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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

Computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in cloud computing Environments

A Particle Swarm Optimization-Based Heuristic for Scheduling...

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International Conference on Advanced Information Networking and Applications (AINA)

作者： Suraj Pandey Linlin Wu Siddeswara Mayura Guru Rajkumar Buyya Cloud Computing and Distributed Systems Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia CSIRO Tasmanian ICT Centre Hobart Australia

ISBN: (纸本)9781424466955;9780769540184

cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. However, users are charged on a pay-per-use basis. User applications may incur large data retrieval and execution costs when they are scheduled taking into account only the `execution time'. In addition to optimizing execution time, the cost arising from data transfers between resources as well as execution costs must also be taken into account. In this paper, we present a particle swarm optimization (PSO) based heuristic to schedule applications to cloud resources that takes into account both computation cost and data transmission cost. We experiment with a workflow application by varying its computation and communication costs. We compare the cost savings when using PSO and existing `Best Resource Selection' (BRS) algorithm. Our results show that PSO can achieve: (a) as much as 3 times cost savings as compared to BRS, and (b) good distribution of workload onto resources.

关键词： Particle swarm optimization Processor scheduling cloud computing Application software Cost function Computational efficiency Dynamic scheduling Computer science Software engineering Application virtualization

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and cloud computing System

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Procedia Computer Science 2023年 220卷 331-338页

作者： Alain Hennebelle Huned Materwala Leila Ismail Independent Researcher Melbourne Australia Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR.

关键词： Artificial Intelligence (AI) cloud computing Diagnosis Digital Health Edge computing eHealth Internet of Things (IoT) Logisitic Regression Machine Learning Prognosis Random Forest Risk Factors Smart Connected Healthcare Type 2 Diabetes Mellitus

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cloudAnalyst: A cloudSim-based Visual Modeller for Analysing cloud computing Environments and Applications

CloudAnalyst: A CloudSim-based Visual Modeller for Analysing...

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International Conference on Advanced Information Networking and Applications

作者： Bhathiya Wickremasinghe Rodrigo N. Calheiros Rajkumar Buyya The Cloud Computing and Distributed Systems (CLOUDS) Laboratory Department of Computer Science and Software Engineering The University of Melbourne Pontifical Catholic University of Rio Grande do Sul

ISBN: (纸本)9781424466955;9780769540184

Advances in cloud computing opens up many new possibilities for Internet applications developers. Previously, a main concern of Internet applications developers was deployment and hosting of applications, because it required acquisition of a server with a fixed capacity able to handle the expected application peak demand and the installation and maintenance of the whole software infrastructure of the platform supporting the application. Furthermore, server was underutilized because peak traffic happens only at specific times. With the advent of the cloud, deployment and hosting became cheaper and easier with the use of pay-peruse flexible elastic infrastructure services offered by cloud providers. Because several cloud providers are available, each one offering different pricing models and located in different geographic regions, a new concern of application developers is selecting providers and data center locations for applications. However, there is a lack of tools that enable developers to evaluate requirements of large-scale cloud applications in terms of geographic distribution of both computing servers and user workloads. To fill this gap in tools for evaluation and modeling of cloud environments and applications, we propose cloudAnalyst. It was developed to simulate large-scale cloud applications with the purpose of studying the behavior of such applications under various deployment configurations. cloudAnalyst helps developers with insights in how to distribute applications among cloud infrastructures and value added services such as optimization of applications performance and providers incoming with the use of Service Brokers.

关键词： cloud computing Modeling Simulation

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Resource Provisioning Policies to Increase IaaS Provider's Profit in a Federated cloud Environment

Resource Provisioning Policies to Increase IaaS Provider's P...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Adel Nadjaran Toosi Rodrigo N. Calheiros Ruppa K. Thulasiram Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia Department of Computer Science University of Manitoba Canada

cloud Federation is a recent paradigm that helps Infrastructure as a Service (IaaS) providers to overcome resource limitation during spikes in demand for Virtual Machines (VMs) by outsourcing requests to other federation members. IaaS providers also have the option of terminating spot VMs, i.e, cheaper VMs that can be canceled to free resources for more profitable VM requests. By both approaches, providers can expect to reject less profitable requests. For IaaS providers, pricing and profit are two important factors, in addition to maintaining a high Quality of Service (QoS) and utilization of their resources to remain in the business. For this, a clear understanding of the usage pattern, types of requests, and infrastructure costs are necessary while making decisions to terminate spot VMs, outsourcing or contributing to the federation. In this paper, we propose policies that help in the decision-making process to increase resources utilization and profit. Simulation results indicate that the proposed policies enhance the profit, utilization, and QoS (smaller number of rejected VM requests) in a cloud federation environment.

关键词： Outsourcing Quality of service Mathematical model Equations Pricing Computational modeling Availability

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The Interplay Between Timeliness and Scalability in cloud Monitoring systems

The Interplay Between Timeliness and Scalability in Cloud Mo...

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IEEE Symposium on Computers and Communication

作者： Guilherme da Cunha Rodrigues Rodrigo N. Calheiros Marcio Barbosa de Carvalho Carlos Raniery Paula dos Santos Lisandro Zambenedetti Granville Liane Tarouco Rajkumar Buyya Computer Networks Group Institute of Informatics Federal University of Rio Grande do Sul Cloud Computing and Distributed Systems (CLOUDS) Laboratory Department of Computing and Information Systems The University of Melbourne Department of Applied Computing Federal University of Santa Maria

ISBN: (纸本)9781467371957

cloud computing is a groundbreaking solution to acquire computational resources on demand. To deliver high quality cloud services and provide features such as reduced costs and availability to customers, a cloud, like any other computational system, needs to be properly managed in accordance with its characteristics (e.g., scalability, elasticity, timeliness). In this scenario, cloud monitoring is a key to achieve it. To properly work, cloud monitoring systems need to meet several requirements such as scalability, accuracy, and timeliness. This paper aims to unveil the trade-off between timeliness and scalability. Evaluations demonstrate the mutual influence between scalability and timeliness based on monitoring parameters (e.g., monitoring topologies, frequency sampling). Results show that non-deep monitoring topologies and decreasing the frequency sampling assist to reduce the mutual influence between timeliness and scalability.

关键词： Scalability cloudS clouds monitoring systems Monitoring parameters cloud computing

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DRLQ: A Deep Reinforcement Learning-based Task Placement for Quantum cloud computing

DRLQ: A Deep Reinforcement Learning-based Task Placement for...

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IEEE International Conference on cloud computing, cloud

作者： Hoa T. Nguyen Muhammad Usman Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Parkville Victoria Australia School of Physics The University of Melbourne Parkville Victoria Australia Data61 CSIRO Clayton Victoria Australia

ISBN: (数字)9798350368536

ISBN: (纸本)9798350368543

The quantum cloud computing paradigm presents unique challenges in task placement due to the dynamic and heterogeneous nature of quantum computation resources. Traditional heuristic approaches fall short in adapting to the rapidly evolving landscape of quantum computing. This paper proposes DRLQ, a novel Deep Reinforcement Learning (DRL)- based technique for task placement in quantum cloud computing environments, addressing the optimization of task completion time and quantum task scheduling efficiency. It leverages the Deep Q Network (DQN) architecture, enhanced with the Rainbow DQN approach, to create a dynamic task placement strategy. This approach is one of the first in the field of quantum cloud resource management, enabling adaptive learning and decision-making for quantum cloud environments and effectively optimizing task placement based on changing conditions and resource availability. We conduct extensive experiments using the QSimPy simulation toolkit to evaluate the performance of our method, demonstrating substantial improvements in task execution efficiency and a reduction in the need to reschedule quantum tasks. Our results show that utilizing the DRLQ approach for task placement can significantly reduce total quantum task completion time by 37.81 % to 72.93% and prevent task rescheduling attempts compared to other heuristic approaches.

关键词： cloud computing Quantum computing Processor scheduling Decision making Computer architecture Dynamic scheduling Deep reinforcement learning

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iQuantum: A Case for Modeling and Simulation of Quantum computing Environments

iQuantum: A Case for Modeling and Simulation of Quantum Comp...

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Quantum Software (QSW), IEEE International Conference on

Today’s quantum computers are primarily accessible through the cloud and are expected to be deployed in edge networks in the near future. With the rapid advancement and proliferation of quantum computing research worldwide, there has been a considerable increase in demand for using cloud-based quantum computation resources. This demand has highlighted the need for designing efficient and adaptable resource management strategies and service models for quantum computing. However, the limited quantity, quality, and accessibility of quantum resources pose significant challenges to practical research in quantum software and systems. To address these challenges, we propose iQuantum, a first-of-its-kind simulation toolkit that can model quantum computing environments for prototyping and evaluating system design and scheduling algorithms. This paper presents the quantum computing system model, architectural design, proof-of-concept implementation, potential use cases, and future development of iQuantum. Our proposed iQuantum simulator is anticipated to boost research in quantum software and systems, particularly in the creation and evaluation of policies and algorithms for resource management, job scheduling, and hybrid quantum-classical task orchestration in quantum computing environments integrating edge and cloud resources.

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An Adaptive Decision-Making Approach for Better Selection of Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation

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Procedia Computer Science 2023年 220卷 470-477页

作者： Rima Kaafarani Leila Ismail Oussama Zahwe ICCS-Lab Computer Science Department American University of Culture and Education 1507 Beirut Lebanon Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Intelligent Distributed Computing and Systems (INDUCE) Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds.

关键词： Blockchain Decision-Map Health Insurance Frauds Detection Hyperledger Fabric Neo Smart Contract Smart Healthcare

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EN-Beats: A Novel Ensemble Learning-Based Method for Multiple Resource Predictions in cloud

EN-Beats: A Novel Ensemble Learning-Based Method for Multipl...

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IEEE International Conference on cloud computing, cloud

作者： Ming Chen Maria Rodriguez Read Patricia Arroba Rajkumar Buyya The Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Laboratorio de Sistemas Integrados (LSI) CCS-Center for Computational Simulation Universidad Politécnica de Madrid Madrid Spain

cloud computing has become an important driving force in the economy and the fundamental facility for digitization transformation. Due to its rapid development and increasing demands, accurate resource usage prediction for the cloud has been a long-term challenge. To address this challenge, this paper introduces RCorrPolicy for resource metrics selection and proposes EN-Beats, an efficient ensemble learning-based approach, for predicting multiple resource usages in the cloud. The paper presents trace-driven experiments conducted on a real-world dataset, demonstrating notable improvements in predicting multiple resource metrics. Ablation experiments conducted on existing methods for RCorrPolicy indicate that the proposed policy enhances the performance of these methods across different evaluated metrics. Furthermore, EN-Beats outperforms existing methods by achieving the lowest NRMSE (lower values indicate better performance) for CPU util rate (up to 8% lower), memory usage (up to 6% lower), and network incoming traffic (up to 3% lower). Additionally, EN-Beats attains the highest R2 score (higher values indicate better performance) for predicting CPU util rate (up to 1.39 higher), memory usage (up to 0.57 higher), and network incoming traffic (up to 0.62 higher).

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