3,343 research outputs found

    Designing a fuzzy scheduler for hard real-time systems

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    In hard real-time systems, tasks have to be performed not only correctly, but also in a timely fashion. If timing constraints are not met, there might be severe consequences. Task scheduling is the most important problem in designing a hard real-time system, because the scheduling algorithm ensures that tasks meet their deadlines. However, the inherent nature of uncertainty in dynamic hard real-time systems increases the problems inherent in scheduling. In an effort to alleviate these problems, we have developed a fuzzy scheduler to facilitate searching for a feasible schedule. A set of fuzzy rules are proposed to guide the search. The situation we are trying to address is the performance of the system when no feasible solution can be found, and therefore, certain tasks will not be executed. We wish to limit the number of important tasks that are not scheduled

    Project scheduling under undertainty – survey and research potentials.

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    The vast majority of the research efforts in project scheduling assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. However, in the real world, project activities are subject to considerable uncertainty, that is gradually resolved during project execution. In this survey we review the fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, stochastic GERT network scheduling, fuzzy project scheduling, robust (proactive) scheduling and sensitivity analysis. We discuss the potentials of these approaches for scheduling projects under uncertainty.Management; Project management; Robustness; Scheduling; Stability;

    Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud

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    With the advent of cloud computing, organizations are nowadays able to react rapidly to changing demands for computational resources. Not only individual applications can be hosted on virtual cloud infrastructures, but also complete business processes. This allows the realization of so-called elastic processes, i.e., processes which are carried out using elastic cloud resources. Despite the manifold benefits of elastic processes, there is still a lack of solutions supporting them. In this paper, we identify the state of the art of elastic Business Process Management with a focus on infrastructural challenges. We conceptualize an architecture for an elastic Business Process Management System and discuss existing work on scheduling, resource allocation, monitoring, decentralized coordination, and state management for elastic processes. Furthermore, we present two representative elastic Business Process Management Systems which are intended to counter these challenges. Based on our findings, we identify open issues and outline possible research directions for the realization of elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and P. Hoenisch (2015). Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud. Future Generation Computer Systems, Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00

    Applications of Soft Computing in Mobile and Wireless Communications

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    Soft computing is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or too difficult to model mathematically. Furthermore, the use of conventional modeling techniques demands rigor, precision and certainty, which carry computational cost. On the other hand, soft computing utilizes computation, reasoning and inference to reduce computational cost by exploiting tolerance for imprecision, uncertainty, partial truth and approximation. In addition to computational cost savings, soft computing is an excellent platform for autonomic computing, owing to its roots in artificial intelligence. Wireless communication networks are associated with much uncertainty and imprecision due to a number of stochastic processes such as escalating number of access points, constantly changing propagation channels, sudden variations in network load and random mobility of users. This reality has fuelled numerous applications of soft computing techniques in mobile and wireless communications. This paper reviews various applications of the core soft computing methodologies in mobile and wireless communications

    Scheduling Periodical Multi-Stage Jobs With Fuzziness to Elastic Cloud Resources

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    © 2020 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] We investigate a workflow scheduling problem with stochastic task arrival times and fuzzy task processing times and due dates. The problem is common in many real-time and workflow-based applications, where tasks with fixed stage number and linearly dependency are executed on scalable cloud resources with multiple price options. The challenges lie in proposing effective, stable, and robust algorithms under stochastic and fuzzy tasks. A triangle fuzzy number-based model is formulated. Two metrics are explored: the cost and the degree of satisfaction. An iterated heuristic framework is proposed to periodically schedule tasks, which consists of a task collection and a fuzzy task scheduling phases. Two task collection strategies are presented and two task prioritization strategies are employed. In order to achieve a high satisfaction degree, deadline constraints are defined at both job and task levels. By designing delicate experiments and applying sophisticated statistical techniques, experimental results show that the proposed algorithm is more effective and robust than the two existing methods.This work was supported by the National Key Research and Development Program of China (No. 2017YFB1400800), the National Natural Science Foundation of China (Nos. 61672297, 61872077, and 61832004), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 18KJB520039) and the National Science Foundation for Post-doctoral Scientists of China (Grant No. 2018M640510). Ruben Ruiz was partially supported by the Spanish Ministry of Science, Innovation, and Universities, under the project "OPTEP-Port Terminal Operations Optimization" (No. RTI2018-094940-B-I00) financed with FEDER funds. The authors would like to thank the anonymous reviewers for their valuable feedback on this work.Zhu, J.; Li, X.; Ruiz García, R.; Li, W.; Huang, H.; Zomaya, AY. (2020). Scheduling Periodical Multi-Stage Jobs With Fuzziness to Elastic Cloud Resources. IEEE Transactions on Parallel and Distributed Systems. 31(12):2819-2833. https://doi.org/10.1109/TPDS.2020.3004134S28192833311

    Provendo robustez a escalonadores de workflows sensíveis às incertezas da largura de banda disponível

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    Orientadores: Edmundo Roberto Mauro Madeira, Luiz Fernando BittencourtTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Para que escalonadores de aplicações científicas modeladas como workflows derivem escalonamentos eficientes em nuvens híbridas, é necessário que se forneçam, além da descrição da demanda computacional desses aplicativos, as informações sobre o poder de computação dos recursos disponíveis, especialmente aqueles dados relacionados com a largura de banda disponível. Entretanto, a imprecisão das ferramentas de medição fazem com que as informações da largura de banda disponível fornecida aos escalonadores difiram dos valores reais que deveriam ser considerados para se obter escalonamentos quase ótimos. Escalonadores especialmente projetados para nuvens híbridas simplesmente ignoram a existência de tais imprecisões e terminam produzindo escalonamentos enganosos e de baixo desempenho, o que os tornam sensíveis às informações incertas. A presente Tese introduz um procedimento pró-ativo para fornecer um certo nível de robustez a escalonamentos derivados de escalonadores não projetados para serem robustos frente às incertezas decorrentes do uso de informações imprecisas dadas por ferramentas de medições de rede. Para tornar os escalonamentos sensíveis às incertezas em escalonamentos robustos às essas imprecisões, o procedimento propõe um refinamento (uma deflação) das estimativas da largura de banda antes de serem utilizadas pelo escalonador não robusto. Ao propor o uso de estimativas refinadas da largura de banda disponível, escalonadores inicialmente sensíveis às incertezas passaram a produzir escalonamentos com um certo nível de robustez às essas imprecisões. A eficácia e a eficiência do procedimento proposto são avaliadas através de simulação. Comparam-se, portanto, os escalonamentos gerados por escalonadores que passaram a usar o procedimento proposto com aqueles produzidos pelos mesmos escalonadores mas sem aplicar esse procedimento. Os resultados das simulações mostram que o procedimento proposto é capaz de prover robustez às incertezas da informação da largura de banda a escalonamentos derivados de escalonardes não robustos às tais incertezas. Adicionalmente, esta Tese também propõe um escalonador de aplicações científicas especialmente compostas por um conjunto de workflows. A novidade desse escalonador é que ele é flexível, ou seja, permite o uso de diferentes categorias de funções objetivos. Embora a flexibilidade proposta seja uma novidade no estado da arte, esse escalonador também é sensível às imprecisões da largura de banda. Entretanto, o procedimento mostrou-se capaz de provê-lo de robustez frente às tais incertezas. É mostrado nesta Tese que o procedimento proposto aumentou a eficácia e a eficiência de escalonadores de workflows não robustos projetados para nuvens híbridas, já que eles passaram a produzir escalonamentos com um certo nível de robustez na presença de estimativas incertas da largura de banda disponível. Dessa forma, o procedimento proposto nesta Tese é uma importante ferramenta para aprimorar os escalonadores sensíveis às estimativas incertas da banda disponível especialmente projetados para um ambiente computacional onde esses valores são imprecisos por natureza. Portanto, esta Tese propõe um procedimento que promove melhorias nas execuções de aplicações científicas em nuvens híbridasAbstract: To derive efficient schedules for the tasks of scientific applications modelled as workflows, schedulers need information on the application demands as well as on the resource availability, especially those regarding the available bandwidth. However, the lack of precision of bandwidth estimates provided by monitoring/measurement tools should be considered by the scheduler to achieve near-optimal schedules. Uncertainties of available bandwidth can be a result of imprecise measurement and monitoring network tools and/or their incapacity of estimating in advance the real value of the available bandwidth expected for the application during the scheduling step of the application. Schedulers specially designed for hybrid clouds simply ignore the inaccuracies of the given estimates and end up producing non-robust, low-performance schedules, which makes them sensitive to the uncertainties stemming from using these networking tools. This thesis introduces a proactive procedure to provide a certain level of robustness for schedules derived from schedulers that were not designed to be robust in the face of uncertainties of bandwidth estimates stemming from using unreliable networking tools. To make non-robust schedulers into robust schedulers, the procedure applies a deflation on imprecise bandwidth estimates before being used as input to non-robust schedulers. By proposing the use of refined (deflated) estimates of the available bandwidth, non-robust schedulers initially sensitive to these uncertainties started to produce robust schedules that are insensitive to these inaccuracies. The effectiveness and efficiency of the procedure in providing robustness to non-robust schedulers are evaluated through simulation. Schedules generated by induced-robustness schedulers through the use of the procedure is compared to that of produced by sensitive schedulers. In addition, this thesis also introduces a flexible scheduler for a special case of scientific applications modelled as a set of workflows grouped into ensembles. Although the novelty of this scheduler is the replacement of objective functions according to the user's needs, it is still a non-robust scheduler. However, the procedure was able to provide the necessary robustness for this flexible scheduler be able to produce robust schedules under uncertain bandwidth estimates. It is shown in this thesis that the proposed procedure enhanced the robustness of workflow schedulers designed especially for hybrid clouds as they started to produce robust schedules in the presence of uncertainties stemming from using networking tools. The proposed procedure is an important tool to furnish robustness to non-robust schedulers that are originally designed to work in a computational environment where bandwidth estimates are very likely to vary and cannot be estimated precisely in advance, bringing, therefore, improvements to the executions of scientific applications in hybrid cloudsDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação2012/02778-6FAPES

    Job Shop Planning and Scheduling for Manufacturers with Manual Operations

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    Job shop scheduling systems are widely employed to optimise the efficiency of machine utilisation in the manufacturing industry, by searching the most cost-effective permutation of job operations based on the cost of each operation on each compatible machine and the relations between job operations. Such systems are paralysed when the cost of operations are not predictable led by the involvement of complex manual operations. This paper proposes a new genetic algorithm-based job shop scheduling system by integrating a fuzzy learning and inference sub-system in an effort to address this limitation. In particular, the fuzzy sub-system adaptively estimates the completion time and thus cost of each manual task under different conditions based on a knowledge base which is initialised by domain experts and then constantly updated based on its built-in learning ability and adaptability. The manufacturer of Point of Sale and Point of Purchase products is taken in this paper as an example case for both theoretical discussion and experimental study. The experimental results demonstrate the promising of the proposed system in improving the efficiency of manual manufacturing operations
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