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3rd IEEE International Conference on e-Science and Grid computing

作者： Reich, Christoph Bubendorfer, Kris Banholzer, Matthias Buyya, Rajkumar Department of Computer Science Hochschule Furtwangen University Germany School of Mathematics Statistics and Computer Science Victoria University of Wellington New Zealand Grid Computing and Distributed Systems (GRIDS) Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia

ISBN: (纸本)9780769530642

Service-Oriented Architectures provide integration of interoperability for independent and loosely coupled services. Web services and the associated new standards such as WSRF are frequently used to realise such Service-Oriented Architectures. In such systems, autonomic principles of self-configuration, self-optimisation, self-healing and self-adapting are desirable to ease management and improve robustness. In this paper we focus on the extension of the self management and autonomic behaviour of a WSRF container connected by a structured P2P overlay network to monitor and rectify its QoS to satisfy its SLAs. The SLA plays an important role during two distinct phases in the life-cycle of a WSRF container Firstly during service deployment when services are assigned to containers in such a way as to minimise the threat of SLA violations, and secondly during maintenance when violations are detected and services are migrated to other containers to preserve QoS. In addition, as the architecture has been designed and built using standardised modem technologies and with high levels of transparency, conventional web services can be deployed with the addition of a SLA specification.

关键词： Containers

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The grid economy

The grid economy

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作者： Buyya, Rajkumar Abramson, David Venugopal, Srikumar Grid Computing and Distributed Systems Laboratory Department of Computer Science and Software Engineering University of Melbourne Vic. 3010 Australia School of Computer Science and Software Engineering Monash University Vic. 3163 Australia

This paper identifies challenges in managing resources in a Grid computing environment and proposes computational economy as a metaphor for effective management of resources and application scheduling. It identifies distributed resource management challenges and requirements of economy-based Grid systems, and discusses various representative economy-based systems, both historical and emerging, for cooperative and competitive trading of resources such as CPU cycles, storage, and network bandwidth. It presents an extensible, service-oriented Grid architecture driven by Grid economy and an approach for its realization by leveraging various existing Grid technologies. It also presents commodity and auction models for resource allocation. The use of commodity economy model for resource management and application scheduling in both computational and data grids is also presented. © 2005 IEEE.

关键词： Computational methods

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Size doesn't matter? On the value of software size features for effort estimation 12

Size doesn't matter? On the value of software size features ...

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8th International Conference on Predictive Models in Software Engineering, PROMISE 2012 - Co-located with ESEM 2012

作者： Kocaguneli, Ekrem Menzies, Tim Hihn, Jairus Kang, Byeong Ho LCSEE West Virginia University Morgantown United States Jet Propulsion Laboratory California Institute of Technology Pasadena United States School of Computing and Information Systems University of Tasmania Australia

ISBN: (纸本)9781450312417

Background: Size features such as lines of code and function points are deemed essential for effort estimation. No one questions under what conditions size features are actually a "must". Aim: To question the need for size features and to propose a method that compensates their absence. Method: A baseline analogy-based estimation method (1NN) and a state-of-the-art learner (CART) are run on reduced (with no size features) and full (with all features) versions of 13 SEE data sets. 1NN is augmented with a popularity-based pre-processor to create "pop1NN". The performance of pop1NN is compared to 1NN and CART using 10-way cross validation w.r.t. MMRE, MdMRE, MAR, PRED(25), MBRE, MIBRE, and MMER. Results: Without any pre-processor, removal of size features decreases the performance of 1NN and CART. For 11 out of 13 data sets, pop1NN removes the necessity of size features. pop1NN (using reduced data) has a comparable performance to CART (using full data). Conclusion: Size features are important and their use is endorsed. However, if there are insufficient means to collect software size metrics, then the use of methods like pop1NN may compensate for size metrics with only a small loss in estimation accuracy. Copyright © 2012 ACM.

关键词： Nearest neighbor search

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A novel visual attention method for target detection from SAR images

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Chinese Journal of Aeronautics 2019年第8期32卷 1946-1958页

作者： Fei GAO Aidong LIU Kai LIU Erfu YANG Amir HUSSAIN School of Electronic and Information Engineering Beihang UniversityBeijing 100083China Space Mechatronic Systems Technology Laboratory Department of DesignManufacture and Engineering ManagementUniversity of StrathclydeGlasgow G1 1XJUK Cognitive Big Data and Cyber-Informatics(CogBID)Laboratory School of ComputingEdinburgh Napier UniversityEdinburgh EH10 5DTScotlandUK

Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down *** the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio(SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.

关键词： Learning strategy Synthetic Aperture Radar(SAR) images Target detection Top-down Visual attention mechanism

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FLight: A Lightweight Federated Learning Framework in Edge and Fog computing

arXiv

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arXiv 2023年

作者： Zhu, Wuji Goudarzi, Mohammad Buyya, Rajkumar Laboratory School of Computing and Information Systems The University of Melbourne Australia Australia

The number of Internet of Things (IoT) applications, especially latency-sensitive ones, have been significantly increased. So, cloud computing, as one of the main enablers of the IoT that offers centralized services, cannot solely satisfy the requirements of IoT applications. Edge/Fog computing, as a distributed computing paradigm, processes, and stores IoT data at the edge of the network, offering low latency, reduced network traffic, and higher bandwidth. The Edge/Fog resources are often less powerful compared to cloud, and IoT data is dispersed among many geo-distributed servers. Hence, Federated Learning (FL), which is a machine learning approach that enables multiple distributed servers to collaborate on building models without exchanging the raw data, is well-suited to Edge/Fog computing environments, where data privacy is of paramount importance. Besides, to manage different FL tasks on Edge/Fog computing environments, a lightweight resource management framework is required to manage different incoming FL tasks while does not incur significant overhead on the system. Accordingly, in this paper, we propose a lightweight FL framework, called FLight, to be deployed on a diverse range of devices, ranging from resource-limited Edge/Fog devices to powerful cloud servers. FLight is implemented based on the FogBus2 framework, which is a containerized distributed resource management framework. Moreover, FLight integrates both synchronous and asynchronous models of FL. Besides, we propose a lightweight heuristic-based worker selection algorithm to select a suitable set of available workers to participate in the training step to obtain higher training time efficiency. The obtained results demonstrate the efficiency of the FLight. The worker selection technique reduces the training time of reaching 80% accuracy by 34% compared to sequential training, while asynchronous one helps to improve synchronous FL training time by 64%. Copyright © 2023, The Authors. All rights res

关键词： Internet of things

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TF-DDRL: A Transformer-enhanced distributed DRL Technique for Scheduling IoT Applications in Edge and cloud computing Environments

arXiv

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arXiv 2024年

作者： Wang, Zhiyu Goudarzi, Mohammad Buyya, Rajkumar Laboratory School of Computing and Information Systems The University of Melbourne Australia Australia

With the continuous increase of IoT applications, their effective scheduling in edge and cloud computing has become a critical challenge. The inherent dynamism and stochastic characteristics of edge and cloud computing, along with IoT applications, necessitate solutions that are highly adaptive. Currently, several centralized Deep Reinforcement Learning (DRL) techniques are adapted to address the scheduling problem. However, they require a large amount of experience and training time to reach a suitable solution. Moreover, many IoT applications contain multiple interdependent tasks, imposing additional constraints on the scheduling problem. To overcome these challenges, we propose a Transformer-enhanced distributed DRL scheduling technique, called TF-DDRL, to adaptively schedule heterogeneous IoT applications. This technique follows the Actor-Critic architecture, scales efficiently to multiple distributed servers, and employs an off-policy correction method to stabilize the training process. In addition, Prioritized Experience Replay (PER) and Transformer techniques are introduced to reduce exploration costs and capture long-term dependencies for faster convergence. Extensive results of practical experiments show that TF-DDRL, compared to its counterparts, significantly reduces response time, energy consumption, monetary cost, and weighted cost by up to 60%, 51%, 56%, and 58%, respectively. © 2024, CC BY.

关键词： Deep reinforcement learning

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Container-based cluster orchestration systems: A taxonomy and future directions

arXiv

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arXiv 2018年

作者： Rodriguez, Maria A. Buyya, Rajkumar CLOUDS Laboratory School of Computing and Information Systems University of Melbourne Australia

Containers, enabling lightweight environment and performance isolation, fast and flexible deployment, and fine-grained resource sharing, have gained popularity in better application management and deployment in addition to hardware virtualization. They are being widely used by organizations to deploy their increasingly diverse workloads derived from modern-day applications such as web services, big data, and IoT in either proprietary clusters or private and public cloud data centers. This has led to the emergence of container orchestration platforms, which are designed to manage the deployment of containerized applications in large-scale clusters. These systems are capable of running hundreds of thousands of jobs across thousands of machines. To do so efficiently, they must address several important challenges including scalability, fault-tolerance and availability, efficient resource utilization, and request throughput maximization among others. This paper studies these management systems and proposes a taxonomy that identifies different mechanisms that can be used to meet the aforementioned challenges. The proposed classification is then applied to various state-of-the-art systems leading to the identification of open research challenges and gaps in the literature intended as future directions for researchers. Copyright © 2018, The Authors. All rights reserved.

关键词： Containers

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Managing trust in distributed environments

Managing trust in distributed environments

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IEEE/ACS International Conference on Pervasive Services (ICPS)

作者： K. Aberer Distributed Information Systems Laboratory Institute for Core Computing Science School for Computer and Communication Science EPF Lausanne Lausanne Switzerland

Summary form only given. In pervasive computing the involved agents and services must organize themselves dynamically both in cooperative and non-cooperative environments in order to enable sharing of distributed resources and knowledge discovery. Provisioning of trusted services appears to be an important factor to enable the success of this new computing paradigm. Reputation-based trust management models are becoming a focus of the research community as a viable solution. On the one hand, the trust models must satisfy constraints imposed by the environment, such as scalability, on the other hand, they must comply with the unique properties of trust as a social and economic phenomenon. In this paper we classify and compare approaches that have been proposed to fulfil this task. The identified classes are: social networks, probabilistic estimation techniques and game-theoretic reputation systems. Social networks and probabilistic estimation techniques operate on so called probabilistic behavior, while game-theoretic models are suitable for rational behavior. Probabilistic techniques normally imply smaller implementation overheads than social networks and enable a more intuitive transition of reputation to trust, while social networks are believed to be able to better detect misbehavior in a broader range of settings. Game-theoretic models, as appropriate for rational economic agents maximizing their utilities, can under specific circumstances enforce trustworthy behavior.

关键词： Environmental management Social network services Pervasive computing distributed computing Environmental economics distributed information systems Scalability

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UNPCC: A novel unsupervised classification scheme for network intrusion detection

UNPCC: A novel unsupervised classification scheme for networ...

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18th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2006

作者： Xie, Zongxing Quirino, Thiago Shyu, Mei-Ling Chen, Shu-Ching Chang, LiWu Department of Electrical and Computer Engineering University of Miami Coral Gables FL 33124 United States Distributed Multimedia Information System Laboratory School of Computing and Information Sciences Florida International University Miami FL United States Center for High Assurance Computer Systems Naval Research Laboratory Washington DC United States

ISBN: (纸本)0769527280

The development of effective classification techniques, particularly unsupervised classification, is important for real-world applications since information about the training data before classification is relatively unknown. In this paper, a novel unsupervised classification algorithm is proposed to meet the increasing demand in the domain of network intrusion detection. Our proposed UNPCC (Unsu-pervised Principal Component Classifier) algorithm is a multi-class unsupervised classifier with absolutely no requirements for any a priori class related data information (e.g., the number of classes and the maximum number of instances belonging to each class), and an inherently natural supervised classification scheme, both which present high detection rates and several operational advantages (e.g., lower training time, lower classification time, lower processing power requirement, and lower memory requirement). Experiments have been conducted with the KDD Cup 99 data and network traffic data simulated from our private network testbed, and the promising results demonstrate that our UNPCC algorithm outperforms several well-known supervised and unsupervised classification algorithms. © 2006 IEEE.

关键词： Intrusion detection

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TWE‐WSD: An effective topical word embedding based word sense disambiguation

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CAAI Transactions on Intelligence Technology 2021年第1期6卷 72-79页

作者： Lianyin Jia Jilin Tang Mengjuan Li Jinguo You Jiaman Ding Yinong Chen Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunmingChina Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunmingChina Library Yunnan Normal UniversityKunmingChina School of Computing Informaticsand Decision SystemsArizona State UniversityTempeArizonaUSA

Word embedding has been widely used in word sense disambiguation(WSD)and many other tasks in recent years for it can well represent the semantics of ***,the existing word embedding methods mostly represent each word as a single vector,without considering the homonymy and polysemy of the word;thus,their performances are *** order to address this problem,an effective topical word embedding(TWE)‐based WSD method,named TWE‐WSD,is proposed,which integrates Latent Dirichlet Allocation(LDA)and word *** of generating a single word vector(WV)for each word,TWE‐WSD generates a topical WV for each word under each *** integrating strategies are designed to obtain high quality contextual *** experiments on SemEval‐2013 and SemEval‐2015 for English all‐words tasks showed that TWE‐WSD outperforms other state‐of‐the‐art WSD methods,especially on nouns.

关键词： embedding Word WSD

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