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25th International Conference on Parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： HoseinyFarahabady, MohammadReza Zomaya, Albert Y. School of Computer Science Center for Distributed and High Performance Computing The University of Sydney SydneyNSW Australia

ISBN: (纸本)9789819642069

Graphics Processing Units (GPUs) are widely used as powerful hardware accelerators for data-intensive tasks. However, their efficacy can be hindered by constraints in device memory and data transfer speeds via the PCI-E link. This paper explores the use of GPUs for executing scalable sorting operations that exceeds the GPU’s global memory by a factor of at least two orders of magnitude. To address this challenge, we leverage the power of NVIDIA’s asynchronous CUDA streams. Our solution enhances GPU performance by implementing a pipeline model to offload sorting operations from the Solid-State Drive (SSD) to GPU memory via the host’s main memory. This strategy maximizes the multiway concurrency level to reduce the overhead associated with data transfer within the GPU memory hierarchy. We perform extensive performance evaluations by sorting 128 billion data elements initially stored on an external NVMe SSD. The results confirm the effectiveness of our approach, showcasing a speed-up of 4.3× compared to the results for CPU-based out-of-memory sorting of skewed data based on Intel’s Threading Building Blocks (TBB) sorting algorithm. Our results include the cost of transfer times to and from the main memory, a factor often overlooked in similar studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graphics processing unit

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I/O Latency Management in Private Cloud Infrastructures 25th

I/O Latency Management in Private Cloud Infrastructures

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25th International Conference on Parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： HoseinyFarahabady, MohammadReza Zomaya, Albert Y. School of Computer Science Center for Distributed and High Performance Computing The University of Sydney SydneyNSW Australia

ISBN: (纸本)9789819642069

This paper examines how co-locating multiple VMs on a single physical server with shared storage impacts I/O performance, specifically focusing on latency of I/O operations, and the overall throughput. We introduce a Proportional-Integral-Derivative (PID) controller to maintain I/O latency close to a predefined target by dynamically adjusting I/O requests based on real-time measurements. Through a series of experiments, we evaluated the effectiveness of our PID controller in comparison to VMware’s default QoS mechanism within a private cloud environment. The results indicate that the PID controller significantly enhances performance for latency-critical workloads, achieving substantially higher compliance with latency targets compared to the default mechanism. However, this prioritization of high-priority VMs comes at the expense of increased latency and reduced throughput for lower-priority workloads during periods of I/O contention. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Three term control systems

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Containerized Data-Flow Processing for Scalable Real-Time Analytics on Edge Devices 25th

Containerized Data-Flow Processing for Scalable Real-Time A...

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25th International Conference on Parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： HoseinyFarahabady, MohammadReza Y. Zomaya, Albert School of Computer Science Center for Distributed and High Performance Computing The University of Sydney SydneyNSW2006 Australia

ISBN: (纸本)9789819642069

The increasing demand for real-time data analysis in Internet of Things (IoT) ecosystems has created several challenges, particularly in environments where resources are limited, and minimizing data processing latency is critical. This paper investigates the use of Linux Containerization (LXC) as a lightweight, resource-efficient alternative to traditional virtual machine managers for accelerating data-flow processing applications. We propose an architecture that integrates Linux Containerization (LXC) with a Proportional-Integral-Derivative (PID) controller to dynamically manage I/O resource allocation, ensuring Quality of Service (QoS) for I/O-intensive data analytics workloads. The proposed PID controller continuously monitors system performance and dynamically adjusts I/O resource allocation to mitigate performance degradation. Through a series of experiments, we demonstrate that LXC-based solutions outperform traditional methods in managing I/O-intensive data analytics workloads in IoT and edge ecosystems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quality of service

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Geo-distributed Analytical Streaming Architecture for IoT Platforms

Geo-Distributed Analytical Streaming Architecture for IoT Pl...

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2024 IEEE International Conference on Cluster computing, CLUSTER 2024

作者： Hoseiny Farahabady, M. Reza Zomaya, Albert Y. The University of Sydney School of Computer Science Center for Distributed and High Performance Computing SydneyNSW2006 Australia

ISBN: (纸本)9798350358711

The surge in real-time IoT data introduces scalability and computational challenges, necessitating advanced architectural and technological solutions. Streamed data processing is increasingly adopted across industries to enhance operational efficiency by extracting insights from vast, unstructured datasets. However, complex analytical tasks, such as multi-join queries, often require stateful iterative calculations on high-volume, high-velocity data, which challenges conventional programming models like MapReduce. This paper introduces an architectural model enabling application developers to create intricate streaming computational logic within an IoT platform. Our architecture supports scalable applications across distributed edge-tier nodes, particularly for iterative analytical operations on streamed data. We discuss core concepts and a timestamp model (borrowed from the timely data-flow concept) attached to data items circulating between computational blocks, which can execute concurrently on different edge-tier nodes. Additionally, we detail a buffer management mechanism that dynamically adjusts memory size in each computational block on nodes with limited capacity. This mechanism considers application performance requirements and runtime conditions to optimize buffer sizes. performance evaluation against cloud-tier alternatives confirms the effectiveness of our solution. Experimental results show a significant reduction in p-99 delay compared to cloud-tier deployment with a database engine for analytical applications involving multi-join operations. © 2024 IEEE.

关键词： MapReduce

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A rank-based multiple-choice secretary algorithm for minimising microgrid operating cost under uncertainties

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Frontiers in Energy 2023年第2期17卷 198-210页

作者： Chunqiu Xia Wei Li Xiaomin Chang Ting Yang Albert Y.Zomaya Centre for Distributed and High Performance Computing School of Information TechnologiesThe University of SydneyCamperdownNSW 2006Australia School of Electrical Automation and Information Engineering Tianjin UniversityTianjin 300072China

The increasing use of distributed energy resources changes the way to manage the electricity *** the traditional centralized powered utility,many homes and businesses with local electricity generators have established their own microgrids,which increases the use of renewable energy while introducing a new challenge to the management of the microgrid system from the mismatch and unknown of renewable energy generations,load demands,and dynamic electricity *** address this challenge,a rank-based multiple-choice secretary algorithm(RMSA)was proposed for microgrid management,to reduce the microgrid operating *** than relying on the complete information of future dynamic variables or accurate predictive approaches,a lightweight solution was used to make real-time decisions under *** RMSA enables a microgrid to reduce the operating cost by determining the best electricity purchase timing for each task under dynamic *** experiments were conducted on real-world data sets to prove the efficacy of our solution in complex and divergent real-world scenarios.

关键词： energy management systems demand response scheduling under uncertainty renewable energy sources multiple-choice secretary algorithm

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ZeTFRi - A Zero Trust-Based Free Rider Detection Framework for Next Generation Federated Learning Networks

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1938-1953页

作者： Edirimannage, Shehan Khalil, Ibrahim Elvitigala, Charitha Daluwatta, Wathsara Wijesekera, Primal Zomaya, Albert Y. RMIT University School of Computing Technologies MelbourneVIC3000 Australia International Computer Science Institute University of California BerkeleyCA94704 United States University of Sydney Centre for Distributed and High-Performance Computing SydneyNSW2006 Australia

With the rapid expansion of next-generation networking, Internet of Things (IoT) devices have become central components of federated learning (FL) networks. FL offers a paradigm for distributed training machine learning models while preserving user data privacy. However, existing network security measures often struggle to identify legitimate contributors from opportunistic free riders within these networks. The Free Rider (FR) problem arises when participants seek to benefit from the FL processes without contributing. In particular, free riders are known to exist within or outside of the network, whereas outside free riders can hardly be identified. The Zero Trust model proposes an environment where no entity, including the network itself, is inherently trusted, providing a foundation to counter external threats seeking to exploit the network. This study proposes a novel framework strengthened by the Zero Trust model to identify external free riders in FL networks. Leveraging a Deep Autoencoding Gaussian Mixture Model (DAGMM)-based technique for internal free rider detection, our framework demonstrates superior performance in identifying free riders across various FR scenarios compared to current state-of-the-art solutions. Through our proposed framework and the principles of Zero Trust, we establish a robust security guarantee for FL networks, ensuring the integrity of the learning process. © 1983-2012 IEEE.

关键词： Federated learning

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Leveraging the Compute Power of Two HPC Systems for higher-Dimensional Grid-Based Simulations with the Widely-distributed Sparse Grid Combination Technique 23

Leveraging the Compute Power of Two HPC Systems for Higher-D...

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2023 International Conference for high performance computing, Networking, Storage and Analysis, SC 2023

作者： Pollinger, Theresa Van Craen, Alexander Niethammer, Christoph Breyer, Marcel Pflüger, Dirk Institute for Parallel and Distributed Systems Universität Stuttgart Stuttgart Germany High Performance Computing Center Universität Stuttgart Stuttgart Germany

ISBN: (纸本)9798400701092

Grid-based simulations of hot fusion plasmas are often severely limited by computational and memory resources;the grids live in four- to six-dimensional space and thus suffer the curse of dimensionality. However, high resolutions are required to fully capture the physics of interest. The sparse grid combination technique is a multi-scale method in which many anisotropically coarse resolved grids are used to approximate a fine-scale solution - -and it alleviates the curse of *** paper presents the core concepts of the widely-distributed combination technique, which allows us to use the compute power and memory of more than one HPC system for the same simulation. We apply the sparse grid combination technique to a six-dimensional advection problem serving as a proxy for plasma simulations. The full-grid solution approximated by the combination technique would contain ≈ 5 ZB if computed with conventional grid-based methods. Even the combination technique simulation operates on ≈ 1 × 1011 double-precision degrees of freedom, or 988 GB, plus the supporting sparse grid data structures. We propose a new approach to divide the compute load between the two HPC systems, requiring only 76 GB to be exchanged. Based on this, we have realized the first synchronous combination technique simulation using two HPC systems, in our case the two German Tier-0 supercomputers HAWK and SuperMUC-NG. On two systems, the simulation can be computed at an average overhead of ≈ 35 % (108 s per combination step) for file I/O and transfer. The presented concepts apply to any pair of HPC systems if high-speed data transfer is possible. © 2023 ACM.

关键词： Supercomputers

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Domain Generalization for Time-Series Forecasting via Extended Domain-Invariant Representations

Domain Generalization for Time-Series Forecasting via Extend...

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Artificial Intelligence of Things (AIoT), IEEE Annual Congress on

作者： Yunchuan Shi Wei Li Albert Y. Zomaya Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia

ISBN: (数字)9798350392296

ISBN: (纸本)9798350392302

Time-series forecasting is crucial for IoT applications, but generalizing across domains is challenging due to distinct data distributions and dynamics. Most domain generalization methods work well for image processing and classification, but they struggle with time-series forecasting. This is because they solely learn domain-invariant representations of input data, ignoring variations in the output space across domains. This oversight can lead to inaccurate forecasts when outputs in new domains exhibit different distributions or temporal patterns. In this work, we present a new approach to improve the time-series forecasting model generalization by extracting domain-invariant representations from both input and output data. Experiments demonstrate the effectiveness of our approach, achieving significant improvements in forecasting accuracy across multiple test domains. Compared to state-of-the-art methods, our approach delivers up to an 8% increase in accuracy.

关键词： Bridges Accuracy Image processing Time series analysis Predictive models Data models Internet of Things

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Quick-MIMIC:A Multimodal Data Extraction Pipeline for MIMIC with Parallelization

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Big Data Mining and Analytics 2024年第4期7卷 1333-1346页

作者： Yutao Dou Wei Li Yangtao Zheng Xiaojun Yao Huanxiang Liu Albert Y.Zomaya Shaoliang Peng College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082Chinaand also with Centre for Distributed and High Performance ComputingSchool of Computer ScienceThe University of SydneyDarlingtonNSW 2008Australia College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China Centre for Distributed and High Performance Computing School of Computer ScienceThe University of SydneyDarlingtonNSW 2008Australia Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China

Medical big data with artificial intelligence are vital in advancing digital ***,the opaque and non-standardised nature embedded in most medical data extraction is prone to batch effects and has become a significant obstacle to reproducing previous *** paper aims to develop an easy-to-use time-series multimodal data extraction pipeline,Quick-MIMIC,for standardised data extraction from MIMIC *** method can fully integrate different data structures into a time-series table,including structured,semi-structured,and unstructured *** also introduce two additional modules to Quick-MIMIC,a pipeline parallelization method and data analysis methods,for reducing the data extraction time and presenting the characteristics of the extracted data *** extensive experimental results show that our pipeline can efficiently extract the needed data from the MIMIC dataset and convert it into the correct format for further analytic tasks.

关键词： MIMIC dataset data extraction pipeline data integration

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Energy efficient resource controller for Apache Storm

Energy efficient resource controller for Apache Storm

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作者： HoseinyFarahabady, MohammadReza Taheri, Javid Zomaya, Albert Y. Tari, Zahir Center for Distributed and High Performance Computing School of Computer Science The University of Sydney CamperdownNSW Australia Department of Computer Science Karlstad University Karlstad Sweden School of Computing Technologies RMIT University MelbourneVIC Australia

Apache Storm is a distributed processing engine that can reliably process unbounded streams of data for real-time applications. While recent research activities mostly focused on devising a resource allocation and task scheduling algorithm to satisfy high performance or low latency requirements of Storm applications across a distributed and multi-core system, finding a solution that can optimize the energy consumption of running applications remains an important research question to be further explored. In this article, we present a controlling strategy for CPU throttling that continuously optimize the level of consumed energy of a Storm platform by adjusting the voltage and frequency of the CPU cores while running the assigned tasks under latency constraints defined by the end-users. The experimental results running over a Storm cluster with 4 physical nodes (total 24 cores) validates the effectiveness of proposed solution when running multiple compute-intensive operations. In particular, the proposed controller can keep the latency of analytic tasks, in terms of 99th latency percentile, within the quality of service requirement specified by the end-user while reducing the total energy consumption by 18% on average across the entire Storm platform. © 2021 John Wiley & Sons, Ltd.

关键词： Storms

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