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Task Scheduling and Computation Offloading in Space Edge computing

Task Scheduling and Computation Offloading in Space Edge Com...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Zihong Lin Haopeng Chen Yucheng Tao Chang Liu Shengyang Liu Fei Han School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Shanghai Institute of Spaceflight Control Technology China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the launch of numerous low Earth orbit (LEO) satellites and the construction of constellation networks, the traditional bent-pipe communication method between LEO satellites and the ground has become inefficient, failing to fully utilize the satellites’ capabilities. To address this, we designed a model where satellites collaboratively execute complex missions at the space edge, reducing communication demands with the ground and improving task execution efficiency. Given the constraints of limited satellite resources, energy, and dynamic inter-satellite topology, we developed task scheduling and computation offloading algorithms using genetic algorithms to enhance task completion rates and resource utilization. Experimental results show that the proposed algorithms achieve good load balancing (approximately 10% of the greedy algorithm) and shorten scheduling length while minimizing timeouts.

关键词： Space vehicles Satellites Processor scheduling Heuristic algorithms Computational modeling Space missions Low earth orbit satellites Approximation algorithms Dynamic scheduling Topology

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From parallelization to Customization - Challenges and Opportunities

From Parallelization to Customization - Challenges and Oppor...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Cong, Jason Univ Calif Los Angeles Comp Sci Dept Ctr Domain Specif Comp CDSC Los Angeles CA 90024 USA

ISBN: (纸本)9781665440660

With large-scale deployment of FPGAs in both private and public clouds in the past a few years, customizable computing is transitioning from advanced research into mainstream computing. In this talk, I shall first showcase a few big data and machine learning applications that benefit significantly from customization. Next, I shall discuss the challenges of FPGA programming for the efficient accelerator designs, which presents a significant barrier to many software programmers, despite the recent advances in high-level synthesis. Then, I shall highlight our recent progress on automated compilation for customized archictectures, such as systolic arrays, stencils, and more general CPPs (composable parallel and pipelined) architectures. I shall also present our ongoing work on HeteroCL, a highly productive multi-paradigm programming framework targeting accelerator-rich heterogeneous architectures, and is being used as a focal point to integrate various optimizaiton techniques and support high-level domain-specific languages (DSL) such as Halide and Pytorch. Our goal is to “demacratize customizable computing” so that most (if not all) software programmers can design optimized accelerators on FPGAs.

关键词： distributed processing Cloud computing Computer architecture Machine learning Programming Big Data Software

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The First international Workshop on computing using EmeRging EXotic AI-Inspired Systems (CORtEX'22)

The First International Workshop on COmputing using EmeRging...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

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Data-Driven Vehicle Positioning with Smartphone Raw GNSS Data in Urban Areas

Data-Driven Vehicle Positioning with Smartphone Raw GNSS Dat...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： MengLing Ou Zenghua Zhao Jianyu Yang Jiankun Wang Jiafan Lu College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Vehicle positioning using raw global navigation satellite system (GNSS) observations from smartphones is crucial for intelligent transportation and smart driving. However, it is challenging to achieve high accuracy due to GNSS signal blockage and reflection in urban areas. In this work, we propose a data-driven smartphone positioning system PhoeNix-RTK empowered by deep transformer model. PhoeNix-RTK corrects positions estimated by the real-time kinematic (RTK) approach by learning from GNSS features with PhoeNix model. To make latent features independent of satellites, PhoeNix encodes the GNSS features based on set transformer. To exploit the temporal dependence in the GNSS features, PhoeNix predicts correction vectors based on transformer in a sequence-to-sequence manner. Extensive experimental results on a public vehicle dataset demonstrate PhoeNix-RTK achieves high accuracy in urban areas, outperforming its counterparts.

关键词： Global navigation satellite system Accuracy Satellites Urban areas Transportation Transformers Vectors Reflection Real-time systems Smart phones

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Heterogeneous Data Processing in All-Digital Phased Array Radar

Heterogeneous Data Processing in All-Digital Phased Array Ra...

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IEEE international symposium on Phased Array Systems and Technology

作者： Skyler Garner Mark Yeary Advanced Radar Research Center University of Oklahoma Norman OK United States

ISBN: (数字)9798350392142

ISBN: (纸本)9798350392159

All-digital phased array radars are an emerging technology that require new data processing techniques which are scalable, rely on open-architecture software components, and do not create bottlenecks for the real-time operation. Achieving a solution for these issues relies on heterogeneous processing and distributed software development on CPUs and FPGAs, whereby the overarching algorithm processing chain is distributed in a way that begins at the ADC of each element on an array and concludes in actionable data products. An important aspect is not only the speed of the algorithms, but the throughput of the packetized data transfer. These packets are not only routed, but they are also acted upon in a network of computing services. Here, we explore an open-source resource manager, known as Kubernetes, which connects the packetized data with computing resources on the servers, i.e., moving data in and out of server RAM to complete algorithms such as creating range-Doppler maps, as our results section shows.

关键词： Phased arrays Array signal processing Software algorithms Signal processing algorithms Radar parallel processing Throughput Arrays Servers Software development management

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Dynamic VNF Deployment and Resource Allocation in Mobile Edge computing

Dynamic VNF Deployment and Resource Allocation in Mobile Edg...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Xiaolong Zhai Zhenli He Yuanfei Xiao Junjie Wu Xuejie Yu School of Software Yunnan University Kunming China Yunnan Key Laboratory of Software Engineering Yunnan University Kunming China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The explosive growth of terminal devices at the network edge, coupled with advancements in communication technology, poses significant challenges to traditional cloud computing models. Despite Mobile Edge computing (MEC) mitigating some issues by enabling real-time data processing closer to the source, it faces challenges with linear growth in computational resources insufficient to meet the exponential growth in service demand. Existing research utilizing Software-Defined Networking (SDN), Network Functions Virtualization (NFV), and Service Function Chain (SFC) technologies has made progress but still faces critical issues, such as the inability to respond in real-time to dynamic demands and inefficiencies in resource management strategies. This paper addresses these challenges by optimizing Virtual Network Function (VNF) deployment strategies in MEC environments. We propose PPO-ERA, a novel algorithm leveraging deep reinforcement learning and the Karush-Kuhn-Tucker (KKT) method. This approach provides real-time, adaptive, and dynamic deployment policies for VNFs, significantly improving both the average response delay of tasks and resource utilization. Key contributions include rigorous SFC-based application modeling, dynamic VNF deployment algorithms, elastic resource allocation, uniform state representation, and extensive performance validation. These advancements enhance the adaptability, efficiency, and performance of VNF deployment strategies, addressing critical challenges in dynamic MEC environments.

关键词： Multi-access edge computing Service function chaining Heuristic algorithms Computational modeling Dynamic scheduling Real-time systems Explosives Network function virtualization Resource management Faces

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NFAERCOM: A Near-Far Area Experience Replay-based Computation Offloading Method

NFAERCOM: A Near-Far Area Experience Replay-based Computatio...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Wenxuan Wang Yi Huang Yunhe Cui Chun Guo Yi Chen Guowei Shen Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education College of Computer Science and Technology Guizhou University Guiyang China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Computation offloading technology plays an important role in Mobile Edge computing (MEC). Most mainstream Deep Reinforcement Learning (DRL)-based computation offloading methods employ random experience replay to train networks. This training method does not take into account the value differences between experiences, resulting in the decrease of training speeds and the increase of task completion delay, energy consumption, and task drop rate. Prioritized Experience Replay (PER) alleviates this issue to some extent. However, using Temporal Difference (TD) error as the criterion for the importance of experiences does not allow for the selection of experiences that are more "concerned" by the Actor network under the Actor-Critic framework. This limitation restricts the performance of the algorithm. To address these issues, this paper focuses on the computation offloading problem in scenarios with multiple mobile devices (MDs) and multiple MEC servers. A near-far area experience replay algorithm-based computation offloading method named NFAERCOM is proposed. NFAERCOM additionally considers the queuing delay at the MEC server and introduces a new near-far area experience replay algorithm. Evaluation results demonstrate that NFAERCOM effectively reduces the task completion delay, energy consumption, and task drop rate of tasks.

关键词： Training Performance evaluation Energy consumption Runtime Multi-access edge computing Processor scheduling Mobile handsets Delays Servers Computational complexity

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Efficient Federated Learning Mechanism Based on Layer-wise Model Pruning

Efficient Federated Learning Mechanism Based on Layer-wise M...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Zhijie Jiang Zhe Zhang Yanchao Zhao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

As a computing paradigm tailored for resource-constrained client devices, federated learning based on model pruning compresses the model size by removing unimportant parameters in the neural network, which has shown outstanding results in improving model efficiency and reducing computing costs. However, previous works simply customized a unified static model pruning rate, ignoring the heterogeneous capabilities of clients and the impact of pruning on different layers of the model during continuous iteration. In this paper, we design a novel Federated learning framework based on Dynamic Layer-wise Pruning, named FedDLP, which is capable of pruning at the hierarchical level depending on the client’s capability and model similarity to improve model efficiency and maintain model performance. This framework consists of two parts. First, pre-training customizes the initial pruning rate: We set the initial pruning rate for each layer according to the different capabilities of heterogeneous clients during the pre-training stage. Second, adaptively optimize the pruning rate: We use cosine similarity to quantify the contribution of each layer of the client model to the global model, thereby adaptively and dynamically optimizing the model pruning rate. Experimental results verify that our proposed method improves model efficiency by 2 to 3.5× compared to the state-of-the-art baselines, while achieving an accuracy difference of no more than 2% compared to the unpruned model.

关键词： Degradation Adaptation models distributed processing Accuracy Costs Federated learning Computational modeling Neural networks Tuning

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Measuring the Impact of Gradient Accumulation on Cloud-based distributed Training

Measuring the Impact of Gradient Accumulation on Cloud-based...

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IEEE/ACM international symposium on Cluster computing and the Grid (CCGRID)

作者： Zimeng Huang Bo Jiang Tian Guo Yunzhuo Liu Shanghai Jiao Tong University China Worcester Polytechnic Institute U.S

Gradient accumulation (GA) is a commonly adopted technique for addressing the GPU memory shortage problem in model training. It reduces memory consumption at the cost of increased computation time. Although widely used, its benefits to model training have not been systematically studied. Our work evaluates and summarizes the benefits of GA, especially in cloud-based distributed training scenarios, where training cost is determined by both execution time and resource consumption. We focus on how GA can be utilized to balance execution time and resource consumption to achieve the lowest bills. Through empirical evaluations on AliCloud platforms, we observe that the total training cost can be reduced by 31.2% on average with a 17.3% increase in training time, when GA is introduced in the large-model and small-bandwidth scenarios with data-parallel training strategies. Besides, taking micro-batch size into optimization can further decrease training time and cost by 21.2% and 24.8% on average, respectively, for hybrid-parallel strategies in large-model and GPU training scenarios.

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distributed Checkpointing in Dataflow with Static Scheduling

Distributed Checkpointing in Dataflow with Static Scheduling

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international symposium on Computer Architecture and High Performance computing Workshops (SBAC-PADW)

作者： Tiago A. O. Alves State University of Rio de Janeiro

The Dataflow model, where instructions or tasks are fired as soon as their input data is ready, was proven to be a good fit for parallel/distributed computation. Previous works have presented DFER (Dataflow Error Recovery Model), that allows transient error and recovery in dataflow by adding special tasks and edges to the dataflow graph itself. However, permanent faults or faults that cause a processing element (PE) to become irresponsive are not addressed by DFER. For those cases it is necessary to adopt a checkpointing method. Since the whole purpose of Dataflow is to achieve high levels of parallelism and explore the potential asynchronicity between PEs, it is clear that the checkpointing method adopted must be uncoordinated and distributed. Current algorithms for distributed checkpointing rely solely on guaranteeing that causality between checkpoints can be trackable. In the context of Dataflow with static scheduling, i.e. when the dataflow graph is partitioned among the available PEs at compile-time, causality trackability is not sufficient as we will show. Since static scheduling of dataflow graphs is very important in various scenarios, it calls for a new algorithm for distributed checkpointing that can be adopted for the execution of statically scheduled dataflow graphs. In this paper we describe why the ability to track causality is not enough for statically scheduled dataflow and introduce a new algorithm for distributed checkpointing specifically tailored for such model of execution.

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