Relevant bibliographies by topics / Cloud Scheduling

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Cloud Scheduling'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Cloud Scheduling"

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Lu, Pingping, Gongxuan Zhang, Zhaomeng Zhu, Xiumin Zhou, Jin Sun, and Junlong Zhou. "A Review of Cost and Makespan-Aware Workflow Scheduling in Clouds." Journal of Circuits, Systems and Computers 28, no. 06 (2019): 1930006. http://dx.doi.org/10.1142/s021812661930006x.

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Scientific workflow is a common model to organize large scientific computations. It borrows the concept of workflow in business activities to manage the complicated processes in scientific computing automatically or semi-automatically. The workflow scheduling, which maps tasks in workflows to parallel computing resources, has been extensively studied over years. In recent years, with the rise of cloud computing as a new large-scale distributed computing model, it is of great significance to study workflow scheduling problem in the cloud. Compared with traditional distributed computing platforms, cloud platforms have unique characteristics such as the self-service resource management model and the pay-as-you-go billing model. Therefore, the workflow scheduling in cloud needs to be reconsidered. When scheduling workflows in clouds, the monetary cost and the makespan of the workflow executions are concerned with both the cloud service providers (CSPs) and the customers. In this paper, we study a series of cost-and-time-aware workflow scheduling algorithms in cloud environments, which aims to provide researchers with a choice of appropriate cloud workflow scheduling approaches in various scenarios. We conducted a broad review of different cloud workflow scheduling algorithms and categorized them based on their optimization objectives and constraints. Also, we discuss the possible future research direction of the clouds workflow scheduling.
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Balashov, Nikita, Alexander Baranov, Sergey Belov, et al. "Advanced Scheduling in IaaS Clouds." EPJ Web of Conferences 214 (2019): 07011. http://dx.doi.org/10.1051/epjconf/201921407011.

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The complexity of modern software libraries and applications makes it hard to predict possible workloads generated by the software that may lead to significant underutilization of hardware in Infrastructure-as-a-Service (IaaS) clouds. In this paper, we give a review of an approach aimed to deal with resource underutilization in cloud environments, including description of a developed software framework and an example algorithm. IaaS clouds following this universal approach can help increase overall cloud resource utilization independently of the variety of cloud applications.
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Loganathan, Shyamala, and Saswati Mukherjee. "Job Scheduling with Efficient Resource Monitoring in Cloud Datacenter." Scientific World Journal 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/983018.

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Cloud computing is an on-demand computing model, which uses virtualization technology to provide cloud resources to users in the form of virtual machines through internet. Being an adaptable technology, cloud computing is an excellent alternative for organizations for forming their own private cloud. Since the resources are limited in these private clouds maximizing the utilization of resources and giving the guaranteed service for the user are the ultimate goal. For that, efficient scheduling is needed. This research reports on an efficient data structure for resource management and resource scheduling technique in a private cloud environment and discusses a cloud model. The proposed scheduling algorithm considers the types of jobs and the resource availability in its scheduling decision. Finally, we conducted simulations using CloudSim and compared our algorithm with other existing methods, like V-MCT and priority scheduling algorithms.
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Khan, Nawsher, Noraziah Ahmad, Tutut Herawan, and Zakira Inayat. "Cloud Computing." International Journal of Cloud Applications and Computing 2, no. 3 (2012): 68–85. http://dx.doi.org/10.4018/ijcac.2012070103.

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Efficiency (in term of time consumption) and effectiveness in resources utilization are the desired quality attributes in cloud services provision. The main purpose of which is to execute jobs optimally, i.e., with minimum average waiting, turnaround and response time by using effective scheduling technique. Replication provides improved availability and scalability; decreases bandwidth use and increases fault tolerance. To speed up access, file can be replicated so a user can access a nearby replica. This paper proposes architecture to convert Globally One Cloud to Locally Many Clouds. By combining replication and scheduling, this architecture improves efficiency and easy accessibility. In the case of failure of one sub cloud or one cloud service, clients can start using another cloud under “failover” techniques. As a result, no one cloud service will go down.
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Shu, Jian, Hemant Jain, and Changyong Liang. "Business Process Driven Trust-Based Task Scheduling." International Journal of Web Services Research 16, no. 3 (2019): 1–28. http://dx.doi.org/10.4018/ijwsr.2019070101.

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The demand for agile and flexible business application systems has sparked interest in using cloud computing technology to respond quickly and effectively to a dynamic business environment. The authors classify the appropriate cloud services as a multi-objectives task scheduling problem in a hybrid cloud service system. In this article, the authors propose a business process (BP) driven task scheduling system that supports multiple clouds, including private ones. A trust-based non-dominated sorting genetic algorithm (NSGA2) is developed to solve the multi-objective task scheduling problem. By sorting populations into different hierarchies based on the ordering of Pareto dominance, they identify a Pareto-optimal multi-dimensional frontier that permits managers to reconcile conflicting objectives when scheduling tasks on cloud resources. The authors illustrate the usability and effectiveness of their approach by applying it to a case study conducting simulated experiments.
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Chu, Junfei, Jie Wu, Qingyuan Zhu, and Jiasen Sun. "Resource scheduling in a private cloud environment: an efficiency priority perspective." Kybernetes 45, no. 10 (2016): 1524–41. http://dx.doi.org/10.1108/k-04-2015-0108.

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Purpose Resource scheduling is the study of how to effectively measure, evaluate, analyze, and dispatch resources in order to meet the demands of corresponding tasks. Aiming at the problem of resource scheduling in the private cloud environment, the purpose of this paper is to propose a resource scheduling approach from an efficiency priority point of view. Design/methodology/approach To measure the computational efficiencies for the resource nodes in a private cloud environment, the data envelopment analysis (DEA) approach is incorporated and a suitable DEA model is proposed. Then, based on the efficiency scores calculated by the proposed DEA model for the resource nodes, the 0-1 programming technique is introduced to build a simple resource scheduling model. Findings The proposed DEA model not only has the ability of ranking all the decision-making units into different positions but also can handle non-discretionary inputs and undesirable outputs when evaluating the resource nodes. Furthermore, the resource scheduling model can generate for the calculation tasks an optimal resource scheduling scheme that has the highest total computational efficiency. Research limitations/implications The proposed method may also be used in studies of resource scheduling studies in the environments of public clouds and hybrid clouds. Practical implications The proposed approach can achieve the goal of resource scheduling in private cloud computing platforms by attaining the highest total computational efficiency, which is very significant in practice. Originality/value This paper uses an efficiency priority point of view to solve the problem of resource scheduling in private cloud environments.
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Yu, Jiaohui. "Qualitative Simulation Algorithm for Resource Scheduling in Enterprise Management Cloud Mode." Complexity 2021 (February 23, 2021): 1–12. http://dx.doi.org/10.1155/2021/6676908.

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Aiming at the problem of resource scheduling optimization in enterprise management cloud mode, a customizable fuzzy clustering cloud resource scheduling algorithm based on trust sensitivity is proposed. Firstly, on the one hand, a fuzzy clustering method is used to divide cloud resource scheduling into two aspects: cloud user resource scheduling and cloud task resource scheduling. On the other hand, a trust-sensitive mechanism is introduced into cloud task scheduling to prevent malicious node attacks or dishonest recommendation from node providers. At the same time, in the cloud task scheduling, cloud resources are divided according to the comprehensive performance of resources, and the trust sensitivity coefficient of each type of task resources is calculated. Then, according to the trust sensitivity coefficient, the matching cloud tasks are selected for users. Through the comparison of simulation experiments, the customized fuzzy clustering cloud resource scheduling algorithm proposed in this paper reduces the user’s cost of selecting cloud service provider in the cloud resource scheduling. It not only embodies the principle of cloud resource allocation on demand but also can give full play to the advantages of cloud resources and improve the throughput of the whole cloud system and the satisfaction of cloud users.
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Gupta, Punit, and Deepika Agrawal. "Trusted Cloud Platform for Cloud Infrastructure." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 8 (2013): 1884–91. http://dx.doi.org/10.24297/ijct.v10i8.1473.

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Reliability and trust Models are used to enhance secure , reliable scheduling , load balancing and QoS in cloud and Distributed environment. Trust models that are being used in Distributed and Grid environment, does not qualify cloud computing environment requirements. Since the parameters that have being taken into consideration in these trust models, does not fit in the cloud Infrastructure As A Service, a suitable trust model is proposed based on the existing model that is suitable for trust value management for the cloud IaaS parameters. Based on the above achieved trust values, trust based scheduling and load balancing is done for better allocation of resources and enhancing the QOS of services been provided to the users. In this paper, an trust based cloud computing framework is proposed using trust model ,trust based scheduling and load balancing algorithms. Here we describe the design and development of trusted Cloud service model for cloud Infrastructure as a service (IaaS) known as VimCloud .VimCloud an open source cloud computing framework that implements the tusted Cloud Service Model and trust based scheduling and load balancing algorithm . However one of the major issues in cloud IaaS is to ensure reliability and security or used data and computation. Trusted cloud service model ensures that user virual machine executes only on trusted cloud node, whose integrity and reliability is known in term of trust value . VimCloud shown practical in term of performace which is better then existing models.
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Chaudhary, Divya, and Bijendra Kumar. "Cloudy GSA for load scheduling in cloud computing." Applied Soft Computing 71 (October 2018): 861–71. http://dx.doi.org/10.1016/j.asoc.2018.07.046.

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Chunlin, Li, and Li LaYuan. "Hybrid Cloud Scheduling Method for Cloud Bursting." Fundamenta Informaticae 138, no. 4 (2015): 435–55. http://dx.doi.org/10.3233/fi-2015-1220.

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Dissertations / Theses on the topic "Cloud Scheduling"

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Sotiriadis, Stelios. "The inter-cloud meta-scheduling." Thesis, University of Derby, 2013. http://hdl.handle.net/10545/299501.

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Inter-cloud is a recently emerging approach that expands cloud elasticity. By facilitating an adaptable setting, it purposes at the realization of a scalable resource provisioning that enables a diversity of cloud user requirements to be handled efficiently. This study’s contribution is in the inter-cloud performance optimization of job executions using metascheduling concepts. This includes the development of the inter-cloud meta-scheduling (ICMS) framework, the ICMS optimal schemes and the SimIC toolkit. The ICMS model is an architectural strategy for managing and scheduling user services in virtualized dynamically inter-linked clouds. This is achieved by the development of a model that includes a set of algorithms, namely the Service-Request, Service-Distribution, Service-Availability and Service-Allocation algorithms. These along with resource management optimal schemes offer the novel functionalities of the ICMS where the message exchanging implements the job distributions method, the VM deployment offers the VM management features and the local resource management system details the management of the local cloud schedulers. The generated system offers great flexibility by facilitating a lightweight resource management methodology while at the same time handling the heterogeneity of different clouds through advanced service level agreement coordination. Experimental results are productive as the proposed ICMS model achieves enhancement of the performance of service distribution for a variety of criteria such as service execution times, makespan, turnaround times, utilization levels and energy consumption rates for various inter-cloud entities, e.g. users, hosts and VMs. For example, ICMS optimizes the performance of a non-meta-brokering inter-cloud by 3%, while ICMS with full optimal schemes achieves 9% optimization for the same configurations. The whole experimental platform is implemented into the inter-cloud Simulation toolkit (SimIC) developed by the author, which is a discrete event simulation framework.
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Wan, Ziqi. "Scheduling Policies for Cloud Computing." Master's thesis, Temple University Libraries, 2015. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/328227.

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Computer and Information Science<br>M.S.<br>Cloud computing focuses on maximizing the effectiveness of the shared resources. Cloud resources are usually not only shared by multiple users but are also dynamically reallocated per demand. This can work for allocating resources to users. This leads to task scheduling as a core and challenging issue in cloud computing. This thesis gives different scheduling strategies and algorithms in cloud computing. For a common cloud user, there is a great potential to boost the performance of mobile devices by offloading computation-intensive parts of mobile applications to the cloud. However, this potential is hindered by a gap between how individual mobile devices demand computational resources and how cloud providers offer them. In this thesis, we present the design of utility-based uploads sharing strategy in cloud scenarios, which bridges the above gap through providing computation offloading as a service to mobile devices. Our scheme efficiently manages cloud resources for offloading requests to improve offloading performances of mobile devices, as well as to reduce the monetary cost per request of the provider. However, from the viewpoint of data centers, resource limitations in both bandwidth and computing triggers a variety of resource management problems. In this thesis, we discuss the tradeoff between locality and load balancing, along with the multi-layer topology of data centers. After that, we investigate the interrelationship between the time cost and the virtual machine rent cost, and formalize it as the parallel speedup pattern. We then design several algorithms by adopting the idea of minimizing the utility cost. Furthermore, we focus on the detail of MapReduce framework in Cloud. For different MapReduce phases, there are different resource requirements. We propose a new scheduling algorithm based on the idea of combining map shuffle pairs, which has better performance than the popular min-max time first algorithm in minimizing the average makespan of tasks in the job matrix. Directions for future research mainly focus on the large scale implementation of our proposed solution. There are a wide variety of open questions remaining with respect to the design of algorithms to minimize response time. Further, it is interesting and important to understand how to schedule in order to minimize other performance metrics.<br>Temple University--Theses
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Clegg, Mathew. "Scheduling Network PerformanceMonitoring in The Cloud." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-328623.

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New trends in the market, adapted to service oriented consumption models, haveunfolded new opportunities in how we monitor network performance. This thesis,introduces a new containerized, decentralized and concurrent scheduler for activenetwork performance monitoring called Controlled Priority Scheduling (CPS). Thescheduler is implemented to suit the container monitoring platform, ConMon. Thescheduler is implemented to run inside distributed containers, where the purpose isto deploy the scheduling container on the same host as the running application.Performing the monitoring in such way gives a better understanding of the networkperformance an application can utilize, compared to the capacity the network canoffer. The CPS scheduler showed an improved monitoring time granularity whencompared too other distributed and decentralized schedulers. In addition, CPSmanages to perform a consistent, near-cyclic monitoring pattern, over a dynamicallyadaptable monitoring cluster, without causing any monitoring conflicts.
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Liu, Ke. "Scheduling algorithms for instance-intensive cloud workflows." Swinburne Research Bank, 2009. http://hdl.handle.net/1959.3/68752.

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Thesis (PhD) - Swinburne University of Technology, Faculty of Engineering and Industrial Sciences, Centre for Complex Software Systems and Services, 2009.<br>A thesis submitted to CS3 - Centre for Complex Software Systems and Services, Faculty of Engineering and Industrial Sciences, Swinburne University of Technology for the degree of Doctor of Philosophy, 2009. Typescript. "June 2009". Bibliography: p. 122-135.
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Cao, Fei. "Efficient Scientific Workflow Scheduling in Cloud Environment." OpenSIUC, 2014. https://opensiuc.lib.siu.edu/dissertations/802.

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Cloud computing enables the delivery of remote computing, software and storage services through web browsers following pay-as-you-go model. In addition to successful commercial applications, many research efforts including DOE Magellan Cloud project focus on discovering the opportunities and challenges arising from the computing and data-intensive scientific applications that are not well addressed by the current supercomputers, Linux clusters and Grid technologies. The elastic resource provision, noninterfering resource sharing and flexible customized configuration provided by the Cloud infrastructure has shed light on efficient execution of many scientific applications modeled as Directed Acyclic Graph (DAG) structured workflows to enforce the intricate dependency among a large number of different processing tasks. Meanwhile, the Cloud environment poses various challenges. Cloud providers and Cloud users pursue different goals. Providers aim to maximize profit by achieving higher resource utilization and users want to minimize expenses while meeting their performance requirements. Moreover, due to the expanding Cloud services and emerging newer technologies, the ever-increasing heterogeneity of the Cloud environment complicates the challenges for both parties. In this thesis, we address the workflow scheduling problem from different applications and various objectives. For batch applications, due to the increasing deployment of many data centers and computer servers around the globe escalated by the higher electricity price, the energy cost on running the computing, communication and cooling together with the amount of CO2 emissions have skyrocketed. In order to maintain sustainable Cloud computing facing with ever-increasing problem complexity and big data size in the next decades, we design and develop energy-aware scientific workflow scheduling algorithm to minimize energy consumption and CO2 emission while still satisfying certain Quality of Service (QoS) such as response time specified in Service Level Agreement (SLA). Furthermore, the underlying Cloud hardware/Virtual Machine (VM) resource availability is time-dependent because of the dual operation modes namely on-demand and reservation instances at various Cloud data centers. We also apply techniques such as Dynamic Voltage and Frequency Scaling (DVFS) and DNS scheme to further reduce energy consumption within acceptable performance bounds. Our multiple-step resource provision and allocation algorithm achieves the response time requirement in the step of forward task scheduling and minimizes the VM overhead for reduced energy consumption and higher resource utilization rate in the backward task scheduling step. We also evaluate the candidacy of multiple data centers from the energy and performance efficiency perspectives as different data centers have various energy and cost related parameters. For streaming applications, we formulate scheduling problems with two different objectives, namely one is to maximize the throughput under a budget constraint while another is to minimize execution cost under a minimum throughput constraint. Two different algorithms named as Budget constrained RATE (B-RATE) and Budget constrained SWAP (B-SWAP) are designed under the first objective; Another two algorithms, namely Throughput constrained RATE (TP-RATE) and Throughput constrained SWAP (TP-SWAP) are developed under the second objective.
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Zheng, Yousi. "Scheduling and Design in Cloud Computing Systems." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1429354074.

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Alsughayyir, Aeshah Yahya. "Energy-aware scheduling in decentralised multi-cloud systems." Thesis, University of Leicester, 2018. http://hdl.handle.net/2381/42407.

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Cloud computing is an emerging Internet-based computing paradigm that aims to provide many on-demand services, requested nowadays by almost all online users. Although it greatly utilises virtualised environments for applications to be executed efficiently in low-cost hosting, it has turned energy wasting and overconsumption issues into major concerns. Many studies have projected that the energy consumption of cloud data-centres would grow significantly to reach 35% of the total energy consumed worldwide, threatening to further boost the world's energy crisis. Moreover, cloud infrastructure is built on a great amount of server equipment, including high performance computing (HPC), and the servers are naturally prone to failures. In this thesis, we study practically as well as theoretically the feasibility of optimising energy consumption in multi-cloud systems. We explore a deadline-based scheduling problem of executing HPC-applications by a heterogeneous set of clouds that are geographically distributed worldwide. We assume that these clouds participate in a federated approach. The practical part of the thesis has focused on combining two energy dimensions while scheduling HPC-applications (i.e., energy consumed for execution and data transmission). It has considered simultaneously minimising application rejections and deadline violations, to support resource reliability, with energy optimisation. In the theoretical part, we have presented the first online algorithms for the non-pre-emptive scheduling of jobs with agreeable deadlines on heterogeneous parallel processors. Through our developed simulation and experimental analysis using real parallel workloads from large-scale systems, the results evidenced that it is possible to reduce a considerable amount of energy while carefully scheduling cloud applications over a multi-cloud system. We have shown that our practical approaches provide promising energy savings with acceptable level of resource reliability. We believe that our scheduling approaches have particular importance in relation with the main aim of green cloud computing for the necessity of increasing energy efficiency.
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Gonzalez, Nelson Mimura. "MPSF: cloud scheduling framework for distributed workflow execution." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-03032017-083914/.

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Cloud computing represents a distributed computing paradigm that gained notoriety due to its properties related to on-demand elastic and dynamic resource provisioning. These characteristics are highly desirable for the execution of workflows, in particular scientific workflows that required a great amount of computing resources and that handle large-scale data. One of the main questions in this sense is how to manage resources of one or more cloud infrastructures to execute workflows while optimizing resource utilization and minimizing the total duration of the execution of tasks (makespan). The more complex the infrastructure and the tasks to be executed are, the higher the risk of incorrectly estimating the amount of resources to be assigned to each task, leading to both performance and monetary costs. Scenarios which are inherently more complex, such as hybrid and multiclouds, rarely are considered by existing resource management solutions. Moreover, a thorough research of relevant related work revealed that most of the solutions do not address data-intensive workflows, a characteristic that is increasingly evident for modern scientific workflows. In this sense, this proposal presents MPSF, the Multiphase Proactive Scheduling Framework, a cloud resource management solution based on multiple scheduling phases that continuously assess the system to optimize resource utilization and task distribution. MPSF defines models to describe and characterize workflows and resources. MPSF also defines performance and reliability models to improve load distribution among nodes and to mitigate the effects of performance fluctuations and potential failures that might occur in the system. Finally, MPSF defines a framework and an architecture to integrate all these components and deliver a solution that can be implemented and tested in real applications. Experimental results show that MPSF is able to predict with much better accuracy the duration of workflows and workflow phases, as well as providing performance gains compared to greedy approaches.<br>A computação em nuvem representa um paradigma de computação distribuída que ganhoudestaque devido a aspectos relacionados à obtenção de recursos sob demanda de modo elástico e dinâmico. Estas características são consideravelmente desejáveis para a execução de tarefas relacionadas a fluxos de trabalho científicos, que exigem grande quantidade de recursos computacionais e grande fluxo de dados. Uma das principais questões neste sentido é como gerenciar os recursos de uma ou mais infraestruturas de nuvem para execução de fluxos de trabalho de modo a otimizar a utilização destes recursos e minimizar o tempo total de execução das tarefas. Quanto mais complexa a infraestrutura e as tarefas a serem executadas, maior o risco de estimar incorretamente a quantidade de recursos destinada para cada tarefa, levando a prejuízos não só em termos de tempo de execução como também financeiros. Cenários inerentemente mais complexos como nuvens híbridas e múltiplas nuvens raramente são considerados em soluções existentes de gerenciamento de recursos para nuvens. Além destes fatores, a maioria das soluções não oferece mecanismos claros para tratar de fluxos de trabalho com alta intensidade de dados, característica cada vez mais proeminente em fluxos de trabalho moderno. Neste sentido, esta proposta apresenta MPSF, uma solução de gerenciamento de recursos baseada em múltiplas fases de gerenciamento baseadas em mecanismos dinâmicos de alocação de tarefas. MPSF define modelos para descrever e caracterizar fluxos de trabalho e recursos de modo a suportar cenários simples e complexos, como nuvens híbridas e nuvens integradas. MPSF também define modelos de desempenho e confiabilidade para melhor distribuir a carga e para combater os efeitos de possíveis falhas que possam ocorrer no sistema. Por fim, MPSF define um arcabouço e um arquitetura que integra todos estes componentes de modo a definir uma solução que possa ser implementada e utilizada em cenários reais. Testes experimentais indicam que MPSF não só é capaz de prever com maior precisão a duração da execução de tarefas, como também consegue otimizar a execução das mesmas, especialmente para tarefas que demandam alto poder computacional e alta quantidade de dados.
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Liu, Shuo. "Delay-Sensitive Service Request Scheduling for Cloud Computing." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1619.

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Cloud computing realizes the long-held dream of converting computing capability into a type of utility. It has the potential to fundamentally change the landscape of the IT industry and our way of life. However, as cloud computing expanding substantially in both scale and scope, ensuring its sustainable growth is a critical problem. Service providers have long been suffering from high operational costs. Especially the costs associated with the skyrocketing power consumption of large data centers. In the meantime, while efficient power/energy utilization is indispensable for the sustainable growth of cloud computing, service providers must also satisfy a user's quality of service (QoS) requirements. This problem becomes even more challenging considering the increasingly stringent power/energy and QoS constraints, as well as other factors such as the highly dynamic, heterogeneous, and distributed nature of the computing infrastructures, etc. In this dissertation, we study the problem of delay-sensitive cloud service scheduling for the sustainable development of cloud computing. We first focus our research on the development of scheduling methods for delay-sensitive cloud services on a single server with the goal of maximizing a service provider's profit. We then extend our study to scheduling cloud services in distributed environments. In particular, we develop a queue-based model and derive efficient request dispatching and processing decisions in a multi-electricity-market environment to improve the profits for service providers. We next study a problem of multi-tier service scheduling. By carefully assigning sub deadlines to the service tiers, our approach can significantly improve resource usage efficiencies with statistically guaranteed QoS. Finally, we study the power conscious resource provision problem for service requests with different QoS requirements. By properly sharing computing resources among different requests, our method statistically guarantees all QoS requirements with a minimized number of powered-on servers and thus the power consumptions. The significance of our research is that it is one part of the integrated effort from both industry and academia to ensure the sustainable growth of cloud computing as it continues to evolve and change our society profoundly.
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Alshahrani, Reem Abdullah. "Theory and Practice in Cloud Datacenters with Distributed Schedulers." Kent State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=kent1564593436089183.

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Books on the topic "Cloud Scheduling"

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Tan, Rong Kun Jason, John A. Leong, and Amandeep S. Sidhu. Optimized Cloud Based Scheduling. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73214-5.

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1979-, Pan Jie, and Teng Fei, eds. Cloud-computing: Data-intensive computing and scheduling. CRC Press, 2012.

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Sokolov, Boris, Dmitry Ivanov, and Alexandre Dolgui, eds. Scheduling in Industry 4.0 and Cloud Manufacturing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43177-8.

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Youn, Chan-Hyun, Min Chen, and Patrizio Dazzi. Cloud Broker and Cloudlet for Workflow Scheduling. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5071-8.

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Pop, Florin, and Maria Potop-Butucaru, eds. Adaptive Resource Management and Scheduling for Cloud Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13464-2.

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Pop, Florin, and Maria Potop-Butucaru, eds. Adaptive Resource Management and Scheduling for Cloud Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28448-4.

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Kousalya, G., P. Balakrishnan, and C. Pethuru Raj. Automated Workflow Scheduling in Self-Adaptive Clouds. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56982-6.

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Sidhu, Amandeep S., Rong Kun Jason Tan, and John A. Leong. Optimized Cloud Based Scheduling. Springer, 2018.

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Sidhu, Amandeep S., Rong Kun Jason Tan, and John A. Leong. Optimized Cloud Based Scheduling. Springer, 2019.

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Optimized Cloud Resource Management and Scheduling. Elsevier, 2015. http://dx.doi.org/10.1016/c2013-0-13415-0.

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Book chapters on the topic "Cloud Scheduling"

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Kohne, Andreas. "Cloud Scheduling." In Cloud-Föderationen. Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-20973-5_5.

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Kohne, Andreas. "DC-Scheduling." In Cloud-Föderationen. Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-20973-5_10.

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Kohne, Andreas. "CSP-Scheduling." In Cloud-Föderationen. Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-20973-5_11.

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Kohne, Andreas. "Föderations-Scheduling." In Cloud-Föderationen. Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-20973-5_12.

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Li, Zhenhua, Yafei Dai, Guihai Chen, and Yunhao Liu. "Cloud Bandwidth Scheduling." In Content Distribution for Mobile Internet: A Cloud-based Approach. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1463-5_7.

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Kanemitsu, Hidehiro, Masaki Hanada, and Hidenori Nakazato. "Multiple Workflow Scheduling with Offloading Tasks to Edge Cloud." In Cloud Computing – CLOUD 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23502-4_4.

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Di Martino, Beniamino, Giuseppina Cretella, and Antonio Esposito. "Cloud Services Composition Through Cloud Patterns." In Adaptive Resource Management and Scheduling for Cloud Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28448-4_10.

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Bittencourt, Luiz F., Edmundo R. M. Madeira, and Nelson L. S. da Fonseca. "Resource Management and Scheduling." In Cloud Services, Networking, and Management. John Wiley & Sons, Inc, 2015. http://dx.doi.org/10.1002/9781119042655.ch10.

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He, Libo, Zhenping Qiang, Lin Liu, Wei Zhou, and Shaowen Yao. "A Conflict Prevention Scheduling Strategy for Shared-State Scheduling in Large Scale Cluster." In Cloud Computing and Security. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48671-0_22.

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Dong, Changchun, and Liang Zhou. "Optimization Algorithm for Freight Car Transportation Scheduling Optimization Based on Process Scheduling Optimization." In Cloud Computing and Security. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00018-9_46.

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Conference papers on the topic "Cloud Scheduling"

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Zhou, Longfei, Lin Zhang, and Lei Ren. "An Individual Requirements-Oriented Service Scheduling Method in Cloud Manufacturing." In ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/msec2017-2817.

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Cloud manufacturing is a novel service-oriented networked manufacturing paradigm for the manufacturing industry. Through aggregating distributed manufacturing resources from different enterprises and transforming them into services, cloud manufacturing is able to provide on-demand manufacturing services to customers. Scheduling, including resource scheduling and task scheduling, is a critical instrument for achieving on-demand service provisioning, and also an important research issue in cloud manufacturing. In the process of service scheduling in cloud manufacturing, the manufacturing services are firstly matched according to the service demander’s functional requirements and service availability to form the candidate service sets. And then the optimized service scheduling scheme is generated according to the service demander’s non-functional requirements. The individual requirements of service demanders are analyzed from aspects of functional and non-functional requirements in this paper. On this basis, the scheduling process for individual requirements in cloud manufacturing system is studied and a cloud manufacturing service scheduling method is proposed. This work can provide support and foundation for the related research of task planning and scheduling in cloud manufacturing system. Finally, a case study is given to verify the proposed service scheduling method.
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Dong, Hang, Boshi Wang, Bo Qiao, et al. "Predictive Job Scheduling under Uncertain Constraints in Cloud Computing." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/499.

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Capacity management has always been a great challenge for cloud platforms due to massive, heterogeneous on-demand instances running at different times. To better plan the capacity for the whole platform, a class of cloud computing instances have been released to collect computing demands beforehand. To use such instances, users are allowed to submit jobs to run for a pre-specified uninterrupted duration in a flexible range of time in the future with a discount compared to the normal on-demand instances. Proactively scheduling those pre-collected job requests considering the capacity status over the platform can greatly help balance the computing workloads along time. In this work, we formulate the scheduling problem for these pre-collected job requests under uncertain available capacity as a Prediction + Optimization problem with uncertainty in constraints, and propose an effective algorithm called Controlling under Uncertain Constraints (CUC), where the predicted capacity guides the optimization of job scheduling and job scheduling results are leveraged to improve the prediction of capacity through Bayesian optimization. The proposed formulation and solution are commonly applicable for proactively scheduling problems in cloud computing. Our extensive experiments on three public, industrial datasets shows that CUC has great potential for supporting high reliability in cloud platforms.
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Kaur, Gagandeep, and Anurag Sharma. "Task Scheduling Algorithms for Cloud Computing: A Critical Review and Open Research Challenges." In International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.50.

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Cloud computing is the most preferred platform to access resources remotely. The benefit of cloud computing over traditional computing techniques is that it provides on-demand services and serves millions of users at the same time. However, scheduling the tasks of users is quite crucial in cloud computing. To overcome this challenge, various task scheduling algorithms have been designed for cloud computing. In this paper, we have done a critical review of various traditional and metaheuristic algorithms based on task scheduling algorithms. The critical review shows that the metaheuristic algorithms are better than traditional algorithms to find the optimal scheduling of the task. Besides that, based on the study, we have defined the open research challenges of the metaheuristic algorithms that help other authors to contribute their research in this field.
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Lin, Cui, and Shiyong Lu. "Scheduling Scientific Workflows Elastically for Cloud Computing." In 2011 IEEE 4th International Conference on Cloud Computing (CLOUD). IEEE, 2011. http://dx.doi.org/10.1109/cloud.2011.110.

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Wei, Haoran, and Fanchao Meng. "A Novel Scheduling Mechanism For Hybrid Cloud Systems." In 2016 IEEE 9th International Conference on Cloud Computing (CLOUD). IEEE, 2016. http://dx.doi.org/10.1109/cloud.2016.0102.

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Katsalis, Kostas, Thanasis G. Papaioannou, Navid Nikaein, and Leandros Tassiulas. "SLA-Driven VM Scheduling in Mobile Edge Computing." In 2016 IEEE 9th International Conference on Cloud Computing (CLOUD). IEEE, 2016. http://dx.doi.org/10.1109/cloud.2016.0104.

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Kijak, Joanna, Piotr Martyna, Maciej Pawlik, Bartosz Balis, and Maciej Malawski. "Challenges for Scheduling Scientific Workflows on Cloud Functions." In 2018 IEEE 11th International Conference on Cloud Computing (CLOUD). IEEE, 2018. http://dx.doi.org/10.1109/cloud.2018.00065.

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He, Ting, Shiyao Chen, Hyoil Kim, Lang Tong, and Kang-Won Lee. "Scheduling Parallel Tasks onto Opportunistically Available Cloud Resources." In 2012 IEEE 5th International Conference on Cloud Computing (CLOUD). IEEE, 2012. http://dx.doi.org/10.1109/cloud.2012.15.

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Yihong Gao, Huadong Ma, Haitao Zhang, Xiangqi Kong, and Wangyang Wei. "Concurrency Optimized Task Scheduling for Workflows in Cloud." In 2013 IEEE 6th International Conference on Cloud Computing (CLOUD). IEEE, 2013. http://dx.doi.org/10.1109/cloud.2013.50.

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Chen, Chien Hung, Jenn Wei Lin, and Sy Yen Kuo. "Deadline-Constrained MapReduce Scheduling Based on Graph Modelling." In 2014 IEEE 7th International Conference on Cloud Computing (CLOUD). IEEE, 2014. http://dx.doi.org/10.1109/cloud.2014.63.

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