Relevant bibliographies by topics / Size-based scheduling

Academic literature on the topic 'Size-based scheduling'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Size-based scheduling"

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DellAmico, Matteo, Damiano Carra, and Pietro Michiardi. "PSBS: Practical Size-Based Scheduling." IEEE Transactions on Computers 65, no. 7 (July 1, 2016): 2199–212. http://dx.doi.org/10.1109/tc.2015.2468225.

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Harchol-Balter, Mor, Bianca Schroeder, Nikhil Bansal, and Mukesh Agrawal. "Size-based scheduling to improve web performance." ACM Transactions on Computer Systems 21, no. 2 (May 2003): 207–33. http://dx.doi.org/10.1145/762483.762486.

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Shim, Euysup, and Byung-Cheol Kim. "Batch-Size Based Repetitive Scheduling Method (BRSM)." International Journal of Construction Education and Research 10, no. 2 (March 10, 2014): 140–56. http://dx.doi.org/10.1080/15578771.2013.826753.

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Pastorelli, Mario, Damiano Carra, Matteo DellAmico, and Pietro Michiardi. "HFSP: Bringing Size-Based Scheduling To Hadoop." IEEE Transactions on Cloud Computing 5, no. 1 (January 1, 2017): 43–56. http://dx.doi.org/10.1109/tcc.2015.2396056.

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Roberts, James, and Dario Rossi. "Size-based scheduling vs fairness for datacenter flows." ACM SIGMETRICS Performance Evaluation Review 50, no. 2 (August 30, 2022): 2–10. http://dx.doi.org/10.1145/3561074.3561076.

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Contrary to the conclusions of a recent body of work where approximate shortest remaining processing time first (SRPT) flow scheduling is advocated for datacenter networks, this paper aims to demonstrate that imposing fairness remains a preferable objective. We evaluate abstract queuing models by analysis and simulation to illustrate the non-optimality of SRPT under the reasonable assumptions that datacenter source-destination flows occur in batches and bursts and not, as usually assumed, individually at the instants of a Poisson process. Results for these models have significant implications for the design of bandwidth sharing strategies for datacenter networks. In particular, we propose a novel "virtual fair scheduling" algorithm that enforces fairness between batches and is arguably simple enough to be implemented in high speed devices.
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Down, Douglas G. "Open Problem—Size-Based Scheduling with Estimation Errors." Stochastic Systems 9, no. 3 (September 2019): 295–96. http://dx.doi.org/10.1287/stsy.2019.0041.

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Rai, Dipankar, Chien-Wei (Wilson) Lin, and Magdoleen T. Ierlan. "The Influence of Scheduling Style on Assortment Size." Management & Marketing 11, no. 4 (December 1, 2016): 553–65. http://dx.doi.org/10.1515/mmcks-2016-0016.

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Abstract People use two types of scheduling styles to schedule their daily activities, namely clock-time or event-time. When people use clock time, they organize tasks based on a clock. When they use event-time, they organize tasks based on their order of completion. This research shows that adopting different scheduling styles influence consumers’ assortment size preferences. We demonstrate, through two studies, that consumers using event-time scheduling style prefer a larger assortment size whereas consumers using clock-time scheduling style prefer a smaller assortment size. We also show that this effect is mediated by desirability-feasibility consideration. Specifically, event-time scheduling style leads consumers to focus on the desirability considerations, which leads them to prefer larger assortment size while shopping. On the other hand, clock-time scheduling style leads consumers to focus on the feasibility considerations, which leads them to prefer smaller assortment size while shopping. We also discuss the theoretical and managerial implications of our research.
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Mi, Ningfang, Giuliano Casale, and Evgenia Smirni. "ASIdE: Using Autocorrelation-Based Size Estimation for Scheduling Bursty Workloads." IEEE Transactions on Network and Service Management 9, no. 2 (June 2012): 198–212. http://dx.doi.org/10.1109/tnsm.2012.041712.100073.

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Verloop, Maaike, Sem Borst, and Rudesindo Núñez-Queija. "Stability of size-based scheduling disciplines in resource-sharing networks." Performance Evaluation 62, no. 1-4 (October 2005): 247–62. http://dx.doi.org/10.1016/j.peva.2005.07.008.

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Marin, Andrea, Sabina Rossi, and Carlo Zen. "Size-based scheduling for TCP flows: Implementation and performance evaluation." Computer Networks 183 (December 2020): 107574. http://dx.doi.org/10.1016/j.comnet.2020.107574.

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Dissertations / Theses on the topic "Size-based scheduling"

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Pastorelli, Mario. "Disciplines basées sur la taille pour la planification des jobs dans data-intensif scalable computing systems." Thesis, Paris, ENST, 2014. http://www.theses.fr/2014ENST0048/document.

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La dernière décennie a vu l’émergence de systèmes parallèles pour l’analyse de grosse quantités de données (DISC) , tels que Hadoop, et la demande qui en résulte pour les politiques de gestion des ressources, pouvant fournir des temps de réponse rapides ainsi qu’équité. Actuellement, les schedulers pour les systèmes de DISC sont axées sur l’équité, sans optimiser les temps de réponse. Les meilleures pratiques pour surmonter ce problème comprennent une intervention manuelle et une politique de planification ad-hoc , qui est sujette aux erreurs et qui est difficile à adapter aux changements. Dans cette thèse, nous nous concentrons sur la planification basée sur la taille pour les systèmes DISC. La principale contribution de ce travail est le scheduler dit Hadoop Fair Sojourn Protocol (HFSP), un ordonnanceur préemptif basé sur la taille qui tient en considération le vieillissement, ayant comme objectifs de fournir l’équité et des temps de réponse réduits. Hélas, dans les systèmes DISC, les tailles des job d’analyse de données ne sont pas connus a priori, donc, HFSP comprends un module d’estimation de taille, qui calcule une approximation et qui affine cette estimation au fur et a mesure du progrès d’un job. Nous démontrons que l’impact des erreurs d’estimation sur les politiques fondées sur la taille n’est pas significatif. Pour cette raison, et en vertu d’être conçu autour de l’idée de travailler avec des tailles estimées, HFSP est tolérant aux erreurs d’estimation de la taille des jobs. Nos résultats expérimentaux démontrent que, dans un véritable déploiement Hadoop avec des charges de travail réalistes, HFSP est plus performant que les politiques de scheduling existantes, a la fois en terme de temps de réponse et d’équité. En outre, HFSP maintiens ses bonnes performances même lorsque le cluster de calcul est lourdement chargé, car il focalises les ressources sur des jobs ayant priorité. HFSP est une politique préventive: la préemption dans un système DISC peut être mis en œuvre avec des techniques différentes. Les approches actuellement disponibles dans Hadoop ont des lacunes qui ont une incidence sur les performances du système. Par conséquence, nous avons mis en œuvre une nouvelle technique de préemption, appelé suspension, qui exploite le système d’exploitation pour effectuer la préemption d’une manière qui garantie une faible latence sans pénaliser l’avancement des jobs a faible priorité
The past decade have seen the rise of data-intensive scalable computing (DISC) systems, such as Hadoop, and the consequent demand for scheduling policies to manage their resources, so that they can provide quick response times as well as fairness. Schedulers for DISC systems are usually focused on the fairness, without optimizing the response times. The best practices to overcome this problem include a manual and ad-hoc control of the scheduling policy, which is error-prone and difficult to adapt to changes. In this thesis we focus on size-based scheduling for DISC systems. The main contribution of this work is the Hadoop Fair Sojourn Protocol (HFSP) scheduler, a size-based preemptive scheduler with aging; it provides fairness and achieves reduced response times thanks to its size-based nature. In DISC systems, job sizes are not known a-priori: therefore, HFSP includes a job size estimation module, which computes approximated job sizes and refines these estimations as jobs progress. We show that the impact of estimation errors on the size-based policies is not signifi- cant, under conditions which are verified in a system such as Hadoop. Because of this, and by virtue of being designed around the idea of working with estimated sizes, HFSP is largely tolerant to job size estimation errors. Our experimental results show that, in a real Hadoop deployment and with realistic workloads, HFSP performs better than the built-in scheduling policies, achieving both fairness and small mean response time. Moreover, HFSP maintains its good performance even when the cluster is heavily loaded, by focusing the resources to few selected jobs with the smallest size. HFSP is a preemptive policy: preemption in a DISC system can be implemented with different techniques. Approaches currently available in Hadoop have shortcomings that impact on the system performance. Therefore, we have implemented a new preemption technique, called suspension, that exploits the operating system primitives to implement preemption in a way that guarantees low latency without penalizing low-priority jobs
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Book chapters on the topic "Size-based scheduling"

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Soudan, Sebastien, Dinil Mon Divakaran, Eitan Altman, and Pascale Vicat-Blanc Primet. "Equilibrium in Size-Based Scheduling Systems." In Analytical and Stochastic Modeling Techniques and Applications, 234–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02205-0_17.

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Divakaran, Dinil Mon, Eitan Altman, and Pascale Vicat-Blanc Primet. "Size-Based Flow-Scheduling Using Spike-Detection." In Analytical and Stochastic Modeling Techniques and Applications, 331–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21713-5_24.

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Song, Wei, and Weihua Zhuang. "Size-Based Load Sharing with SRPT Scheduling." In Interworking of Wireless LANs and Cellular Networks, 43–60. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4379-7_4.

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Aalto, Samuli, and Pasi Lassila. "Impact of Size-Based Scheduling on Flow Level Performance in Wireless Downlink Data Channels." In Lecture Notes in Computer Science, 1096–107. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72990-7_94.

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Manabe, Ren, and Yusuke Gotoh. "Evaluation of Scheduling Method for Division Based Broadcasting of Multiple Video Considering Data Size." In Advances on P2P, Parallel, Grid, Cloud and Internet Computing, 329–39. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02607-3_29.

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Jung, Low Tang, and Ahmed Abba Haruna. "Incentive-Based Scheduling for Green Computational Grid." In Role of IoT in Green Energy Systems, 272–93. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6709-8.ch012.

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In the computing grid environment, jobs scheduling is fundamentally the process of allocating computing jobs with choices relevant to the available resources. As the scale of grid computing system grows in size over time, exponential increase in energy consumption is foreseen. As such, large data centers (DC) are embarking on green computing initiatives to address the IT operations impact on the environment. The main component within a computing system consuming the most electricity and generating the most heat is the microprocessor. The heat generated by these high-performance microprocessors is emitting high CO2 footprint. Therefore, jobs scheduling with thermal considerations (thermal-aware) to the microprocessors is important in DC grid operations. An approach for jobs scheduling is proposed in this chapter for reducing electricity usage (green computing) in DC grid. This approach is the outcome of the R&D works based on the DC grid environment in Universiti Teknologi PETRONAS, Malaysia.
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He, Yaohua, and Chi-Wai Hui. "Dynamic rule-based genetic algorithm for large-size single-stage batch scheduling." In 16th European Symposium on Computer Aided Process Engineering and 9th International Symposium on Process Systems Engineering, 1911–16. Elsevier, 2006. http://dx.doi.org/10.1016/s1570-7946(06)80327-5.

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Huang, Ailing, Yijing Miao, and Jiarui Li. "Optimization of Transit Scheduling Combined with Short-Turn Service Based on Real-Time Passenger Flow." In Fuzzy Systems and Data Mining VI. IOS Press, 2020. http://dx.doi.org/10.3233/faia200752.

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In view of a series of problems, such as unable to meet the needs of passengers, high full load ratio or waste of carrying capacity on unbalanced passenger flow sections caused by the all-stop fleet scheduling in the urban public transit system, this paper proposed a bus combination scheduling strategy with considering short-turn service based on the imbalance coefficient of passenger flow and a method to determine the turning back point. A combined dispatching optimization model is established with the objective function of minimizing the total system cost which includes the waiting time cost of passengers, the congestion feeling cost and the operation cost of public transit enterprises. The headways of short-turn and all-stop scheme are optimized by the combined scheduling model, and the solution method is proposed. Taking Beijing No. A bus line as an empirical analysis object, the real-time passenger flow and vehicle data in a working day are collected and analyzed, and the optimized scheme of short-turn service combination scheduling is obtained. The results show that compared with the traditional all-stop fleet scheduling, the optimized short-turn service combination scheduling can reduce the fleet size by 4.9% and effectively improve the operation efficiency and system benefits.
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Niroomand, Sadegh, and Béla Vizvári. "Modeling a Real Cable Production System as a Single Machine-Scheduling Problem." In Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics, 327–45. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9644-0.ch012.

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In cases where the size and colour of cable are changed, the cable industry is classified as a multi-product, mass production system. The paper provides a mixed integer linear programming model based on continuous time representation for a case study on the scheduling problem of the cable industry to minimize the total cost including setup cost, operating cost, and inventory holding cost. As the solution methodology, three grouping policies are proposed while Xpress solver could not give any feasible solution for the model. Cables of the same size and the same colour, respectively, of the different types of cable are grouped together. A metaheuristic based on a simulated annealing algorithm is applied to minimize the total cost of proposed solutions. Finally the solution with the smallest total cost is selected as the production schedule of the study case.
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Rodriguez, Maria, and Rajkumar Buyya. "Container Orchestration With Cost-Efficient Autoscaling in Cloud Computing Environments." In Handbook of Research on Multimedia Cyber Security, 190–213. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2701-6.ch010.

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Containers are widely used by organizations to deploy diverse workloads such as web services, big data, and IoT applications. Container orchestration platforms are designed to manage the deployment of containerized applications in large-scale clusters. The majority of these platforms optimize the scheduling of containers on a fixed-sized cluster and are not enabled to autoscale the size of the cluster nor to consider features specific to public cloud environments. This chapter presents a resource management approach with three objectives: 1) optimize the initial placement of containers by efficiently scheduling them on existing resources, 2) autoscale the number of resources at runtime based on the cluster's workload, and 3) consolidate applications into fewer VMs at runtime. The framework was implemented as a Kubernetes plugin and its efficiency was evaluated on an Australian cloud infrastructure. The experiments demonstrate that a reduction of 58% in cost can be achieved by dynamically managing the cluster size and placement of applications.
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Conference papers on the topic "Size-based scheduling"

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Serwadda, Abdul, Vir V. Phoha, and Idris A. Rai. "Size-based scheduling." In the 17th ACM conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1866307.1866412.

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Khawam, Kinda, and Dana Marinca. "Size-based Proportional Fair scheduling." In 2010 IEEE 21st International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC 2010). IEEE, 2010. http://dx.doi.org/10.1109/pimrc.2010.5671641.

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Pastorelli, Mario, Antonio Barbuzzi, Damiano Carra, Matteo Dell'Amico, and Pietro Michiardi. "HFSP: Size-based scheduling for Hadoop." In 2013 IEEE International Conference on Big Data. IEEE, 2013. http://dx.doi.org/10.1109/bigdata.2013.6691554.

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Mangharam, Rahul, Mustafa Demirhan, Ragunathan Rajkumar, and Dipankar Raychaudhuri. "Size matters: size-based scheduling for MPEG-4 over wireless channels." In Electronic Imaging 2004, edited by Nalini Venkatasubramanian. SPIE, 2003. http://dx.doi.org/10.1117/12.538820.

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Mon Divakaran, Dinil, Fabienne Anhalt, Eitan Altman, and Pascale Vicat-Blanc Primet. "Size-based flow scheduling in a CICQ switch." In 2010 International Conference on High Performance Switching and Routing (HPSR). IEEE, 2010. http://dx.doi.org/10.1109/hpsr.2010.5580275.

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Dell'Amico, Matteo, Damiano Carra, Mario Pastorelli, and Pietro Michiardi. "Revisiting Size-Based Scheduling with Estimated Job Sizes." In 2014 IEEE 22nd International Symposium on Modelling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS. IEEE, 2014. http://dx.doi.org/10.1109/mascots.2014.57.

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Lassila, Pasi, and Samuli Aalto. "Combining opportunistic and size-based scheduling in wireless systems." In the 11th international symposium. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1454503.1454558.

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Lim, Sungtaek, Jihong Kim, and Kiyoung Choi. "Scheduling-based code size reduction in processors with indirect addressing mode." In the ninth international symposium. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/371636.371710.

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Proebster, Magnus. "Improving the quality of experience with size-based and opportunistic scheduling." In 2014 11th International Symposium on Wireless Communications Systems (ISWCS). IEEE, 2014. http://dx.doi.org/10.1109/iswcs.2014.6933394.

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Ohki, Makoto. "Many-Objective Nurse Scheduling using NSGA-II based on Pareto Partial Dominance with Linear Subset-size Scheduling." In 10th International Joint Conference on Computational Intelligence. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006894501180125.

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