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International Conference on Parallel and distributed systems (ICPADS)

作者： Mustafizur Rahman Rajiv Ranjan Rajkumar Buyya Cloud Computing and Distributed Systems Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia Service Oriented Computing Research Group School of Computer Science and Engineering University of New South Wales Australia

In this paper, we propose a taxonomy that characterizes and classifies different components of autonomic application management in Grids. We also survey several representative Grid systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the similarities and differences of state-of-the-art technologies utilized in autonomic application management from the perspective of Grid computing, but also identifies the areas that require further research initiatives.

关键词： Monitoring Resource management Taxonomy Processor scheduling Decision making Dynamic scheduling

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DEMOTS: A Decentralized Task Scheduling Algorithm for Micro-clouds with Dynamic Power-Budgets

DEMOTS: A Decentralized Task Scheduling Algorithm for Micro-...

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IEEE International Conference on cloud computing, cloud

作者： Tharindu B. Hewage Shashikant Ilager Maria A. Rodriguez Patricia Arroba Rajkumar Buyya The Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Australia Vienna University of Technology (TU Wien) Vienna Austria Laboratorio de Sistemas Integrados (LSI) CCS-Center for Computational Simulation Universidad Politécnica de Madrid Spain

The Internet of Things (IoT) driven latency-critical applications are deployed on lightweight Micro-clouds at the network's edge. Renting physical space from geographically distributed colocation datacenters connected via a Wide Area Network (WAN) is a cost-effective way of deploying Micro-clouds, despite WANs' dynamic communication latency from traffic congestion. However, this deployment approach can limit Micro-clouds to operate within a soft power budget, as colocation datacenter providers utilize it to add more servers and lower capital costs through oversubscribing power infrastructure. As a result, Micro-clouds use extreme energy reduction measures like power capping and task throttling to address power overdraw events, where power consumption exceeds soft power budget limits, which reduces the performance of latency-critical applications. We propose a solution where a dynamic power budget can be achieved by adding renewable energy sources to the existing soft power budget without upgrading power delivery systems. To take advantage of this, we propose a dynamic, decentralized task-scheduling algorithm called DEMOTS. DEMOTS effectively utilizes the available dynamic power budget in a WAN with varying degrees of network traffic congestion, thereby avoiding the need for extreme energy reduction measures. We implement DEMOTS on a simulation test-bed. Compared to state-of-the-art baseline using MCOP for decentralized task-scheduling in Micro-clouds, DEMOTS reduces Power Overdraw Impact up to 19%, Task Latency Increase Impact up to 47 %, and Task Schedule Time Impact up to 49%.

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Kernel methods based approaches to image classification and retrieval

Kernel methods based approaches to image classification and ...

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International Conference on Advanced computing, ICAC

作者： C. Chandra Sekhar Sasi Kumar Madhan Subhas Raj Kumar Buyya Department of Computer Science and Engineering Indian Institute of Technology Madras CDAC Pune Chronus Corporation Computer Science and Software Engineering and Director of the Cloud Computing and Distributed Systems (CLOUDS) Laboratory at the University of Melbourne Australia

Support vector machine (SVM) is a pattern classification model suitable for classification and annotation of images using non-vectorial type representations of images. Varying length patterns extracted from image data correspond to sets of local feature vectors. Kernels designed for varying length patterns are called as dynamic kernels. The talk presents the issues in designing the dynamic kernel based SVMS for classification and annotation of images. Different methods for designing the dynamic kernels are presented. An intermediate matching kernel (IMK) for a pair of varying length patterns is constructed by matching the pairs of local feature vectors selected using a set of virtual feature vectors. For patterns corresponding to sets of feature vectors, a Gaussian mixture model (GMM) is used as the set of virtual feature vectors. The GMM-based IMK is considered for image processing tasks such as image classification, matching and annotation in content-based image retrieval. The talk presents results of experimental studies on image classification, annotation and retrieval of images using the kernel methods.

关键词： cloud computing Kernel Technological innovation Support vector machine classification Learning systems Hidden Markov models Computational modeling

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Automated Controller Placement for Software-Defined Networks to Resist DDoS Attacks

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Computers, Materials & Continua 2021年第9期68卷 3147-3165页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Lee Ching Kwang Rizaludin Kaspin Bhawani Shankar Chowdhry Rajkumar Buyya Satya Prasad Majumder Manoj Gupta Shuaib Memon Faculty of Computing&Informatics Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation CentreCyberjaya63000SelangorMalaysia National Center of Robotics and Automation Mehran University of Engineering&TechnologyJamshoroPakistan Department of Computer Science and Engineering Hanyang UniversitySeoul04763South Korea School of Electrical and Electronic Engineering Nanyang Technological University639798Singapore Cloud Computing and Distributed Systems Laboratory The University of MelbourneMelbourneVIC 3053Australia Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology(BUET)Dhaka1205Bangladesh Department of Electronics and Communication Engineering JECRC UniversityVidhaniJaipur303905India Auckland Institute of Studies Mt AlbertAucklandNew Zealand

In software-defined networks(SDNs),controller placement is a critical factor in the design and planning for the future Internet of Things(IoT),telecommunication,and satellite communication *** research has concentrated largely on factors such as reliability,latency,controller capacity,propagation delay,and energy ***,SDNs are vulnerable to distributed denial of service(DDoS)attacks that interfere with legitimate use of the *** ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design,especially in critical applications such as military,health care,and financial services networks requiring high *** propose a mathematical model for planning the deployment of SDN smart backup controllers(SBCs)to preserve service in the presence of DDoS *** a number of input parameters,our model has two distinct ***,it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating ***,it recommends an optimal number of smart backup controllers for use with different levels of DDoS *** goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the *** simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.

关键词： SDN automated controller placement SBC ILP DDoS attack

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cloud computing

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China Communications 2014年第4期11卷 I0001-I0002页

作者：崔勇 Rajkumar Buyya 刘江川 Tsinghua University Network Technology in Institute Tsinghua University IPv6 Transition Softwire WG of IETF Tsinghua University Computer Science and Software Engineering at the University of Melbourne Cloud Computing and Distributed Systems(CLOUDS) Laboratory at the University of Melbourne School of Computing Science at Simon Fraser University

cloud computing is a novel computing paradigm that utilizes remote cloud resources to achieve a high-performance *** provides infrastructure,platform and software as different on-demand *** has made remarkable progress in cloudbased products and operating system *** government,enterprises and research institutions are all active in the development of cloud computing-related *** the progress,many important

关键词：计算模式性能计算基础设施操作系统科研机构远程软件

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Machine learning-based orchestration of containers: A taxonomy and future directions

arXiv

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arXiv 2021年

作者： Zhong, Zhiheng Xu, Minxian Rodriguez, Maria Alejandra Xu, Chengzhong Buyya, Rajkumar Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems University of Melbourne Grattan Street Parkville MelbourneVICVIC 3010 Australia Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen University Town 1068 Xueyuan Avenue Guangdong Shenzhen518055 China State Key Lab of IOTSC University of Macau Avenida da Universidade Taipa 999078 China

Containerization is a lightweight application virtualization technology, providing high environmental consistency, operating system distribution portability, and resource isolation. Existing mainstream cloud service providers have prevalently adopted container technologies in their distributed system infrastructures for automated application management. To handle the automation of deployment, maintenance, autoscaling, and networking of containerized applications, container orchestration is proposed as an essential research problem. However, the highly dynamic and diverse feature of cloud workloads and environments considerably raises the complexity of orchestration mechanisms. Machine learning algorithms are accordingly employed by container orchestration systems for behavior modelling and prediction of multi-dimensional performance metrics. Such insights could further improve the quality of resource provisioning decisions in response to the changing workloads under complex environments. In this paper, we present a comprehensive literature review of existing machine learning-based container orchestration approaches. Detailed taxonomies are proposed to classify the current researches by their common features. Moreover, the evolution of machine learning-based container orchestration technologies from the year 2016 to 2021 has been designed based on objectives and metrics. A comparative analysis of the reviewed techniques is conducted according to the proposed taxonomies, with emphasis on their key characteristics. Finally, various open research challenges and potential future directions are highlighted. © 2021, CC0.

关键词： Containers

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Transforming Research with Quantum computing

Journal of Economy and Technology

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Journal of Economy and Technology 2024年

作者： Sukhpal Singh Gill Rajkumar Buyya School of Electronic Engineering and Computer Science Queen Mary University of London London UK Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Australia

Quantum computing is a novel method of computation that uses the principles of quantum mechanics to handle highly challenging situations in a very short amount of time. Quantum technology has the ability to significantly impact worldwide advancement, even prior to the complete deployment of quantum machines. Quantum technology for communication, computation, and sensors has the capacity to revolutionise many industries, and several nations are making investments in this promising field. This includes research investments from both the public and commercial sectors. This article delves into the recent quantum computing advancements and the potential opportunities made possible by quantum technology in the next few decades. We outline a vision and scientific innovation for embracing the quantum age, as well as explore the pioneering applications of quantum computing. We also highlight software tools and platforms for quantum programming to unlock the power of computing and revolutionize the world. Finally, we identify the groundbreaking impacts of quantum computing on next-generation research and discuss the benefits of unleashing its revolutionary capabilities.

关键词： Quantum computing Quantum Technology Quantum Artificial Intelligence Quantum Machine Learning Quantum cloud computing Quantum Applications Quantum Programming Quantum Simulations Quantum Supermacy

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Hermes: Memory-Efficient Pipeline Inference for Large Models on Edge Devices

Hermes: Memory-Efficient Pipeline Inference for Large Models...

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IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Xueyuan Han Zinuo Cai Yichu Zhang Chongxin Fan Junhan Liu Ruhui Ma Rajkumar Buyya ParisTech Elite Institute of Technology Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China UM-SJTU Joint Institute Shanghai Jiao Tong University Shanghai China Shanghai Aerospace System Engineering Institute Shanghai China Cloud Computing and Distributed Systems (CLOUDS) Laboratory The University of Melbourne Melbourne Australia

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

The application of Transformer-based large models has achieved numerous success in recent years. However, the exponential growth in the parameters of large models introduces formidable memory challenge for edge deployment. Prior works to address this challenge mainly focus on optimizing the model structure and adopting memory swapping methods. However, the former reduces the inference accuracy, and the latter raises the inference latency. This paper introduces PIPELoAD, a novel memory-efficient pipeline execution mechanism. It reduces memory usage by incorporating dynamic memory management and minimizes inference latency by employing parallel model loading. Based on PIPELoAD mechanism, we present Hermes, a framework optimized for large model inference on edge devices. We evaluate Hermes on Transformer-based models of different sizes. Our experiments illustrate that Hermes achieves up to 4.24 x increase in inference speed and 86.7% lower memory consumption than the state-of-the-art pipeline mechanism for BERT and ViT models, 2.58 x increase in inference speed and 90.3% lower memory consumption for GPT-style models.

关键词： Performance evaluation Accuracy Computational modeling Memory management Pipelines Loading Transformers Load modeling

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DTCMS: Dynamic traffic congestion management in Social Internet of Vehicles (SIoV)

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Internet of Things (Netherlands) 2021年 16卷

作者： Roopa, M.S. Ayesha Siddiq, S. Buyya, Rajkumar Venugopal, K.R. Iyengar, S.S. Patnaik, L.M. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru 560-001 India Cloud Computing and Distributed Systems Laboratory School of Computing and Information Systems University of Melbourne Melbourne 3053 VIC Australia Bangalore University Bengaluru 560-001 India Department of Computer Science and Engineering Florida International University Miami 33199 FL United States Consciousness Studies Program National Institute of Advanced Studies Indian Institute of Science Bengaluru 560-012 India

With the augmentation of traffic exponentially, we observe that traffic congestion does not guarantee road safety or enhance the driving experience. In the recent past, Social Internet of Vehicles (SIoV), a social network paradigm permits social relationships among every vehicle in the network or with any road infrastructure to render a radically useful environment. SIoV is beneficial for the drivers, in improving road safety, avoiding mishaps, and providing a friendly-driving experience. In this paper, we propose a traffic scheduling algorithm to gain the maximum throughput for the flow of vehicles at a road intersection with the formation of social relationships among the vehicles and with the Road Side Units (RSUs). The algorithm estimates the flow rate of vehicles for lanes at the intersections exploiting the volume of traffic moving through the given road. A condition matrix is designed for the consistent movement of traffic considering different routes on the road segments. Social relationships are devised on various aspects of travel needs for a safe, agile, and better driving experience. Simulation results illustrate the efficacy of the proposed scheme with high traffic throughput, service rate and reduce the total travelling time, delay time, and average waiting time in comparison with Dynamic Throughput Maximization Framework and Adaptive Traffic Control Algorithm. © 2020 Elsevier B.V.

关键词： Dynamic traffic control Social Internet of Vehicles (SIoV) Social relationships Traffic congestion management

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Efficient Training Approaches for Performance Anomaly Detection Models in Edge computing Environments

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ACM Transactions on Autonomous and Adaptive systems 1000年

作者： Duneesha Fernando Maria Rodriguez Patricia Arroba Leila Ismail Rajkumar Buyya Quantum Cloud Computing and Distributed Systems (qCLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Australia CCS–Center for Computational Simulation Universidad Politécnica de Madrid Spain Intelligent Distributed Computing and Systems Research (INDUCE) Lab Department of Computer Science and Software Engineering United Arab Emirates University United Arab Emirates

Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to performance anomalies caused by resource hogging (e.g., CPU or memory), resource contention, etc., which can negatively impact their Quality of Service and violate their Service Level Agreements. Existing research on performance anomaly detection for edge computing environments focuses on model training approaches that either achieve high accuracy at the expense of a time-consuming and resource-intensive training process or prioritize training efficiency at the cost of lower accuracy. To address this gap, while considering the resource constraints and the large number of devices in modern edge platforms, we propose two clustering-based model training approaches: (1) intra-cluster parameter transfer learning-based model training (ICPTL) and (2) cluster-level model training (CM). These approaches aim to find a trade-off between the training efficiency of anomaly detection models and their accuracy. We compared the models trained under ICPTL and CM to models trained for specific devices (most accurate, least efficient) and a single general model trained for all devices (least accurate, most efficient). Our findings show that ICPTL’s model accuracy is comparable to that of the model per device approach while requiring only 40% of the training time. In addition, CM further improves training efficiency by requiring 23% less training time and reducing the number of trained models by approximately 66% compared to ICPTL, yet achieving a higher accuracy than a single general model.

关键词： Edge computing Microservices IoT Performance anomaly detection Machine learning Deep learning

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