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Hierarchical Scheduling of Hybrid DNN Tasks in Embedded Real-Time Systems 29

Hierarchical Scheduling of Hybrid DNN Tasks in Embedded Real...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Feng, Jiaxin Zhu, Kun Zhang, Tong Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350330717

With the widespread application of deep learning (DL) technology in the modern Internet of Things (IoT) areas such as autonomous driving, smart cities and homes, embedded real-time systems are increasingly used at the edge of the network to complete various hybrid DNN tasks. Although embedded real-time systems are equipped with heterogeneous CPU and GPU cores to reduce the response time of inference jobs, the computing resources of heterogeneous devices are not fully utilized, and there is still plenty of room for schedulability to be improved. In this paper, we propose a layer-based hybrid deep neural network (DNN) tasks scheduling algorithm in embedded real-time systems (LHTS) that maps DNN layers to CPU and GPU devices and regulates their start time to avoid confliction. We evaluate LHTS through extensive simulations. The experimental results show that LHTS can achieve more sufficient use of heterogeneous CPU and GPU resources in embedded real-time systems, reduce the worst-case execution time and enhance the schedulability performance of hybrid DNN tasks. © 2023 IEEE.

关键词： deep neural network optimization real-time system resource allocation task scheduling

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Improved DQN-based Computation Offloading Algorithm in MEC Environment 28

Improved DQN-Based Computation Offloading Algorithm in MEC E...

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IEEE 28th International conference on parallel and distributed Systems (IEEE ICPADS)

作者： Zhao, Zheyu Cheng, Hao Xu, Xiaohua Univ Sci & Technol China Sch Comp Sci & Technol Hefei Peoples R China Delft Univ Technol Dept Elect Engn Math & Comp Sci Delft Netherlands

ISBN: (纸本)9781665473156

Massive terminal users have brought explosive need of data residing at edge of overall network. Multiple Mobile Edge Computing (MEC) servers are built in/near base station to meet this need. However, optimal distribution of these servers to multiple users in real time is still a problem. Reinforcement Learning (RL) as a framework to solve interaction problem is a promising solution. In order to apply RL based algorithm into a multi-agent environment, we propose an iterative scheme: select individual users with priorities to interact with the environment iteratively one at a time. Furthermore, we tried to optimize the overall system performance based on this scheme. Hence, we construct three objective system performance indicators: average processing cost, delay and energy consumption, improve the existing Deep Q-learning network (DQN) by using the cost as reward function, changing the fixed exploitation rate into dynamic one that associated with reward and episode time. In order to explore the performance potential of the proposed algorithm, we have simulated the proposed algorithm, DQN algorithm and greedy algorithm under different users and data sizes. The results show that the proposed algorithm had reduced at least 12% of system average processing cost comparing to the greedy algorithm. It also outperform the greedy algorithm and DQN algorithm in delay and energy consumption significantly.

关键词： Mobile Edge Computing Computation Offloading Reinforcement Learning Deep Q-Learning network

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TreeOptimizer: A classifier-based task scheduling framework 23

TreeOptimizer: A classifier-based task scheduling framework

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24th International conference on distributed Computing and networking (ICDCN)

作者： Venkatesh, Pruthvi Raj Srinivas, M. Krishna, P. Radha Openstream Technol Bangalore Karnataka India Natl Inst Technol Warangal Andhra Pradesh India

ISBN: (纸本)9781450397964

distributed Computing (DC) involves a collection of tasks (or modules) executed in parallel on different compute nodes connected through a network. Cloud Service providers (CSP) such as Azure[1], Amazon[2], and Google[3] are providing DC platforms as PaaS (Platform As A Service) offerings. These cloud platforms reduce implementation costs but have a significant drawback as these services can be configured to spawn only a single type of compute node for executing all the tasks in the DC environment. These drawback lead to inefficiency in execution cost and time as each task will have specific compute node requirements. This paper presents a novel framework called TreeOptimizer(TO) to resolve these shortcomings. TO uses a classifier-based dynamic task scheduling to determine the best available node to perform the task. The framework has been tested in Azure Batch[1] for an Oil Industry use case for extracting data from scanned images. Experimental results indicate that TO significantly reduces the overall execution cost by 68% and processing time by 8%. Although this paper uses Batch Service to explain the proposed framework, it can be applied to other PaaS DC platforms.

关键词： distributed Computing Azure Batch Decision Tree PaaS CSP

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Exploiting Fine-Grained Structured Pruning for Efficient Inference on CNN Model 29

Exploiting Fine-Grained Structured Pruning for Efficient Inf...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Wu, Cheng-Hung Hong, Ding-Yong Liu, Pangfeng Wu, Jan-Jan National Taiwan University Department of Computer Science and Information Engineering Taiwan Institute of Information Science Academia Sinica Taiwan

ISBN: (纸本)9798350330717

Weight pruning is a technique to remove redundant or unimportant weights from the network. It can help reduce the size and computational cost of neural networks while preserving their accuracy. In this paper, we aim to design efficient CNN models with N:M pruning on the CPU. We propose a dynamic programming algorithm to find a good sparsity ratio for every layer under a total time budget based on the execution times and L1 norm of layers. After deciding the sparsity ratio of each layer, we leverage the auto-tuner of the TVM compiler to search for an optimization schedule of the pruned convolution to accelerate fine-grained pruned models. Experimental results show that our scheme can achieve 0.35% accuracy improvement and a 1.55× speedup on VGG-16 with ImageNet than the dense model. © 2023 IEEE.

关键词： deep neural network weight pruning

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Unsupervised Graph-Sequence Anomaly Detection for 5G Core network Control Plane Traffic 29

Unsupervised Graph-Sequence Anomaly Detection for 5G Core Ne...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Wang, Min Li, Peng Cheng, Zhang Liu, Wenmao Nie, Lei Bao, Haizhou Liu, Qin Zhang, Kai Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China Nsfocus Technologies Group Co. Ltd China Wuhan University School of Cyber Science and Engineering China

ISBN: (纸本)9798350330717

5G Core network (5GC) employs a Service based Architecture (SBA). This architecture decomposes the control plane into multiple independent network Functions (NFs). NFs open interfaces to provide services to other NFs, which makes the control plane more susceptible to external malicious attacks. However, existing anomaly detection methods focus more on traffic statistics features and are difficult to apply to the 5GC control plane. In this paper, we proposed GSAD, a Graph-Sequence analysis-based Anomaly Detection method for 5GC control plane traffic. We model control plane traffic as a directed graph to depict topological and NF interaction information. Further, we use the normalizing flows with temporal dependencies to mine the sequential information in the traffic. GSAD combines the topological and sequential information to provide fine-grained detection. We evaluate our proposed framework on the 5GC testbed using Free5GC and UERANSIM in various scenarios. Experimental results demonstrate that our framework outperforms the baselines significantly in terms of detection performance. © 2023 IEEE.

关键词： 5G core network anomaly detection control plane graph neural network normalizing flows

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Exploring Load Forecasting: Bridging Statistical Methods to Deep Learning Techniques in Real-World Environment 20

Exploring Load Forecasting: Bridging Statistical Methods to ...

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20th Annual International conference on distributed Computing in Smart Sensor Systems and the Internet of Things (DCOSS-IoT)

作者： Makris, Ioannis Moschos, Nikolaos Radoglou-Grammatikis, PaPanagiotis Andriopoulos, Nikolaos Tzanis, Nikolaos Malamaki, Kyriaki-Nefeli Fotopoulou, Maria Kaousias, Konstantinos Tompros, George Lampsidis Sarigiannidis, Panagiotis MetaMind Innovat Kozani Greece Ctr Res & Technol Hellas CERTH Athens Greece Hellen Elect Distribut Network Operator HEDNO Athens Greece Univ Western Macedonia Dept Elect & Comp Engn Kozani Greece K3Y Ltd Sofia Bulgaria

ISBN: (纸本)9798350369458;9798350369441

Load forecasting has a significant impact on energy management and planning, facilitating efficient allocation of resources and grid operations. In this study, a comparative analysis of traditional statistical methods and deep learning techniques is conducted utilizing a real-world dataset from the Ikaria islanded grid. This paper focuses on four different forecasting approaches: Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX), Long Short-Term Memory (LSTM) networks, and Deep Neural networks (DNN). Through the appropriate processing of the data, extensive experimentation took place, aiming to capture the complex and nonlinear patterns of the dataset. The results indicated that LSTM and DNN outperformed both ARIMA and SARIMAX in all three evaluation metrics, achieving 0.13, 0.09, and 2.11%, RMSE, MAE, and MAPE, respectively. As a result, this study validates the superiority of deep learning techniques in real-world islanded grid environments being capable of accurately predicting future load values based on historical data.

关键词： Deep Neural networks Electricity Demand Prediction Load Forecasting Long Short-term Memory Time Series Analysis

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K-means ++ and Reinforcement Learning-based Clustering Method to Achieve Energy Efficiency in Underwater Acoustic Sensor networks 21

K-means ++ and Reinforcement Learning-Based Clustering Metho...

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21st IEEE International Symposium on parallel and distributed processing with Applications, 13th IEEE International conference on Big Data and Cloud Computing, 16th IEEE International conference on Social Computing and networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xiong, Tao Hou, Rui Zhou, Lingyun Xing, Guanglin Zeng, Deze South-Central Minzu University College of Computer Science Wuhan China China University of Geosciences Computer School Wuhan China

ISBN: (纸本)9798350329223

In recent years, underwater acoustic sensor networks (UASNs) have had a wide range of applications in the fields of underwater environment sensing, marine resource exploitation and water quality analysis. Sensor nodes operate in an underwater environment and are powered by embedded batteries, making it very difficult to replenish power. It is thus important to efficiently improve sensor energy efficiency in UASNs and to extend the network lifetime. Clustering can effectively reduce UASN energy consumption by dividing sensor nodes into clusters;however, energy efficiency is always affected by the imbalance in energy consumption due to the unreasonable selection of cluster heads (CHs) and routes. In this paper, a K-means ++ and reinforcement learning-based clustering (KRC) method is proposed to improve energy efficiency. First, the UASN clusters are uniformly distributed using the K-means ++ algorithm. Then, a combination of clustering and reinforcement learning is applied to CH selection, which calculates the energy of intercluster data transmission when each node in the cluster is a CH based on the node's residual energy and location and selects the appropriate CHs to equalize the energy consumption of each network node. The optimal transmission route constructed by reinforcement learning is used to select suitable nodes for relaying data for sensor nodes. The simulation results show that the KRC method effectively balances UASN node energy consumption and improves energy efficiency, thereby extending network survival. © 2023 IEEE.

关键词： Energy efficiency

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A Caching Placement Strategy based on Dynamic Router Hierarchy for Named Data networking 29

A Caching Placement Strategy Based on Dynamic Router Hierarc...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Wang, Haotian Zhao, Mingzhu Yu, Meiju Inner Mongolia University College of Computer Science Hohhot China

ISBN: (纸本)9798350330717

Named Data networking (NDN), as one of the most promising future Internet architectures, can effectively relieve the bandwidth pressure caused by the rapid growth of network users and network traffic. It uses a content-based communication framework, in which the routers in NDN have the capacity of caching contents passing by and provide data services for incoming requests. The caching strategies, however, currently widely used in NDN are facing several challenges including low cache utilization, long service response time and so on. To address the above issue, considering the dynamic changes of routers' location in different request/reply processes, a caching placement strategy based on dynamic router hierarchy (DRH) is proposed to determine the caching routers on the data return path. The strategy includes a dynamic hierarchical algorithm of routers, a content popularity classification algorithm and a cache-matching algorithm. By analyzing the users' requests for different contents and the location of routers, DRH determines the content popularity and importance of routers, and then caches the contents of different popularity levels on the corresponding routers. The level of each router is determined only in each request/reply process, that is, the same router has different levels in different request/reply paths. We implement DRH and conduct performance evaluations based on ndnSIM. The simulation result shows that DRH outperforms baseline solutions in terms of cache-hit rate, network throughput and average cache-hit distance. © 2023 IEEE.

关键词： caching placement strategy dynamic router hierarchy NDN

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VeriChroma: Ownership Verification for Federated Models via RGB Filters 30th

VeriChroma: Ownership Verification for Federated Models via ...

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30th European conference on parallel and distributed processing (Euro-Par)

作者： Nie, Hewang Lu, Songfeng Wang, Mu Xiao, Jue Lu, Zhi Yi, Zepu Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan 430074 Peoples R China Univ South China Affiliated South China Hosp Clin Res Inst Hengyang 421001 Hunan Peoples R China

ISBN: (纸本)9783031697654;9783031697661

The rapid evolution of artificial intelligence (AI), especially in deep learning, is significantly driven by big data. However, the intensive resources required for training deep neural networks (DNN) highlight the urgent need for effective model protection and ownership verification. Current neural network watermarking methods fall short in federated learning contexts. This paper introduces VeriChroma, an innovative framework crafted to secure DNN models and affirm ownership within such environments. VeriChroma enables clients to embed and verify private ID-based watermarks independently, ensuring straightforward ownership claims. Through strategic image blocking and positional mapping, it overcomes conflicts between client constraints, ensuring tailored watermark integration. Furthermore, VeriChroma utilizes RGB filters for watermark triggers, bolstering both the robustness and stealth of the watermarking process. Our findings underscore VeriChroma's effectiveness and practicality, showcasing its potential to enhance DNN model security, resolve federated learning disputes, and provide secure, unobtrusive watermarking, marking a significant advancement in federated learning security and intellectual property rights protection.

关键词： Deep neural network Federated learning Model watermarking Ownership Verification Backdoor watermark

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GDL-GNN: Applying GPU Dataloading of Large Datasets for Graph Neural network Inference 30th

GDL-GNN: Applying GPU Dataloading of Large Datasets for Grap...

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30th European conference on parallel and distributed processing (Euro-Par)

作者： Dang, Haoran Wu, Meng Yan, Mingyu Ye, Xiaochun Fan, Dongrui Chinese Acad Sci SKLP Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9783031697654;9783031697661

Graph neural networks (GNNs) have emerged as a popular choice for analyzing structured data organized as graphs. Nevertheless, GNN models tend to be shallow, failing to fully exploit the capabilities of modern GPUs. Our motivational tests reveal that GPU dataloading for GNN inference yields remarkable performance enhancements when both the graph topology and features reside in GPU memory. Unfortunately, the use of this approach is hindered by the large size of real-world graph datasets. To address this limitation, we introduce GDL-GNN, a partition-based method that incorporates all essential information for inference within each subgraph. It thus combines the efficiency of GPU dataloading with layerwise inference, while maintaining the accuracy of full-neighbor inference. Additional optimization enables GDL-GNN to avoid unnecessary representation computation on halo nodes and to conceal file loading time. Evaluation shows the effectiveness of GDL-GNN in both single- and multi-GPU scenarios, revealing a reduction in inference time of up to 59.9% without compromising accuracy.

关键词： GNN inference Graph partition Multi-GPU

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