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检索条件"机构=Cloud Computing and Distributed Systems Laboratory"

共 61 条记录，以下是21-30 订阅

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Preemption-Aware Energy Management in Virtualized Data Centers

Preemption-Aware Energy Management in Virtualized Data Cente...

引用

IEEE International Conference on cloud computing, cloud

作者： Mohsen Amini Salehi P. Radha Krishna Krishnamurty Sai Deepak Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory Department of Computing and Information Systems The University of Melbourne Australia Infosys Research Laboratories India

ISBN: (纸本)9781467328920

Energy efficiency is one of the main challenge hat data centers are facing nowadays. A considerable portion of the consumed energy in these environments is wasted because of idling resources. To avoid wastage, offering services with variety of SLAs (with different prices and priorities) is a common practice. The question we investigate in this research is how the energy consumption of a data center that offers various SLAs can be reduced. To answer this question we propose an adaptive energy management policy that employs virtual machine(VM) preemption to adjust the energy consumption based on user performance requirements. We have implementedour proposed energy management policy in Haize a as a real scheduling platform for virtualized data centers. Experimental results reveal 18% energy conservation (up to 4000 kWh in 30 days) comparing with other baseline policies without any major increase in SLA violation.

关键词： Switches Energy management Energy consumption Engines Equations Mathematical model Virtual machining

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Software-Defined cloud computing: Architectural elements and open challenges

Software-Defined Cloud Computing: Architectural elements and...

引用

International Conference on Advances in computing, Communications and Informatics (ICACCI)

作者： Rajkumar Buyya Rodrigo N. Calheiros Jungmin Son Amir Vahid Dastjerdi Young Yoon Cloud Computing and Distributed Systems (CLOUDS) Laboratory The University of Melbourne Australia Samsung Electronics Korea

The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined cloud computing, or simply Software-Defined clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.

关键词： Computational modeling Adaptation models Computer architecture

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Innovations in Computer Science and Engineering 1

引用

丛书名： Lecture Notes in Networks and systems

1000年

作者： H. S. Saini Rishi Sayal A. Govardhan Rajkumar Buyya

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Artificial Intelligence (AI)-Centric Management of Resources in Modern distributed computing systems

Artificial Intelligence (AI)-Centric Management of Resources...

引用

IEEE cloud Summit

作者： Shashikant Ilager Rajeev Muralidhar Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Australia Amazon Web Services (AWS) Australia

ISBN: (数字)9781728182667

ISBN: (纸本)9781728182674

Contemporary distributed computing systems (DCS) such as cloud Data Centers are large scale, complex, heterogeneous, and distributed across multiple networks and geographical boundaries. On the other hand, the Internet of Things (IoT)-driven applications are producing a huge amount of data that requires real-time processing and fast response. Managing these resources efficiently to provide reliable services to end-users or applications is a challenging task. The existing Resource Management systems (RMS) rely on either static or heuristic solutions inadequate for such composite and dynamic systems. The advent of Artificial Intelligence (AI) due to data availability and processing capabilities manifested into possibilities of exploring data-driven solutions in RMS tasks that are adaptive, accurate, and efficient. In this regard, this paper aims to draw the motivations and necessities for data-driven solutions in resource management. It identifies the challenges associated with it and outlines the potential future research directions detailing where and how to apply the data-driven techniques in the different RMS tasks. Finally, it provides a conceptual data-driven RMS model for DCS and presents the two real-time use cases (GPU frequency scaling and data centre resource management from Google cloud and Microsoft Azure) demonstrating AI-centric approaches' feasibility.

关键词： Data centers cloud computing Computational modeling Real-time systems Resource management Artificial intelligence Task analysis

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Financial Option Market Model for Federated cloud Environments

Financial Option Market Model for Federated Cloud Environmen...

引用

IEEE International Conference on Utility and cloud computing (UCC)

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

Pay-per-use service by cloud service providers has attracted customers in the recent past and is still evolving. Since the resources being dealt within clouds are non-storable and the physical resources need to be replaced very often, pricing the service in a way that would return profit on the initial capital investments to the service providers has been a major issue. Moreover, to maintain Quality of Service (QoS) to customers who reserve the resources in advance and may or may not be using the resources at a future date makes the resources wasted, if not allocated to other on-demand users. Therefore, a need for a mechanism to guarantee the resources to reserved users whenever they need them, while keeping the resources busy all the time is in very high demand. The concept of federation of cloud service providers has been proposed in the past wherein resources are traded between the providers whenever need arises. We propose a financial option based cloud resources pricing model to address the above situation. This model allows a provider to hedge the critical and risky situation of reserved users requesting the resources while all the resources have been allocated to other users, by trading (buying or outsourcing) resources from other service providers in the cloud federation. We show that using financial option based contracts between cloud providers in a cloud federation, providers are able to enhance profit and acquire the needed resources at any given time. It would also help creating a trust and goodwill from the clients on the cloud service providers by less number of QoS violation.

关键词： Contracts Pricing Quality of service cloud computing Availability Computational modeling Load modeling

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Preemption-aware Admission Control in a Virtualized Grid Federation

Preemption-aware Admission Control in a Virtualized Grid Fed...

引用

International Conference on Advanced Information Networking and Applications (AINA)

作者： Mohsen Amini Salehi Bahman Javadi Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia

Many applications in federated Grids have quality-of-service (QoS) constraints such as deadline. Admission control mechanisms assure QoS constraints of the applications by limiting the number of user requests accepted by a resource provider. However, in order to maximize their profit, resource owners are interested in accepting as many requests as possible. In these circumstances, the question that arises is: what is the effective number of requests that can be accepted by a resource provider in a way that the number of accepted external requests is maximized and, at the same time, QoS violations are minimized. In this paper, we answer this question in the context of a virtualized federated Grid environment, where each Grid serves requests from external users along with its local users and requests of local users have preemptive priority over external requests. We apply analytical queuing model to address this question. Additionally, we derive a preemption-aware admission control policy based on the proposed model. Simulation results under realistic working conditions indicate that the proposed policy improves the number of completed external requests (up to 25%). In terms of QoS violations, the 95% confidence interval of the average difference with other policies is between (14.79%, 18.56%).

关键词： Admission control Mathematical model Equations Analytical models Time factors Queueing analysis

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Reliable Provisioning of Spot Instances for Compute-intensive Applications

Reliable Provisioning of Spot Instances for Compute-intensiv...

引用

International Conference on Advanced Information Networking and Applications (AINA)

作者： William Voorsluys Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia

cloud computing providers are now offering their unused resources for leasing in the spot market, which has been considered the first step towards a full-fledged market economy for computational resources. Spot instances are virtual machines (VMs) available at lower prices than their standard on-demand counterparts. These VMs will run for as long as the current price is lower than the maximum bid price users are willing to pay per hour. Spot instances have been increasingly used for executing compute-intensive applications. In spite of an apparent economical advantage, due to an intermittent nature of biddable resources, application execution times may be prolonged or they may not finish at all. This paper proposes a resource allocation strategy that addresses the problem of running compute-intensive jobs on a pool of intermittent virtual machines, while also aiming to run applications in a fast and economical way. To mitigate potential unavailability periods, a multifaceted fault-aware resource provisioning policy is proposed. Our solution employs price and runtime estimation mechanisms, as well as three fault-tolerance techniques, namely check pointing, task duplication and migration. We evaluate our strategies using trace-driven simulations, which take as input real price variation traces, as well as an application trace from the Parallel Workload Archive. Our results demonstrate the effectiveness of executing applications on spot instances, respecting QoS constraints, despite occasional failures.

关键词： Fault tolerance Fault tolerant systems Checkpointing Runtime Virtual machining Computational modeling

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High-Performance cloud computing: A View of Scientific Applications

High-Performance Cloud Computing: A View of Scientific Appli...

引用

International Symposium on Parallel Architectures, Algorithms and Networks (ISPAN)

作者： Christian Vecchiola Suraj Pandey Rajkumar Buyya Cloud computing and Distributed Systems (CLOUDS) Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia

Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a service level agreement (SLA), which ensure the desired quality of service (QoS). Aneka, an enterprise cloud computing solution, harnesses the power of compute resources by relying on private and public clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.

关键词： cloud computing Quality of service Application software Scientific computing Availability Large-scale systems Context-aware services Finance Computer applications Gene expression

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NetworkcloudSim: Modelling Parallel Applications in cloud Simulations

NetworkCloudSim: Modelling Parallel Applications in Cloud Si...

引用

IEEE International Conference on Utility and cloud computing (UCC)

作者： Saurabh Kumar Garg Rajkumar Buyya Cloud Computing and Distributed Systems (CLOUDS) Laboratory Department of Computer Science and Software Engineering University of Melbourne Australia

As interest in adopting cloud computing for various applications is rapidly growing, it is important to understand how these applications and systems will perform when deployed on clouds. Due to the scale and complexity of shared resources, it is often hard to analyze the performance of new scheduling and provisioning algorithms on actual cloud test beds. Therefore, simulation tools are becoming more and more important in the evaluation of the cloud computing model. Simulation tools allow researchers to rapidly evaluate the efficiency, performance and reliability of their new algorithms on a large heterogeneous cloud infrastructure. However, current solutions lack either advanced application models such as message passing applications and workflows or scalable network model of data center. To fill this gap, we have extended a popular cloud simulator (cloudSim) with a scalable network and generalized application model, which allows more accurate evaluation of scheduling and resource provisioning policies to optimize the performance of a cloud infrastructure.

关键词： Computational modeling Data models cloud computing Network topology Bandwidth Processor scheduling Virtual machining

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Intelligent and cloud computing 1

引用

丛书名： Smart Innovation, systems and Technologies

1000年

作者： Debahuti Mishra Srikanta Patnaik Rajkumar Buyya Prasant Mohapatra

ISBN: (数字)9789811562020

ISBN: (纸本)9789811562013;9789811562044

This book features a collection of high-quality research papers presented at the International Conference on Intelligent and cloud computing (ICICC 2019), held at Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, India, on December 20, 2019. Including contributions on system and network design that can support existing and future applications and services, it covers topics such as cloud computing system and network design, optimization for cloud computing, networking, and applications, green cloud system design, cloud storage design and networking, storage security, cloud system models, big data storage, intra-cloud computing, mobile cloud system design, real-time resource reporting and monitoring for cloud management, machine learning, data mining for cloud computing, data-driven methodology and architecture, and networking for machine learning systems.

关键词： Computational Intelligence Machine Learning Mobile and Network Security Big Data Data Engineering

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