43,610 research outputs found

    Scheduling of operating system processes

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    Proces predstavlja računalni program koji se izvršava. Kako ne bi došlo do zastoja između procesa, koriste se algoritmi raspoređivanja kojima se izrađuje plan raspoređivanja procesa na CPU-u. Algoritmi raspoređivanja mogu se podijeliti u 3 kategorije: raspoređivanje u batch sustavim, raspoređivanje u interaktivnim sustavima te raspoređivanje u sustavima stvarnog vremena. U ovom radu obrađeni su algoritmi koji spadaju u batch sustave i interaktivne sustave, te su implementirani i testirani isti. Također, kratko su spomenuti i sustavi stvarnog vremena.Process represents a computer program that is being executed. In order to avoid interruptions between the processes, the scheduling algorithms used to create the CPU scheduling plan are used. The scheduling algorithms can be divided into 3 categories: scheduling in batch systems, scheduling in interactive systems, and scheduling in real-time systems This paper deals with algorithms that fall into categories of scheduling in batch systems and scheduling in interactive systems and the algorithms of the same categories have been implemented. Also, the real-time systems are briefly mentioned

    Metascheduling of HPC Jobs in Day-Ahead Electricity Markets

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    High performance grid computing is a key enabler of large scale collaborative computational science. With the promise of exascale computing, high performance grid systems are expected to incur electricity bills that grow super-linearly over time. In order to achieve cost effectiveness in these systems, it is essential for the scheduling algorithms to exploit electricity price variations, both in space and time, that are prevalent in the dynamic electricity price markets. In this paper, we present a metascheduling algorithm to optimize the placement of jobs in a compute grid which consumes electricity from the day-ahead wholesale market. We formulate the scheduling problem as a Minimum Cost Maximum Flow problem and leverage queue waiting time and electricity price predictions to accurately estimate the cost of job execution at a system. Using trace based simulation with real and synthetic workload traces, and real electricity price data sets, we demonstrate our approach on two currently operational grids, XSEDE and NorduGrid. Our experimental setup collectively constitute more than 433K processors spread across 58 compute systems in 17 geographically distributed locations. Experiments show that our approach simultaneously optimizes the total electricity cost and the average response time of the grid, without being unfair to users of the local batch systems.Comment: Appears in IEEE Transactions on Parallel and Distributed System

    Communication and control in small batch part manufacturing

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    This paper reports on the development of a real-time control network as an integrated part of a shop floor control system for small batch part manufacturing. The shop floor control system is called the production control system (PCS). The PCS aims at an improved control of small batch part manufacturing systems, enabling both a more flexible use of resources and a decrease in the economical batch size. For this, the PCS integrates various control functions such as scheduling, dispatching, workstation control and monitoring, whilst being connected on-line to the production equipment on the shop floor. The PCS can be applied irrespective of the level of automation on the shop floor. The control network is an essential part of the PCS, as it provides a real-time connection between the different modules (computers) of the PCS, which are geographically distributed over the shop floor. An overview of the requirements of such a control network is given. The description of the design includes the services developed, the protocols used and the physical layout of the network. A prototype of the PCS, including the control network, has been installed and tested in a pilot plant. The control network has proven that it can supply a manufacturing environment, consisting of equipment from different vendors with different levels of automation, with a reliable, low cost, real-time communication facility

    Machine Learning and Inverse Optimization for Estimation of Weighting Factors in Multi-Objective Production Scheduling Problems

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    In recent years, scheduling optimization has been utilized in production systems. To construct a suitable mathematical model of a production scheduling problem, modeling techniques that can automatically select an appropriate objective function from historical data are necessary. This paper presents two methods to estimate weighting factors of the objective function in the scheduling problem from historical data, given the information of operation time and setup costs. We propose a machine learning-based method, and an inverse optimization-based method using the input/output data of the scheduling problems when the weighting factors of the objective function are unknown. These two methods are applied to a multi-objective parallel machine scheduling problem and a real-world chemical batch plant scheduling problem. The results of the estimation accuracy evaluation show that the proposed methods for estimating the weighting factors of the objective function are effective

    An analysis of the factors of conversion from a batch-processing, tape-oriented retrieval system to an on-line, real-time system

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    The factors for converting a batch-processing, tape-oriented document reference system to an-on-line, real-time system are discussed. The factors analyzed are file structures, file addressing, file maintenance, communication, scheduling, and modular programming structures. The levels of design for the individual factors are illustrated in order to show the complexities of conversion to an on-line, real-time system. It is concluded that system conversion from a batch-processing, tape-oriented system to an on-line, real-time system requires a total systems redesign, not a modification of the existing system. A vector model for system conversion is presented. The vector quantities of the model represent the factor levels and their probable costs-for implementation. It is concluded from the analysis and the vector model that interrelations between the factor levels must be followed for an operationally complete system conversion

    Implementation of an Industry 4.0 system to optimally manage chemical plant operation

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    The evolution of the process industry in the direction of automation and digitalization is nowadays a consolidated phenomenon. In this direction, Industry 4.0 paradigms are leading many industrial companies to significantly update their facilities. This paper presents a scheduling algorithm that takes the role of a real-time optimization (RTO) element in a larger project framework where the various network components are aimed to be all highly interconnected. The proposed methodology is applied to an Italian chemical industrial site, in order to best manage the production rates of the various products and the sales plan for the different clients. Numerous plants and processes are considered into the model: batch and continuous production lines, saleable and non-storable products. Concepts of linear optimization and batch operation scheduling are used in the algorithm construction. This whole structure lays the foundation for a full integration between different elements of the facility, that is, the control systems and the selling department

    Using hierarchical scheduling to support soft real-time applications in general-purpose operating systems

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    Journal ArticleThe CPU schedulers in general-purpose operating systems are designed to provide fast response time for interactive applications and high throughput for batch applications. The heuristics used to achieve these goals do not lend themselves to scheduling real-time applications, nor do they meet other scheduling requirements such as coordinating scheduling across several processors or machines, or enforcing isolation between applications, users, and administrative domains. Extending the scheduling subsystems of general-purpose operating systems in an ad hoc manner is time consuming and requires considerable expertise as well as source code to the operating system. Furthermore, once extended, the new scheduler may be as inflexible as the original. The thesis of this dissertation is that extending a general-purpose operating system with a general, heterogeneous scheduling hierarchy is feasible and useful. A hierarchy of schedulers generalizes the role of CPU schedulers by allowing them to schedule other schedulers in addition to scheduling threads. A general, heterogeneous scheduling hierarchy is one that allows arbitrary (or nearly arbitrary) scheduling algorithms throughout the hierarchy. In contrast, most of the previous work on hierarchical scheduling has imposed restrictions on the schedulers used in part or all of the hierarchy. This dissertation describes the Hierarchical Loadable Scheduler (HLS) architecture, which permits schedulers to be dynamically composed in the kernel of a general-purpose operating system. The most important characteristics of HLS, and the ones that distinguish it from previous work, are that it has demonstrated that a hierarchy of nearly arbitrary schedulers can be efficiently implemented in a general-purpose operating system, and that the behavior of a hierarchy of soft real-time schedulers can be reasoned about in order to provide guaranteed scheduling behavior to application threads. The flexibility afforded by HLS permits scheduling behavior to be tailored to meet complex requirements without encumbering users who have modest requirements with the performance and administrative costs of a complex scheduler. Contributions of this dissertation include the following. (1) The design, prototype implementation, and performance evaluation of HLS in Windows 2000. (2) A system of guarantees for scheduler composition that permits reasoning about the scheduling behavior of a hierarchy of soft real-time schedulers. Guarantees assure users that application requirements can be met throughout the lifetime of the application, and also provide application developers with a model of CPU allocation to which they can program. (3) The design, implementation, and evaluation of two augmented CPU reservation schedulers, which provide increase scheduling predictability when low-level operating system activity steals time from applications

    Active job monitoring in pilots

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    Recent developments in high energy physics (HEP) including multi-core jobs and multi-core pilots require data centres to gain a deep understanding of the system to monitor, design, and upgrade computing clusters. Networking is a critical component. Especially the increased usage of data federations, for example in diskless computing centres or as a fall-back solution, relies on WAN connectivity and availability. The specific demands of different experiments and communities, but also the need for identification of misbehaving batch jobs, requires an active monitoring. Existing monitoring tools are not capable of measuring fine-grained information at batch job level. This complicates network-aware scheduling and optimisations. In addition, pilots add another layer of abstraction. They behave like batch systems themselves by managing and executing payloads of jobs internally. The number of real jobs being executed is unknown, as the original batch system has no access to internal information about the scheduling process inside the pilots. Therefore, the comparability of jobs and pilots for predicting run-time behaviour or network performance cannot be ensured. Hence, identifying the actual payload is important. At the GridKa Tier 1 centre a specific tool is in use that allows the monitoring of network traffc information at batch job level. This contribution presents the current monitoring approach and discusses recent e_orts and importance to identify pilots and their substructures inside the batch system. It will also show how to determine monitoring data of specific jobs from identified pilots. Finally, the approach is evaluated

    A Novel Workload Allocation Strategy for Batch Jobs

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    The distribution of computational tasks across a diverse set of geographically distributed heterogeneous resources is a critical issue in the realisation of true computational grids. Conventionally, workload allocation algorithms are divided into static and dynamic approaches. Whilst dynamic approaches frequently outperform static schemes, they usually require the collection and processing of detailed system information at frequent intervals - a task that can be both time consuming and unreliable in the real-world. This paper introduces a novel workload allocation algorithm for optimally distributing the workload produced by the arrival of batches of jobs. Results show that, for the arrival of batches of jobs, this workload allocation algorithm outperforms other commonly used algorithms in the static case. A hybrid scheduling approach (using this workload allocation algorithm), where information about the speed of computational resources is inferred from previously completed jobs, is then introduced and the efficiency of this approach demonstrated using a real world computational grid. These results are compared to the same workload allocation algorithm used in the static case and it can be seen that this hybrid approach comprehensively outperforms the static approach

    A hyper-heuristic for adaptive scheduling in computational grids

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    In this paper we present the design and implementation of an hyper-heuristic for efficiently scheduling independent jobs in computational grids. An efficient scheduling of jobs to grid resources depends on many parameters, among others, the characteristics of the resources and jobs (such as computing capacity, consistency of computing, workload, etc.). Moreover, these characteristics change over time due to the dynamic nature of grid environment, therefore the planning of jobs to resources should be adaptively done. Existing ad hoc scheduling methods (batch and immediate mode) have shown their efficacy for certain types of resource and job characteristics. However, as stand alone methods, they are not able to produce the best planning of jobs to resources for different types of Grid resources and job characteristics. In this work we have designed and implemented a hyper-heuristic that uses a set of ad hoc (immediate and batch mode) scheduling methods to provide the scheduling of jobs to Grid resources according to the Grid and job characteristics. The hyper-heuristic is a high level algorithm, which examines the state and characteristics of the Grid system (jobs and resources), and selects and applies the ad hoc method that yields the best planning of jobs. The resulting hyper-heuristic based scheduler can be thus used to develop network-aware applications that need efficient planning of jobs to resources. The hyper-heuristic has been tested and evaluated in a dynamic setting through a prototype of a Grid simulator. The experimental evaluation showed the usefulness of the hyper-heuristic for planning of jobs to resources as compared to planning without knowledge of the resource and job characteristics.Peer ReviewedPostprint (author's final draft
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