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Online Evolutionary Batch Size Orchestration for Scheduling Deep Learning Workloads in GPU Clusters 21

Online Evolutionary Batch Size Orchestration for Scheduling ...

引用

international conference for High Performance computing, networking, Storage and Analysis (SC21)

作者： Bian, Zhengda Li, Shenggui Wang, Wei You, Yang Natl Univ Singapore Singapore Singapore ByteDance Singapore Singapore

ISBN: (纸本)9781450384421

Efficient CPU resources is essential to maximize resource utilization and save training the increasing amount of deep learning workloads in shared GPU clusters. Existing GPU schedulers largely rely on static policies to leverage the performance characteristics of deep learning jobs. However, they can hardly reach optimal efficiency due to the lack of elasticity. To address the problem, we propose ONES, an ONline Evolutionary Scheduler for elastic batch size orchestration. ONES automatically manages the elasticity of each job based on the training batch size, so as to maximize CPU utilization and improve scheduling efficiency. It determines the batch size for each job through an online evolutionary search that can continuously optitnize the scheduling decisions. We evaluate the effectiveness of ONES with 64 CPUs on TACC's Longhorn supercomputers. The results show that ONES can outpernqm the prior deep learning schedulers with a significantly shorter average job completion time.

关键词： Deep learning resource scheduling evolutionary search distributed training

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Blockchain-based Data Quality Assessment to Improve distributed Machine Learning

Blockchain-based Data Quality Assessment to Improve Distribu...

引用

international conference on computing, networking and Communications (ICNC)

作者： Yao Du Zehua Wang Cyril Leung Leung Victor C.M. Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Data quality assessment is critical for distributed machine learning (DML). Data collected from heterogeneous Internet of things (IoT) devices may contain biased information that decreases the prediction accuracy of DML models. To address these challenges, we propose a blockchain-based approach to assess the quality of data that are not independent and identically distributed (non-IID). A blockchain running atop mobile edge computing (MEC) is helpful to protect privacy, security, and integrity of healthcare data when IoT devices are connected to MEC servers. Therefore, it is critical to integrate data quality assessment module on blockchain when building a blockchain-enabled DML system. In this paper, we jointly consider information loss and marginal utility of non-IID data samples. Specifically, we use Kullback-Leibler (KL) divergence to evaluate the information loss between IID and non-IID data samples and apply the reciprocal of data quantity to model the marginal utility of data samples. Human activities and handwritten digit recognition data sets are used for performance evaluations. Experiments show that our proposed scheme outperforms benchmarks regarding model test accuracy on various non-IID data samples.

关键词： Data integrity Computational modeling Machine learning Consensus algorithm Prediction algorithms Data models Blockchains

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A Hybrid Approach to Protect Server from IP Spoofing Attack

A Hybrid Approach to Protect Server from IP Spoofing Attack

引用

2022 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Parekh, Varsha Saravanan, M. SRM Institute of Science and Technology School of Computing Department of Networking and Communications Tamil Nadu Chennai India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

The rapid expansion of the internet has resulted in distributed denial of service cyberattacks being one of the most critical problems in a data centre environment where numerous servers are placed. A distributed Denial of Service attack especially IP spoofing, carried out by countless specialists, creates a significant number of packets, which can quickly exhaust the processing and communication capabilities of a target in a short amount of time. Defending against such attack require a number of actions, including detection, classification, and traceback in order to mitigate the problem. In this research, we present a hybrid strategy for the identification and prevention of distributed denial of service (DDoS) attacks based on IP spoofing. Our suggested detection system makes use of both distance calculation and machine learning-based detection methods individually, but in an integrated manner, and it integrates the results of the two indicators to upgrade the system identification precision by combining their results. Our proposed hybrid system is tested against the CAIDA 2007 dataset in order to determine its detection performance. As a result, DDoS attack based on IP spoofing on the internal network is detected and spoofed IPs are found out and analyzed for the protection of server in order to forestall unapproved access and to maintain security of information handled by server. Also, SNORT based prevention technique is used so as to protect the server from attack. We conclude that the suggested hybrid system outperforms the non-hybrid detection systems in terms of detection performance and helps in preventing the attack by finding out the spoofed IP packet causing the attack thus protecting the system from attack. © 2022 IEEE.

关键词： Denial-of-service attack

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A Novel Federated Learning Model Aggregation Method Empowered by Coordination of computing and networking

A Novel Federated Learning Model Aggregation Method Empowere...

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Networks, Communications and Intelligent computing (NCIC), international conference on

作者： Shoufeng Wang Hua-Min Chen Jianchao Guo Ye Ouyang Fan Li Xuan Chen Yun Li Leiming Ma Jie Sun Shuling Wang AsiaInfo technologies (China) INC. Beijing China Beijing University of Technology. Beijing China China Unicom Beijing Branch. Beijing China China Unicom Beijing China

ISBN: (数字)9798350371949

ISBN: (纸本)9798350371956

Federated learning is a machine learning training paradigm that enables model training under data privacy constraints. Its distinguishing capability is the ability to train models without requiring participating parties to share private data. However, model training and aggregation in federated learning is communication and computation intensive. Relying solely on cloud computing is insufficient to balance the trade-offs in model aggregation and training given the distributed nature of federated learning. Coordination of computing and networking (CNC), an emerging infrastructure, incorporates massive cloud, edge / end computing into the communication network enabled by ubiquitous 6G connectivity. This forms an integrated infrastructure encompassing both networking and ubiquitous computing. CNC can improve performance for computing-intensive workloads, time-sensitive applications and distributed computing tasks via on-demand computing invocation. This paper studies how to exploit the ubiquitous distributed computing resources of computational networks to facilitate federated learning model training and aggregation. Challenges addressed include communication overhead from repeated model aggregations across participating nodes and the central node, computing overhead for local model training at participating nodes and global model aggregation at the central node, and improving computational network resource utilization efficiency throughout this process.

关键词： Training 6G mobile communication Industries Data privacy Cloud computing Federated learning Computational modeling

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Islanding Detection by Current Based Technique in Indian Distribution Grid

Islanding Detection by Current Based Technique in Indian Dis...

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international conference on computing and networking Technology (ICCNT)

作者： Prahlad Mundotiya Charul Jain Kusum Verma Department of Electrical Engineering MNIT Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

With rising electricity demand, distribution networks (DNs) are constantly subjected to increased stress, reliability, and power quality issues. The integration of distributed Generation (DG) into distribution networks in existing electricity grids presents substantial technological, environmental, and economic challenges. Because of the interconnection of DGs in the distribution system, the direction of power flow varies depending on the operating circumstances, resulting in various technical issues such as an increase in system fault level, a delay in fault clearance, etc. DGs are used in smart grid technologies, where the microgrid concept has been used for some specific areas of DNs. The microgrid has two modes of operation - one gridconnected mode and another islanded mode. This study presents islanding detection (in microgrid) in DNs with DGs using the dq transformation technique. The simulation is done in PSCAD, and results are obtained for various fault conditions. The simulation uses practical data from Gujarat DISCOM, MGVCL’s Anand Karmasad 11 kV distribution feeder, and their covered area.

关键词： Islanding Microgrids Distribution networks Voltage Power system stability Stability analysis distributed power generation Smart grids Reliability Stress

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The Metaverse: Challenges and Opportunities for AI to Shape the Virtual Future

The Metaverse: Challenges and Opportunities for AI to Shape ...

引用

ACIS international conference on Software Engineering, Artificial Intelligence, networking, and Parallel/distributed computing (SNPD)

作者： Vincenzo De Masi Qinke Di Siyi Li Yuhan Song School of Creative and Culture Beijing Normal University - Hong Kong Baptist University United International College Zhuhai China School of Computer and Information Engineering Guangdong Songshan Polytechnic Shaoguan China Media Communication Hong Kong Baptist University Hong Kong China Fine Arts CGA New York Institute of Technology New York US

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

The metaverse, a concept of an immersive and interconnected virtual world, is rapidly gaining traction as the next frontier in digital interaction. This article explores the critical role of artificial intelligence (AI) in shaping the future of the metaverse by addressing key challenges such as interoperability and synchronization. Drawing on recent data and case studies, including Roblox’s success and the rise of AI-powered platforms like Crucible and Improbable, the article examines how AI can enable seamless experiences across virtual worlds and optimize network infrastructure for real-time, shared experiences. The article also discusses the ethical and social implications of AI in the metaverse and emphasizes the need for collaborative efforts to ensure responsible innovation. As the boundaries between the physical and virtual worlds blur, the AI-enabled metaverse has the potential to revolutionize various aspects of human life, from education and entertainment to scientific exploration and problem-solving.

关键词： Technological innovation Ethics Metaverse Shape Real-time systems Synchronization Problem-solving

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A Study on Migration Scheduling in distributed Stream Processing Engines 22

A Study on Migration Scheduling in Distributed Stream Proces...

引用

23rd international conference on distributed computing and networking, ICDCN 2022

作者： Lindeberg, Morten Plagemann, Thomas University of Oslo Norway

ISBN: (纸本)9781450395601

The cost of migrating stateful operators in distributed stream processing has attracted research attention. Reactive migration in response to context changes is the common approach. Other migration scheduling strategies, like proactive migration based on prediction, and delayed migration are nearly neglected. This paper investigates four algorithms that explore these alternative scheduling strategies. The algorithms are implemented in a prototype stream processing overlay and run over an emulated network. Experiments with synthetic workload reveal that (1) proactive migration can reduce average event delivery latency, and (2) that it is important to handle noise in the data to avoid wrong adaptations. Experiments with real workload demonstrate that (1) pro-activity is not always beneficial, and (2) careful timing of migration depending on operator state, has a large potential to limit overhead. The experiments demonstrate a reduction in state size of 38 %, resulting in a 30 % reduction in freeze time. Consideration of operator state size is especially important. The state transfer can lead to contention that further harms event delivery and/or causes network timeouts for cases with limited network resources. © 2022 ACM.

关键词： Scheduling

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Message from the conference Chair

Message from the Conference Chair

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Summer Virtual conference on Software Engineering, Artificial Intelligence, networking and Parallel/distributed computing (SNPD-Summer), ACIS international

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Scalable and Consistent Graph Neural Networks for distributed Mesh-based Data-driven Modeling

Scalable and Consistent Graph Neural Networks for Distribute...

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High Performance computing, networking, Storage and Analysis, SC-W: Workshops of the international conference for

作者： Shivam Barwey Riccardo Balin Bethany Lusch Saumil Patel Ramesh Balakrishnan Pinaki Pal Romit Maulik Venkatram Vishwanath Argonne National Laboratory Transportation and Power Systems USA Argonne National Laboratory Leadership Computing Facility USA Computational Science Division Argonne National Laboratory USA Information Sciences and Technology Pennsylvania State University USA Argonne National Laboratory Mathematics and Computer Science USA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

This work develops a distributed graph neural network (GNN) methodology for mesh-based modeling applications using a consistent neural message passing layer. As the name implies, the focus is on enabling scalable operations that satisfy physical consistency via halo nodes at sub-graph boundaries. Here, consistency refers to the fact that a GNN trained and evaluated on one rank (one large graph) is arithmetically equivalent to evaluations on multiple ranks (a partitioned graph). This concept is demonstrated by interfacing GNNs with NekRS, a GPU-capable exascale CFD solver developed at Argonne National Laboratory. It is shown how the NekRS mesh partitioning can be linked to the distributed GNN training and inference routines, resulting in a scalable mesh-based data-driven modeling workflow. We study the impact of consistency on the scalability of mesh-based GNNs, demonstrating efficient scaling in consistent GNNs for up to O(1B) graph nodes on the Frontier exascale supercomputer.

关键词： Training Data-driven modeling Analytical models Costs Message passing Scalability Writing Graph neural networks Supercomputers Standards

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Artificial Intelligence for SDN Security: Analysis, Challenges and Approach Proposal 15

Artificial Intelligence for SDN Security: Analysis, Challeng...

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15th IEEE international conference on Security of Information and Networks, SIN 2022

作者： Sahbi, Amina Jaidi, Faouzi Bouhoula, Adel University of Carthage Higher School of Communications of Tunis Digital Security Research Lab TunisLR18TICO1 Tunisia University of Carthage National School of Engineers of Carthage Tunis Tunisia Arabian Gulf University College of Graduate Studies Department of Next-Generation Computing Bahrain

ISBN: (数字)9781665454650

ISBN: (纸本)9781665454650

The dynamic state of networks presents a challenge for the deployment of distributed applications and protocols. Ad-hoc schedules in the updating phase might lead to a lot of ambiguity and issues. By separating the control and data planes and centralizing control, Software Defined networking (SDN) offers novel opportunities and remedies for these issues. However, software-based centralized architecture for distributed environments introduces significant challenges. Security is a main and crucial issue in SDN. This paper presents a deep study of the state-of-the-art of security challenges and solutions for the SDN paradigm. The conducted study helped us to propose a dynamic approach to efficiently detect different security violations and incidents caused by network updates including forwarding loop, forwarding black hole, link congestion, network policy violation, etc. Our solution relies on an intelligent approach based on the use of Machine Learning and Artificial Intelligence Algorithms. © 2022 IEEE.

关键词： Software defined networking

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