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Multi-Class Task Offloading Optimization in Mobile Edge computing

Multi-Class Task Offloading Optimization in Mobile Edge Comp...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Songkang Ma Zhenli He Libo Feng Xiaolong Zhai Yiyan Tong 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 proliferation of mobile devices and the increasing demand for portable services have led to a surge in computationally intensive and time-sensitive applications, necessitating efficient computation offloading in Mobile Edge computing. Existing research often overlooks the distinct response time requirements of different applications and their mutual interference during queuing. This paper addresses these gaps by optimizing offloading strategies with multiple response time constraints, focusing on the queuing impact of various tasks. We model edge servers as M/M/1 queuing systems and develop a set of mathematical models. Using algorithms based on Karush-Kuhn-Tucker conditions, we derive optimal offloading strategies to minimize system power consumption. Our approach significantly reduces system power consumption in practical applications, enhancing resource utilization for service providers. Our work introduces a comprehensive and realistic model of task offloading, considering multiple task types, their distinct average response time requirements, and their mutual interference during queuing. This advancement ensures efficient and effective offloading strategies, addressing the complexity of multiple tasks and varying response time constraints.

关键词： Power demand Multi-access edge computing Computational modeling Interference Mathematical models Computational efficiency Time factors Servers Resource management Surges

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A Lightweight Privacy-Preserving Asynchronous Federated Learning Scheme in Internet of Vehicles

A Lightweight Privacy-Preserving Asynchronous Federated Lear...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Xin Liu Xiaodong Shen Chang Xu Liehuang Zhu Kashif Sharif School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The Internet of Vehicles (IoV) facilitates wireless communication and information exchange among vehicles, road infrastructure, and pedestrians, creating a comprehensive network for intelligent vehicle control. Within the IoV framework, numerous computing tasks typically transmitted to the cloud for centralized processing lead to significant latency and substantial cloud computing burdens. To optimize IoV efficiency while safeguarding vehicle data privacy, federated learning (FL) has shown promising potential. However, due to vehicle mobility, FL encounters challenges such as communication bandwidth limitations, varying road conditions, and data transmission delays. Additionally, traditional FL methods do not fully ensure the non-disclosure of user data during aggregation. To address these issues, we propose a lightweight encryption-based asynchronous federated learning scheme (LPAsyFL) for privacy protection in IoV. This scheme supports user participation and withdrawal, ensuring security in honest but curious environments. By utilizing lightweight cryptographic primitives and asynchronous aggregation techniques, we introduce a dynamic aggregation mechanism that reduces communication overhead and enhances model aggregation efficiency. Simulation results on various datasets demonstrate that our approach reduces communication costs and improves aggregation efficiency in the vehicular network.

关键词： Training Wireless communication Privacy Cloud computing Federated learning Roads Simulation Cryptography Vehicle dynamics Internet of Vehicles

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QoS-aware Dynamic Service Caching and Updating in Cost-efficient Multi-Access Edge computing

QoS-aware Dynamic Service Caching and Updating in Cost-effic...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Shuaibing Lu Xin Jin Jie Wu Shuyang Zhou Shen Wu Jackson Yang Ran Yan College of Computer Science Beijing University of Technology Beijing China Center for Networked Computing Temple University USA School of Software Engineering Beijing Jiaotong University Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In the context of mobile edge computing, achieving dynamic service caching and updating to guarantee the QoS of users and reduce system costs is a challenging problem. However, existing research still has certain deficiencies in considering the dynamic behavior of users and the limited storage resources of edge servers. To address this problem, this paper proposes three novel strategies for the different stages of service caching and updating to jointly optimize the delay and cost. At the initial service caching stage, we propose a caching strategy based on dynamic programming, taking into account the constraint of limited memory resources. Given the dynamic behavior of users, we formulate the joint optimization problem as a Markov Decision Process (MDP) and design a service extension strategy based on Q-learning at the service updating decision-making stage and a replacement strategy taking both the distribution of service replications and service access frequency into account at the service updating replacement stage to guarantee the QoS of users. We effectively tackle the challenges arising from the dynamic behavior of users and limited storage resources. Through extensive comparative experiments, our approach outperforms traditional strategies by significantly reducing user latency and system cost.

关键词： Costs Multi-access edge computing Q-learning Heuristic algorithms Memory management Quality of service Dynamic scheduling Dynamic programming Servers Optimization

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Towards xBGAS on CHERI: Supporting a Secure Global Memory

Towards xBGAS on CHERI: Supporting a Secure Global Memory

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

作者： Mert Side Brody Williams John Leidel Jonathan Woodruff Simon W. Moore Yong Chen Texas Tech University Lubbock TX USA Tactical Computing Labs Muenster TX USA University of Cambridge Cambridge UK

Modern computing systems embrace heterogeneous system architectures to meet high-performance computing requirements. As a result, the Extended Base Global Address Space (xBGAS) project introduced an extension to the RISCV instruction set architecture (ISA) to provide extended addressing capabilities. Although the xBGAS project is currently implemented on the RISC-V ISA, the project’s central vision is to include a broad domain of ideas that is not dependent on any single microarchitecture. Therefore, demonstrating the existing xBGAS benchmarks on the ARM ISA would enable working on a real system prototype. The ARM Morello platform is intended as an industrial demonstrator of a capability architecture using Capability Hardware Enhanced RISC Instructions (CHERI). Consequently, understanding the CHERI semantics and identifying the desired features for xBGAS are crucial for making xBGAS portable across platforms. In this work, we investigate creating a software-based xBGAS solution for the Morello platform by porting the xBGAS runtime onto the ARM ISA. First, we revisit the basics of the xBGAS project and the CHERI project to learn what the security benefits of CHERI on xBGAS are by taking advantage of CHERI capabilities. Then, we explore the implementation of the CHERI capabilities and Morello simulators to discuss an adaptation of the scalable xBGAS runtime library to support other ISAs such as ARM.

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Exploring MPI Collective I/O and File-per-process I/O for Checkpointing a Logical Inference Task

Exploring MPI Collective I/O and File-per-process I/O for Ch...

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

作者： Fan, Ke Micinski, Kristopher Gilray, Thomas Kumar, Sidharth Univ Alabama Birmingham Birmingham AL 35294 USA Syracuse Univ Syracuse NY 13244 USA

ISBN: (纸本)9781665435772

We present a scalable parallel I/O system for a logical-inferencing application built atop a deductive database. Deductive databases can make logical deductions (i.e. conclude additional facts), based on a set of program rules, derived from facts already in the database. Datalog is a language or family of languages commonly used to specify rules and queries for a deductive database. Applications built using Datalog can range from graph mining (such as computing transitive closure or k-cliques) to program analysis (control and data-flow analysis). In our previous papers, we presented the first implementation of a data-parallel Datalog built using MPI. In this paper, we present a parallel I/O system used to checkpoint and restart applications built on top of our Datalog system. State of the art Datalog implementations, such as Souffle, only support serial I/O, mainly because the implementation itself does not support many-node parallel execution. computing the transitive closure of a graph is one of the simplest logical-inferencing applications built using Datalog;we use it as a micro-benchmark to demonstrate the efficacy of our parallel I/O system. Internally, we use a nested B-tree data-structure to facilitate fast and efficient in-memory access to relational data. Our I/O system therefore involves two steps, converting the application data-layout (a nested B-tree) to a stream of bytes followed by the actual parallel I/O. We explore two popular I/O techniques POSIX I/O and MPI collective I/O. For extracting performance out of MPI Collective I/O we use adaptive striping, and for POSIX I/O we use file-per-process I/O. We demonstrate the scalability of our system at up to 4,096 processes on the Theta supercomputer at the Argonne National Laboratory.

关键词： Checkpointing distributed processing Scalability Conferences Supercomputers Deductive databases Task analysis

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PRACTICE AND EXPERIENCE IN USING parallel AND SCALABLE MACHINE LEARNING IN REMOTE SENSING FROM HPC OVER CLOUD TO QUANTUM computing

PRACTICE AND EXPERIENCE IN USING PARALLEL AND SCALABLE MACHI...

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IEEE international Geoscience and Remote Sensing symposium (IGARSS)

作者： Rieder, Morris Cavallaro, Gabriele Benediktsson, Jon Atli Univ Iceland Sch Engn & Nat Sci Reykjavik Iceland Forschungszentrum Julich Julich Supercomp Ctr Julich Germany

ISBN: (纸本)9781665403696

Using computationally efficient techniques for transforming the massive amount of Remote Sensing (RS) data into scientific understanding is critical for Earth science. The utilization of efficient techniques through innovative computing systems in RS applications has become more widespread in recent years. The continuously increased use of Deep Learning (DL) as a specific type of Machine Learning (ML) for data-intensive problems (i.e., 'big data') requires powerful computing resources with equally increasing performance. This paper reviews recent advances in High-Performance computing (HPC), Cloud computing (CC), and Quantum computing (QC) applied to RS problems. It thus represents a snapshot of the state-of-the-art in ML in the context of the most recent developments in those computing areas, including our lessons learned over the last years. Our paper also includes some recent challenges and good experiences by using Europeans fastest supercomputer for hyper-spectral and multi-spectral image analysis with state-of-the-art data analysis tools. It offers a thoughtful perspective of the potential and emerging challenges of applying innovative computing paradigms to RS problems.

关键词： High performance computing cloud computing quantum computing machine learning deep learning parallel and distributed algorithms remote sensing

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Load balancing for distributed nonlocal models within asynchronous many-task systems

Load balancing for distributed nonlocal models within asynch...

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

作者： Gadikar, Pranav Diehl, Patrick Jha, Prashant K. Indian Inst Technol Madras Comp Sci Dept Chennai Tamil Nadu India Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA Univ Texas Austin Oden Inst Computat Engn & Sci Austin TX 78712 USA

ISBN: (纸本)9781665435772

In this work, we consider the challenges of developing a distributed solver for models based on nonlocal interactions. In nonlocal models, in contrast to the local model, such as the wave and heat partial differential equations, the material interacts with neighboring points on a larger-length scale compared to the mesh discretization. In developing a fully distributed solver, the interaction over a length scale greater than mesh size introduces additional data dependencies among the compute nodes and communication bottleneck. In this work, we carefully look at these challenges in the context of nonlocal models;to keep the presentation specific to the computational issues, we consider a nonlocal heat equation in a 2d setting. In particular, the distributed framework we propose pays greater attention to the bottleneck of data communication and the dynamic balancing of loads among nodes with varying compute capacity. For load balancing, we propose a novel framework that assesses the compute capacity of nodes and dynamically balances the load so that the idle time among nodes is minimal. Our framework relies heavily on HPX library, an asynchronous many-task run time system. We present several results demonstrating the effectiveness of the proposed framework.

关键词： Nonlocal Computational Models Load Balancing AMT HPX parallel and distributed computing

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FedClust: Optimizing Federated Learning on Non-IID Data Through Weight-Driven Client Clustering

FedClust: Optimizing Federated Learning on Non-IID Data Thro...

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

作者： Md Sirajul Islam Simin Javaherian Fei Xu Xu Yuan Li Chen Nian-Feng Tzeng School of Computing and Informatics University of Louisiana Lafayette USA School of Computer Science and Technology East China Normal University China Department of Computer and Information Sciences University of Delaware USA

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Federated learning (FL) is an emerging distributed machine learning paradigm enabling collaborative model training on decentralized devices without exposing their local data. A key challenge in FL is the uneven data distribution across client devices, violating the well-known assumption of independent-and-identically-distributed (IID) training samples in conventional machine learning. Clustered federated learning (CFL) addresses this challenge by grouping clients based on the similarity of their data distributions. However, existing CFL approaches require a large number of communication rounds for stable cluster formation and rely on a predefined number of clusters, thus limiting their flexibility and adaptability. This paper proposes FedClust, a novel CFL approach leveraging correlations between local model weights and client data distributions. FedClust groups clients into clusters in a one-shot manner using strategically selected partial model weights and dynamically accommodates newcomers in real-time. Experimental results demonstrate FedClust outperforms baseline approaches in terms of accuracy and communication costs.

关键词： Training Adaptation models distributed processing Limiting Costs Correlation Federated learning

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Invited paper: An Artificial Matrix Generator for Multi-platform SpMV Performance Analysis

Invited paper: An Artificial Matrix Generator for Multi-plat...

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

作者： Dimitrios Galanopoulos Panagiotis Mpakos Petros Anastasiadis Nectarios Koziris Georgios Goumas Computing Systems Laboratory National Technical University of Athens Greece

Sparse Matrix-Vector (SpMV) multiplication is a frequently encountered computational kernel, that is notorious for achieving only a small fraction of system peak performance. The optimization of this kernel and the identification of bottlenecks that limit its performance have been the subject of considerable research efforts. Nevertheless, obtaining an unbiased matrix dataset that can expose these bottlenecks remains a recurring challenge. To this end, we propose a feature-based artificial matrix generation method, that associates the most common SpMV bottlenecks with core matrix features, and develop a generator program that can quickly produce a wide variety of matrices on the fly for performance analysis. To evaluate our generator, we compare the performance of artificial and real matrices, across three different platforms: a CPU, a GPU and an FPGA.

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Brief Announcement: On the distributed Construction of Stable Networks in Polylogarithmic parallel Time 1

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6th international symposium on Algorithmic Aspects of Cloud computing (ALGOCLOUD)

作者： Connor, Matthew Univ Liverpool Dept Comp Sci Liverpool Merseyside England

ISBN: (数字)9783030930431

ISBN: (纸本)9783030930431;9783030930424

We study the class of networks which can be created in polylogarithmic parallel time by network constructors [O. Michail, P. Spirakis, distributed computing 29(3), 207-237 (2016)]. We prove that the class of trees where each node has any k >= 2 children can be constructed in O(log n) parallel time with high probability. We show that constructing networks which are k-regular is Omega(n) time, but a minimal relaxation to (l, k)-regular networks, where l = k - 1 can be constructed in polylogarithmic parallel time for any fixed k, where k > 2. We further demonstrate that when the finite-state assumption is relaxed and k is allowed to grow with n, then k = log log n acts as a threshold above which network construction is again polynomial time. We use this to provide a partial characterisation of the class of polylogarithmic time network constructors.

关键词： Population protocol distributed network construction Polylogarithmic time protocol Spanning tree Regular network

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