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MicroSplit: Efficient Splitting of Microservices on Edge Clouds

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MicroSplit: Efficient Splitting of Microservices on Edge Clo...

IEEE/ACM Symposium on Edge Computing (SEC)

作者： Ali Rahmanian Ahmed Ali-Eldin Björn Skubic Erik Elmroth Department of Computing Science Umeå University Umeå Sweden Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Cloud Systems and Platforms Ericsson Research Stockholm Sweden

ISBN: (数字)9781665486118

ISBN: (纸本)9781665486125

Edge cloud systems reduce the latency between users and applications by offloading computations to a set of small-scale computing resources deployed at the edge of the network. However, since edge resources are constrained, they can become saturated and bottlenecked due to increased load, resulting in an exponential increase in response times or failures. In this paper, we argue that an application can be split between the edge and the cloud, allowing for better performance compared to full migration to the cloud, releasing precious resources at the edge. We model an application's internal call-Graph as a Directed-Acyclic-Graph. We use this model to develop MicroSplit, a tool for efficient splitting of microservices between constrained edge resources and large-scale distant backend clouds. MicroSplit analyzes the dependencies between the microservices of an application, and using the Louvain method for community detection-a popular algorithm from Network science-decides how to split the microservices between the constrained edge and distant data centers. We test MicroSplit with four microservice based applications in various realistic cloud-edge settings. Our results show that Microsplit migrates up to 60 % of the microservices of an application with a slight increase in the mean-response time compared to running on the edge, and a latency reduction of up to 800 % compared to migrating the entire application to the cloud. Compared to other methods from the State-of-the-Art, MicroSplit reduces the total number of services on the edge by up to five times, with minimal reduction in response times.

关键词： Data centers Image edge detection Microservice architectures Complex networks Time factors Resource management Load modeling

Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

Climatebug: A Data-Driven Framework for Analyzing Bank Reporting Through a Climate Lens

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SSRN

SSRN 2022年

作者： Yu, Yinan Scheidegger, Samuel Elliott, Jasmine Löfgren, Åsa Department of Computer Science and Engineering Chalmers University of Technology Sweden Asymptotic AI Sweden Department of Philosophy Linguistics and Theory of Science University of Gothenburg Sweden Department of Economics University of Gothenburg Sweden

In this article we present a framework, climateBUG, that offers a framework to detect latent information about how banks discuss their activities related to climate change. The framework is based on a corpus of European banks’ annual reports, and it consists of an ingestion pipeline to extract information from the corpus data, a configurable database and a set of API’s. In addition, climateBUG provides two standalone components that can be used for customized analyses: 1. A unique annotated corpus with the foci of climate change and finance with approximately 1.1M (1,070,070) data points offered open access that can be used as training data for further model optimization. 2. A deep learning model adapted to the corpus. Benchmarking of the classification performance of the model shows that it outperforms other models with similar foci. The framework has been developed using an interdisciplinary approach with relevant domain knowledge from financial reporting, climate economics, and advanced computational linguistics natural language processing. The intended users of climateBUG include academic researchers, government representatives, managers from the the banking industry, and journalists. © 2022, The Authors. All rights reserved.

关键词： Climate change

Mapping Africa’s Settlements: High Resolution Urban and Rural Map by Deep Learning and Satellite Imagery

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arXiv 2024年

作者： Kakooei, Mohammad Bailie, James Söderberg, Albin Becevic, Albin Daoud, Adel Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Statistics Harvard University Massachusetts United States Institute for Analytical Sociology Linköping University Sweden Center for Advanced Study in the Behavioral Sciences Stanford University United States The AI and Global Development Lab Sweden

Accurate Land Use and Land Cover (LULC) maps are essential for understanding the drivers of sustainable development, in terms of its complex interrelationships between human activities and natural resources. However, existing LULC maps often lack precise urban and rural classifications, particularly in diverse regions like Africa. This study presents a novel construction of a high-resolution rural-urban map using deep learning techniques and satellite imagery. We developed a deep learning model based on the DeepLabV3 architecture, which was trained on satellite imagery from Landsat-8 and the ESRI LULC dataset, augmented with human settlement data from the GHS-SMOD. The model utilizes semantic segmentation to classify land into detailed categories, including urban and rural areas, at a 10-meter resolution. Our findings demonstrate that incorporating LULC along with urban and rural classifications significantly enhances the model’s ability to accurately distinguish between urban, rural, and non-human settlement areas. Therefore, our maps can support more informed decision-making for policymakers, researchers, and stakeholders. We release a continent wide urban-rural map, covering the period 2016 and 2022. © 2024, CC BY.

关键词： Landsat

FsaNet: Frequency Self-attention for Semantic Segmentation

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arXiv 2022年

作者： Zhang, Fengyu Panahi, Ashkan Gao, Guangjun The School of Traffic & Transportation Engineering Central South University Changsha410075 China The Department of Computer Science and Engineering Chalmers University of Technology GöteborgSE-412 96 Sweden

Considering the spectral properties of images, we propose a new self-attention mechanism with highly reduced computational complexity, up to a linear rate. To better preserve edges while promoting similarity within objects, we propose individualized processes over different frequency bands. In particular, we study a case where the process is merely over low-frequency components. By ablation study, we show that low frequency self-attention can achieve very close or better performance relative to full frequency even without retraining the network. Accordingly, we design and embed novel plug- and-play modules to the head of a CNN network that we refer to as FsaNet. The frequency self-attention 1) requires only a few low frequency coefficients as input, 2) can be mathematically equivalent to spatial domain self-attention with linear structures, 3) simplifies token mapping (1×1 convolution) stage and token mixing stage simultaneously. We show that frequency self-attention requires 87.29% ∼ 90.04% less memory, 96.13% ∼ 98.07% less FLOPs, and 97.56% ∼ 98.18% in run time than the regular self-attention. Compared to other ResNet101-based self-attention networks, FsaNet achieves a new state-of-the-art result (83.0% mIoU) on Cityscape test dataset and competitive results on ADE20k and VOCaug. FsaNet can also enhance MASK R-CNN for instance segmentation on COCO. In addition, utilizing the proposed module, Segformer can be boosted on a series of models with different scales, and Segformer-B5 can be improved even without retraining. Code is accessible at https://***/zfy-csu/FsaNet. © 2022, CC BY-NC-SA.

关键词： Semantic Segmentation

Cycle-object consistency for image-to-image domain adaptation

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Pattern Recognition 2023年第1期138卷

作者： Lin, Che-Tsung Kew, Jie-Long Chan, Chee Seng Lai, Shang-Hong Zach, Christopher Department of Electrical Engineering Chalmers University of Technology Sweden CISiP Faculty of Comp Sci. and Info. Tech. Universiti Malaya Malaysia Microsoft AI R&D Center Taiwan Department of Computer Science National Tsing Hua University Taiwan

Recent advances in generative adversarial networks (GANs) have been proven effective in performing domain adaptation for object detectors through data augmentation. While GANs are exceptionally successful, those methods that can preserve objects well in the image-to-image translation task usually require an auxiliary task, such as semantic segmentation to prevent the image content from being too distorted. However, pixel-level annotations are difficult to obtain in practice. Alternatively, instance-aware image-translation model treats object instances and background separately. Yet, it requires object detectors at test time, assuming that off-the-shelf detectors work well in both domains. In this work, we present AugGAN-Det, which introduces Cycle-object Consistency (CoCo) loss to generate instance-aware translated images across complex domains. The object detector of the target domain is directly leveraged in generator training and guides the preserved objects in the translated images to carry target-domain appearances. Compared to previous models, which e.g., require pixel-level semantic segmentation to force the latent distribution to be object-preserving, this work only needs bounding box annotations which are significantly easier to acquire. Next, as to the instance-aware GAN models, our model, AugGAN-Det, internalizes global and object style-transfer without explicitly aligning the instance features. Most importantly, a detector is not required at test time. Experimental results demonstrate that our model outperforms recent object-preserving and instance-level models and achieves state-of-the-art detection accuracy and visual perceptual quality. © 2023 Elsevier Ltd

关键词： Object detection

Distributed DoS Detection in IoT Networks Using Intelligent Machine Learning Algorithms 2

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Distributed DoS Detection in IoT Networks Using Intelligent ...

2nd International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2021

作者： Binny, S. Pendyala, Shamili Srimani Pimo, S. John Aurelia, Sagaya Reddy, P Rahul Satyanarayana, Damaraju Sri Sai Kristu Jyoti College of Management and Technology Department of Computer Science Kerala India Institute of Aeronautical Engineering Department of ECE Hyderabad India St. Xavier's Catholic College of Engineering Nagercoil India Christ University Bangalore India Geethanjali Institute of Science and Technology ECE Nellore India Chalmers University of Technology Goteborg Sweden

ISBN: (纸本)9781665482974

The threat of a Distributed Denial of Service (DDoS) attack on web-based services and applications is grave. It only takes a few minutes for one of these attacks to cripple these services, making them unavailable to anyone. The problem has further persisted with the widespread adoption of insecure Internet of Things (IoT) devices across the Internet. In addition, many currently used rule-based detection systems are weak points for attackers. We conducted a comparative analysis of ML algorithms to detect and classify DDoS attacks in this paper. These classifiers compare Nave Bayes with J48 and Random Forest with ZeroR ML as well as other machine learning algorithms. It was found that using the PCA method, the optimal number of features could be found. ML has been implemented with the help of the WEKA tool. © 2021 IEEE.

关键词： Denial-of-service attack

A Taxonomy of Information Attributes for Test Case Prioritisation: Applicability, Machine Learning

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arXiv 2022年

作者： Ramírez, Aurora Feldt, Robert Romero, José Raúl Chalmers University of Technology Sweden Campus de Rabanales Edificio Marie Curie CórdobaE-14071 Spain Software Engineering Division Department of Computer Science and Engineering Chalmers University of Technology Jupiter Building Lindholmen Campus GothenburgSE-412 96 Sweden

Most software companies have extensive test suites and re-run parts of them continuously to ensure recent changes have no adverse effects. Since test suites are costly to execute, industry needs methods for test case prioritisation (TCP). Recently, TCP methods use machine learning (ML) to exploit the information known about the system under test (SUT) and its test cases. However, the value added by ML-based TCP methods should be critically assessed with respect to the cost of collecting the information. This paper analyses two decades of TCP research, and presents a taxonomy of 91 information attributes that have been used. The attributes are classified with respect to their information sources and the characteristics of their extraction process. Based on this taxonomy, TCP methods validated with industrial data and those applying ML are analysed in terms of information availability, attribute combination and definition of data features suitable for ML. Relying on a high number of information attributes, assuming easy access to SUT code and simplified testing environments are identified as factors that might hamper industrial applicability of ML-based TCP. The TePIA taxonomy provides a reference framework to unify terminology and evaluate alternatives considering the cost-benefit of the information attributes. © 2022, CC BY-NC-ND.

关键词： Software testing

YOLO-LLTS: Real-Time Low-Light Traffic Sign Detection via Prior-Guided Enhancement and Multi-Branch Feature Interaction

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arXiv 2025年

作者： Lin, Ziyu Wu, Yunfan Ma, Yuhang Chen, Junzhou Zhang, Ronghui Wu, Jiaming Yin, Guodong Lin, Liang Guangdong Key Laboratory of Intelligent Transportation System School of intelligent systems engineering Sun Yat-sen University Guangzhou510275 China Department of Architecture and Civil Engineering Chalmers University of Technology Sven Hultins gata 6 GothenburgSE-412 96 Sweden School of Mechanical Engineering Southeast University Nanjing211189 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China

Detecting traffic signs effectively under low-light conditions remains a significant challenge. To address this issue, we propose YOLO-LLTS, an end-to-end real-time traffic sign detection algorithm specifically designed for low-light environments. Firstly, we introduce the High-Resolution Feature Map for Small Object Detection (HRFM-TOD) module to address indistinct small-object features in low-light scenarios. By leveraging high-resolution feature maps, HRFM-TOD effectively mitigates the feature dilution problem encountered in conventional PANet frameworks, thereby enhancing both detection accuracy and inference speed. Secondly, we develop the Multi-branch Feature Interaction Attention (MFIA) module, which facilitates deep feature interaction across multiple receptive fields in both channel and spatial dimensions, significantly improving the model’s information extraction capabilities. Finally, we propose the Prior-Guided Enhancement Module (PGFE) to tackle common image quality challenges in low-light environments, such as noise, low contrast, and blurriness. This module employs prior knowledge to enrich image details and enhance visibility, substantially boosting detection performance. To support this research, we construct a novel dataset, the Chinese Nighttime Traffic Sign Sample Set (CNTSSS), covering diverse nighttime scenarios, including urban, highway, and rural environments under varying weather conditions. Experimental evaluations demonstrate that YOLO-LLTS achieves state-of-the-art performance, outperforming the previous best methods by 2.7% mAP50 and 1.6% mAP50:95 on TT100K-night, 1.3% mAP50 and 1.9% mAP50:95 on CNTSSS, and achieving superior results on the CCTSDB2021 dataset. Moreover, deployment experiments on edge devices confirm the real-time applicability and effectiveness of our proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature extraction