawei Technologies Munich Germany

Federated Learning (FL) is a machine learning paradigm that enables the training of a shared global model across distributed clients while keeping the training data local. While most prior work on designing systems for FL has focused on using stateful always running components, recent work has shown that components in an FL system can greatly benefit from the usage of serverless computing and Function-as-a-Service technologies. To this end, distributed training of models with serverless FL systems can be more resource-efficient and cheaper than conventional FL systems. However, serverless FL systems still suffer from the presence of stragglers, i.e., slow clients due to their resource and statistical heterogeneity. While several strategies have been proposed for mitigating stragglers in FL, most methodologies do not account for the particular characteristics of serverless environments, i.e., cold-starts, performance variations, and the ephemeral stateless nature of the function instances. Towards this, we propose FedLesScan, a novel clustering-based semi-asynchronous training strategy, specifically tailored for serverless FL. FedLesScan dynamically adapts to the behaviour of clients and minimizes the effect of stragglers on the overall system. We implement our strategy by extending an open-source serverless FL system called FedLess. Moreover, we comprehensively evaluate our strategy using the 2nd generation Google Cloud Functions with four datasets and varying percentages of stragglers. Results from our experiments show that compared to other approaches FedLesScan reduces training time and cost by an average of 8% and 20% respectively while utilizing clients better with an average increase in the effective update ratio of 17.75%. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Real-Time Capability of Dlr's Beamforming Synthetic Aperture Radar Processing architecture

Real-Time Capability of Dlr's Beamforming Synthetic Aperture...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

ISBN: (数字)9798350320107

ISBN: (纸本)9798350331745

Synthetic Aperture Radar (SAR) enables the generation of realistic and high-resolution 2D or 3D representations of landscapes. Typically, radar instruments are deployed in specially equipped, low-flying aircraft that capture a significant amount of raw data, necessitating image reconstruction processing. However, the aircraft's limited onboard processing capabilities (power, size, weight, cooling, and communication bandwidth to ground stations) and the need to generate multiple SAR products, such as slant-range and geo-coded images during a single flight, require efficient onboard processing and transmission to the ground station. This paper outlines the processing architecture of the digital beamforming SAR (DBFSAR) employed by the German Aerospace Center (DLR) and the specific measures implemented to enable onboard processing. We elucidate the essential software optimizations and their integration into the SAR onboard routines, facilitating (near) real-time capability under certain conditions. Furthermore, we share the insights gained from our work and discuss their applicability to other processing scenarios with limited resource availability.

关键词： Three-dimensional displays Array signal processing Instruments computer architecture Radar imaging Real-time systems Software

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Scalable Infrastructure and Workflow for Anomaly Detection in an Automotive Industry

Scalable Infrastructure and Workflow for Anomaly Detection i...

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Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： Anshul Jindal Michael Gerndt Mario Bauch Hachim Haddouti Chair of Computer Architecture and Parallel Systems Technical University of Munich Garching (near Munich) Germany OTD Platform and Services Munich Germany

ISBN: (数字)9781728142104

ISBN: (纸本)9781728142111

Anomalies are unexpected instances which significantly deviate from the normal patterns formed by the majority of a dataset. The more an observation deviate from the normal pattern, the more likely it is an anomaly. The continuous increase in the number of car models and configuration possibilities has led to continuous increase in the complexity of logistics supply chain and production. Consequently, it has become difficult to manage the whole IT Landscape, a small anomaly/failure somewhere in the system could lead to a huge loss of money. Therefore, to identify and ultimately resolve quickly a problem in such a system is highly important. This paper addresses the challenge of identifying anomalies in a scalable way. The new data collected suffers from the problem of lack of labels for training. This challenge is addressed in the developed solution by using multiple unsupervised algorithms and reporting those observation as anomalies which are commonly reported as anomalies by all the algorithms. The developed solution also tackles the problem of data heterogeneity and big size by using Spark underneath for scalable data processing. Scalability test results demonstrate the reduction in training time of 100 transactions by 80% when using 10 cores instead of using 1 core. The results of the study have also pointed out that increasing the number of cores does not necessarily means reduction in the overall execution time, there are other factors like communications between the cores, non-spark related processing tasks, etc which can also influence the execution time.

关键词： Anomaly detection Time series analysis Industries Production Data models Support vector machines Automotive engineering

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Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments

Apodotiko: Enabling Efficient Serverless Federated Learning ...

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IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Mohak Chadha Alexander Jensen Jianfeng Gu Osama Abboud Michael Gerndt Chair of Computer Architecture and Parallel Systems Technische Universität München Garching (near Munich) Germany Huawei Technologies Munich Germany

ISBN: (数字)9798350395662

ISBN: (纸本)9798350395679

Federated Learning (FL) is an emerging machine learning paradigm that enables the collaborative training of a shared global model across distributed clients while keeping the data decentralized. Recent works on designing systems for efficient FL have shown that utilizing serverless computing technologies, particularly Function-as-a-Service (FaaS) for FL, can enhance resource efficiency, reduce training costs, and alleviate the complex infrastructure management burden on data holders. However, current serverless FL systems still suffer from the presence of stragglers, i.e., slow clients that impede the collaborative training process. While strategies aimed at mitigating stragglers in these systems have been proposed, they overlook the diverse hardware resource configurations among FL clients. To this end, we present Apodotiko, a novel asynchronous training strategy designed for serverless FL. Our strategy incorporates a scoring mechanism that evaluates each client’s hardware capacity and dataset size to intelligently prioritize and select clients for each training round, thereby minimizing the effects of stragglers on system performance. We comprehensively evaluate Apodotiko across diverse datasets, considering a mix of CPU and GPU clients, and compare its performance against five other FL training strategies. Results from our experiments demonstrate that Apodotiko outperforms other FL training strategies, achieving an average speedup of 2.75x and a maximum speedup of 7.03x. Furthermore, our strategy significantly reduces cold starts by a factor of four on average, demonstrating suitability in serverless environments.

关键词： Training Federated learning System performance Prevention and mitigation Collaboration Serverless computing Graphics processing units Distributed databases Hardware Data models

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