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Journal articles on the topic 'Scheduling policies'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 September 2023

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1

Guide, V. Daniel R., Mark E. Kraus, and Rajesh Srivastava. "Scheduling policies for remanufacturing." International Journal of Production Economics 48, no. 2 (January 1997): 187–204. http://dx.doi.org/10.1016/s0925-5273(96)00091-6.

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2

Caspi, Paul, Jean-Louis Colaço, Léonard Gérard, Marc Pouzet, and Pascal Raymond. "Synchronous objects with scheduling policies." ACM SIGPLAN Notices 44, no. 7 (June 28, 2009): 11–20. http://dx.doi.org/10.1145/1543136.1542455.

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3

Labarta, Jesus, Sergi Girona, and Toni Cortes. "Analyzing scheduling policies using Dimemas." Parallel Computing 23, no. 1-2 (April 1997): 23–34. http://dx.doi.org/10.1016/s0167-8191(96)00094-4.

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4

Kesselman, Alex, and Adi Rosén. "Scheduling policies for CIOQ switches." Journal of Algorithms 60, no. 1 (July 2006): 60–83. http://dx.doi.org/10.1016/j.jalgor.2004.09.003.

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5

Thomasian, Alexander, and Chang Liu. "Disk scheduling policies with lookahead." ACM SIGMETRICS Performance Evaluation Review 30, no. 2 (September 2002): 31–40. http://dx.doi.org/10.1145/588160.588165.

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6

Anton, E., R. Righter, and I. M. Verloop. "Scheduling under redundancy." ACM SIGMETRICS Performance Evaluation Review 50, no. 2 (August 30, 2022): 30–32. http://dx.doi.org/10.1145/3561074.3561085.

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In the present extended abstract we investigate the impact that the scheduling policy has on the performance of redundancy systems when the usual exponentially distributed i.i.d. copies assumption is relaxed. In particular, we investigate the performance, in terms of the total number of jobs in the system, not only for redundancy-oblivious policies, such as FCFS (First-Come-First-Serve) and ROS (Random- Order-of-Service), but also for redundancy-aware policies of the form Π1-?2, where Π1 discriminates among job classes and Π2 discriminates among jobs of the same class. Examples of first-level policies are LRF (Least-Redundant-First) and MRF (Most-Redundant-First), where under LRF, respectively MRF, within a server jobs with fewer copies, respectively more copies, have priority over jobs with more copies, respectively fewer copies. Second-level policies could be FCFS or ROS.

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7

Guide, V. D. R., R. Srivastava, and M. E. Kraus. "Priority scheduling policies for repair shops." International Journal of Production Research 38, no. 4 (March 2000): 929–50. http://dx.doi.org/10.1080/002075400189220.

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8

Krueger, P., and N. G. Shivaratri. "Adaptive location policies for global scheduling." IEEE Transactions on Software Engineering 20, no. 6 (June 1994): 432–44. http://dx.doi.org/10.1109/32.295892.

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9

Brown, Patrick. "Comparing FB and PS scheduling policies." ACM SIGMETRICS Performance Evaluation Review 34, no. 3 (December 2006): 18–20. http://dx.doi.org/10.1145/1215956.1215965.

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10

Martinez, G., E. Heymann, and M. Senar. "Integrating scheduling policies into workflow engines." Procedia Computer Science 1, no. 1 (May 2010): 2743–52. http://dx.doi.org/10.1016/j.procs.2010.04.308.

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11

Somló, J. "Suitable switching policies for FMS scheduling." Mechatronics 14, no. 2 (March 2004): 199–225. http://dx.doi.org/10.1016/s0957-4158(03)00030-8.

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12

Núñez-del-Toro, Cristina, Elena Fernández, Jörg Kalcsics, and Stefan Nickel. "Scheduling policies for multi-period services." European Journal of Operational Research 251, no. 3 (June 2016): 751–70. http://dx.doi.org/10.1016/j.ejor.2015.12.002.

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13

Jin, Wangkai, and Xiangjun Peng. "SLITS: Sparsity-Lightened Intelligent Thread Scheduling." ACM SIGMETRICS Performance Evaluation Review 51, no. 1 (June 26, 2023): 21–22. http://dx.doi.org/10.1145/3606376.3593568.

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To make the most of hardware resources in multi-core architectures, effective thread scheduling is crucial. To achieve this, various scheduling objectives have been developed, such as reducing hardware resource contention [1, 11], allocating resources evenly for co-running threads [ 5, 6], and following priority-based policies [7]. Current thread scheduling designs can be categorized into two types. The first type involves fixed-rule scheduling1, which does not depend on workload characteristics and cannot meet the needs of different scheduling objectives. The second type takes the scheduling objectives into account by collecting run-time information on threads together with their correlations (e.g., Cache Miss Count [10, 13], Thread IPC [20], dynamic priority requirements like Earliest Deadline First [2 , 8, 17]), and making scheduling decisions based on thread-to-thread interactions. A unified approach to these two types of scheduler designs can be achieved by focusing on Thread-Interaction statistics. To this end, we introduce the Thread-Interaction Matrix (TIM), which stores statistics on thread-to-thread interaction. These statistics can be any type of run-time statistics concerning the thread-to-thread pairs (e.g., Cache Miss Count [10 , 13], Thread IPC [20], dynamic priority requirements like Earliest Deadline First [2 , 8, 17])2. For fixed-rule scheduling, the TIM contains static values as the statistics do not affect the scheduling decisions. Based on the TIM, scheduling policies can be customized by specifying the rules of thread rescheduling, such as reschedule conditions and strategy. Combining the Thread-Interaction Matrix and scheduling policy provides a formalization of existing thread scheduler designs. Therefore, it is essential to design a general scheduler that can be tailored to different scheduling objectives by co-designing the Thread-Interaction Matrix and the scheduling policy in a synergistic manner.

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14

Weishaupt, J�rgen. "Optimal myopic policies and index policies for stochastic scheduling problems." ZOR - Methods and Models of Operations Research 40, no. 1 (March 1994): 75–89. http://dx.doi.org/10.1007/bf01414030.

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15

Kaffes, Kostis. "OS Scheduling." Queue 21, no. 2 (April 30, 2023): 88–95. http://dx.doi.org/10.1145/3595837.

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In any system that multiplexes resources, the problem of scheduling what computations run where and when is perhaps the most fundamental. Yet, like many other essential problems in computing (e.g., query optimization in databases), academic research in scheduling moves like a pendulum, with periods of intense activity followed by periods of dormancy when it is considered a "solved" problem. These three papers make significant contributions to an ongoing effort to develop better scheduling policies for modern computing systems. The papers highlight the need for better, more efficient, and more flexible OS schedulers; open up new areas of research; and demonstrate the importance of continued development and innovation in OS scheduling policies.

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16

Zhang, Fan, Baozhu Li, and Gangqiang Yang. "Research on Throughput-Guaranteed MAC Scheduling Policies in Wireless Networks." Entropy 24, no. 9 (September 4, 2022): 1246. http://dx.doi.org/10.3390/e24091246.

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In wireless networks, MAC scheduling methods can be divided into two types according to the implementation model: centralized and distributed scheduling. By reasonably designing MAC scheduling policies, both centralized and distributed schedulers can ensure a reliable throughput capacity region, i.e., realizing throughput-guaranteed. However, it can be found that some existing throughput-guaranteed scheduling schemes cannot further ensure bounded end-to-end average delay, and the reason for this phenomenon has not been deeply analyzed. In practical communication networks, throughput and delay are equally important. Based on this idea, the existing MAC scheduling strategies are investigated systematically in this paper from two aspects of throughput and delay, and their performances are evaluated and compared through both theoretical analysis and simulation experiments. The work of this paper provides a theoretical basis for the improvement of MAC scheduling technology in the next-generation wireless networks.

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17

Laws, C. N., and G. M. Louth. "Dynamic Scheduling of a Four-Station Queueing Network." Probability in the Engineering and Informational Sciences 4, no. 1 (January 1990): 131–56. http://dx.doi.org/10.1017/s0269964800001492.

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This paper is concerned with the problem of optimally scheduling a multiclass open queueing network with four single-server stations in which dynamic control policies are permitted. Under the assumption that the system is heavily loaded, the original scheduling problem can be approximated by a dynamic control problem involving Brownian motion. We reformulate and solve this problem and, from the interpretation of the solution, we obtain two dynamic scheduling policies for our queueing network. We compare the performance of these policies with two static scheduling policies and a lower bound via simulation. Our results suggest that under either dynamic policy the system, at least when heavily loaded, exhibits the form of resource pooling given by the solution to the approximating control problem. Furthermore, even when lightly loaded the system performs better under the dynamic policies than under either static policy.

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18

Eryilmaz, A., R. Srikant, and J. R. Perkins. "Stable scheduling policies for fading wireless channels." IEEE/ACM Transactions on Networking 13, no. 2 (April 2005): 411–24. http://dx.doi.org/10.1109/tnet.2004.842226.

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19

Yang, Changwoo, Adam Wierman, Sanjay Shakkottai, and Mor Harchol-Balter. "Many Flows Asymptotics for SMART Scheduling Policies." IEEE Transactions on Automatic Control 57, no. 2 (February 2012): 376–91. http://dx.doi.org/10.1109/tac.2011.2173418.

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20

Mosheiov, Gur. "V-Shaped Policies for Scheduling Deteriorating Jobs." Operations Research 39, no. 6 (December 1991): 979–91. http://dx.doi.org/10.1287/opre.39.6.979.

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21

Kundu, Atreyee, and Daniel E. Quevedo. "Stabilizing Scheduling Policies for Networked Control Systems." IEEE Transactions on Control of Network Systems 7, no. 1 (March 2020): 163–75. http://dx.doi.org/10.1109/tcns.2019.2913566.

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22

Williams, Myrtle Taylor. "Policies and Procedures for Scheduling Student Nurses." JONA: The Journal of Nursing Administration 18, no. 9 (September 1988): 32. http://dx.doi.org/10.1097/00005110-198809010-00007.

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23

Kadloor, Sachin, Xun Gong, Negar Kiyavash, and Parv Venkitasubramaniam. "Designing Router Scheduling Policies: A Privacy Perspective." IEEE Transactions on Signal Processing 60, no. 4 (April 2012): 2001–12. http://dx.doi.org/10.1109/tsp.2011.2182348.

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24

Shah, Devavrat, John N. Tsitsiklis, and Yuan Zhong. "Qualitative properties of α-weighted scheduling policies." ACM SIGMETRICS Performance Evaluation Review 38, no. 1 (June 12, 2010): 239–50. http://dx.doi.org/10.1145/1811099.1811067.

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25

Olivier, C., and O. Dessoude. "Index Rule Scheduling Policies Applied to Identification." IFAC Proceedings Volumes 25, no. 20 (September 1992): 147–52. http://dx.doi.org/10.1016/s1474-6670(17)49853-3.

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26

Cassandras, Christos G., and Vibhor Julka. "Scheduling policies using marked/phantom slot algorithms." Queueing Systems 20, no. 1-2 (March 1995): 207–54. http://dx.doi.org/10.1007/bf01158437.

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27

Eberle, Franziska, Felix Fischer, Jannik Matuschke, and Nicole Megow. "On index policies for stochastic minsum scheduling." Operations Research Letters 47, no. 3 (May 2019): 213–18. http://dx.doi.org/10.1016/j.orl.2019.03.007.

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28

Burgess, K., and K. M. Passino. "Stable scheduling policies for flexible manufacturing systems." IEEE Transactions on Automatic Control 42, no. 3 (March 1997): 420–25. http://dx.doi.org/10.1109/9.557589.

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29

Müller-Hannemann, Matthias, and Karsten Weihe. "Moving policies in cyclic assembly line scheduling." Theoretical Computer Science 351, no. 3 (February 2006): 425–36. http://dx.doi.org/10.1016/j.tcs.2005.10.023.

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30

M�hring, Rolf H., and Frederik Stork. "Linear preselective policies for stochastic project scheduling." Mathematical Methods of Operations Research (ZOR) 52, no. 3 (December 31, 2000): 501–15. http://dx.doi.org/10.1007/s001860000095.

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31

Koroušić-Seljak, Barbara. "Task scheduling policies for real-time systems." Microprocessors and Microsystems 18, no. 9 (January 1994): 501–11. http://dx.doi.org/10.1016/0141-9331(94)90073-6.

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32

Kumar, P. R., and S. P. Meyn. "Stability of queueing networks and scheduling policies." IEEE Transactions on Automatic Control 40, no. 2 (1995): 251–60. http://dx.doi.org/10.1109/9.341782.

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33

Rosti, E., E. Smirni, L. W. Dowdy, G. Serazzi, and K. C. Sevcik. "Processor saving scheduling policies for multiprocessor systems." IEEE Transactions on Computers 47, no. 2 (1998): 178–89. http://dx.doi.org/10.1109/12.663764.

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34

Lin, Kyle Y., Moshe Kress, and Roberto Szechtman. "Scheduling policies for an antiterrorist surveillance system." Naval Research Logistics 56, no. 2 (March 2009): 113–26. http://dx.doi.org/10.1002/nav.20341.

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35

Liang, William Kun, Barış Balcıog̃lu, and Robert Svaluto. "Scheduling policies for a repair shop problem." Annals of Operations Research 211, no. 1 (July 3, 2013): 273–88. http://dx.doi.org/10.1007/s10479-013-1412-6.

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36

Liu, Zhen, and Don Towsley. "Stochastic Scheduling in in-Forest Networks." Advances in Applied Probability 26, no. 1 (March 1994): 222–41. http://dx.doi.org/10.2307/1427588.

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In this paper we study the extremal properties of several scheduling policies in an in-forest network consisting of multiserver queues. Each customer has a due date, and we assume that service times at the different queues form mutually independent sequences of independent and identically distributed random variables independent of the arrival times and due dates. Furthermore, the network is assumed to consist of a mixture of nodes, some of which permit only non-preemptive service policies whereas the others permit preemptive resume policies. In the case of non-preemptive queues, service times may be generally distributed if there is only one server; otherwise the service times are required to be increasing in likelihood ratio (ILR). In the case of preemptive queues, service times are restricted to exponential distributions. Using stochastic majorizations and partial orders on permutations, we establish that, within the class of work conserving service policies, the stochastically smallest due date (SSDD) and the stochastically largest due date (SLDD) policies minimize and maximize, respectively, the vector of the customer latenesses of the first n customers in the sense of the Schur-convex order and some weaker orders, provided the due dates are comparable in some stochastic sense. It then follows that the first come-first served (FCFS) and the last come-first served (LCFS) policies minimize and maximize, respectively, the vector of the response times of the first n customers in the sense of the Schur-convex order. We also show that the FCFS and LCFS policies minimize and maximize, respectively, the vector of customer end-to-end delays in the sense of the strong stochastic order. Extensions to the class of non-idling policies and to the stationary regime are also given.

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37

Liu, Zhen, and Don Towsley. "Stochastic Scheduling in in-Forest Networks." Advances in Applied Probability 26, no. 01 (March 1994): 222–41. http://dx.doi.org/10.1017/s0001867800026094.

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In this paper we study the extremal properties of several scheduling policies in an in-forest network consisting of multiserver queues. Each customer has a due date, and we assume that service times at the different queues form mutually independent sequences of independent and identically distributed random variables independent of the arrival times and due dates. Furthermore, the network is assumed to consist of a mixture of nodes, some of which permit only non-preemptive service policies whereas the others permit preemptive resume policies. In the case of non-preemptive queues, service times may be generally distributed if there is only one server; otherwise the service times are required to be increasing in likelihood ratio (ILR). In the case of preemptive queues, service times are restricted to exponential distributions. Using stochastic majorizations and partial orders on permutations, we establish that, within the class of work conserving service policies, the stochastically smallest due date (SSDD) and the stochastically largest due date (SLDD) policies minimize and maximize, respectively, the vector of the customer latenesses of the first n customers in the sense of the Schur-convex order and some weaker orders, provided the due dates are comparable in some stochastic sense. It then follows that the first come-first served (FCFS) and the last come-first served (LCFS) policies minimize and maximize, respectively, the vector of the response times of the first n customers in the sense of the Schur-convex order. We also show that the FCFS and LCFS policies minimize and maximize, respectively, the vector of customer end-to-end delays in the sense of the strong stochastic order. Extensions to the class of non-idling policies and to the stationary regime are also given.

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38

Pedarsani, Ramtin, Jean Walrand, and Yuan Zhong. "Robust scheduling for flexible processing networks." Advances in Applied Probability 49, no. 2 (June 2017): 603–28. http://dx.doi.org/10.1017/apr.2017.14.

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Abstract Modern processing networks often consist of heterogeneous servers with widely varying capabilities, and process job flows with complex structure and requirements. A major challenge in designing efficient scheduling policies in these networks is the lack of reliable estimates of system parameters, and an attractive approach for addressing this challenge is to design robust policies, i.e. policies that do not use system parameters such as arrival and/or service rates for making scheduling decisions. In this paper we propose a general framework for the design of robust policies. The main technical novelty is the use of a stochastic gradient projection method that reacts to queue-length changes in order to find a balanced allocation of service resources to incoming tasks. We illustrate our approach on two broad classes of processing systems, namely the flexible fork-join networks and the flexible queueing networks, and prove the rate stability of our proposed policies for these networks under nonrestrictive assumptions.

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39

Thaman, Jyoti, and Manpreet Singh. "Extending Dynamic Scheduling Policies in WorkflowSim by Using Variance based Approach." International Journal of Grid and High Performance Computing 8, no. 2 (April 2016): 76–93. http://dx.doi.org/10.4018/ijghpc.2016040105.

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Workflow scheduling has been around for more than two decades. With growing interest in service oriented computing architecture among researchers and corporate users, different platform like clusters computing, grid computing and most recent cloud computing, appeared on computing horizon. Cloud computing has attracted a lot of interest from all types of users. It gave rise to variety of applications and tasks with varied requirements. Heterogeneity in application's requirement catalyzes the provision of customized services for task types. Representation of tasks characteristics and inter-task relationship through workflows is in use since the ages of automation. Scheduling of workflows not only maintain the hierarchical relationship between the tasks but also dictates the requirement of dynamic scheduling. This paper presents a variance based extensions of few promising dynamic scheduling policies supported by WorkflowSim. An exhaustive performance analysis presents strength and weakness of the authors' proposal.

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40

Gu, Jian, and Wei Min Mao. "A Production Scheduling Framework Integrated with Simulation Module." Advanced Materials Research 602-604 (December 2012): 1831–34. http://dx.doi.org/10.4028/www.scientific.net/amr.602-604.1831.

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It is necessary to achieve high system performances in terms of throughput rate and service level in today’s business environment. This can be achieved by implementing efficient and effective production planning methods complemented with precise and fast scheduling predictions. A framework is proposed to integrate real-time production data, scheduling mechanisms and simulation for providing realistic scheduling policies that could be used for operational and tactical decision-making. The focus is on the use of discrete event simulation utilizing relevant shop floor data, provided by an ERP system. A primary objective is to evaluate and characterize scheduling policies in a discrete manufacturing environment.

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41

Nain, Philippe, Pantelis Tsoucas, and Jean Walrand. "Interchange arguments in stochastic scheduling." Journal of Applied Probability 26, no. 4 (December 1989): 815–26. http://dx.doi.org/10.2307/3214386.

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Interchange arguments are applied to establish the optimality of priority list policies in three problems. First, we prove that in a multiclass tandem of two ·/M/1 queues it is always optimal in the second node to serve according to the cµ rule. The result holds more generally if the first node is replaced by a multiclass network consisting of ·/M/1 queues with Bernoulli routing. Next, for scheduling a single server in a multiclass node with feedback, a simplified proof of Klimov's result is given. From it follows the optimality of the index rule among idling policies for general service time distributions, and among pre-emptive policies when the service time distributions are exponential. Lastly, we consider the problem of minimizing the blocking in a communication link with lossy channels and exponential holding times.

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42

Nain, Philippe, Pantelis Tsoucas, and Jean Walrand. "Interchange arguments in stochastic scheduling." Journal of Applied Probability 26, no. 04 (December 1989): 815–26. http://dx.doi.org/10.1017/s0021900200027686.

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Interchange arguments are applied to establish the optimality of priority list policies in three problems. First, we prove that in a multiclass tandem of two ·/M/1 queues it is always optimal in the second node to serve according to the cµ rule. The result holds more generally if the first node is replaced by a multiclass network consisting of ·/M/1 queues with Bernoulli routing. Next, for scheduling a single server in a multiclass node with feedback, a simplified proof of Klimov's result is given. From it follows the optimality of the index rule among idling policies for general service time distributions, and among pre-emptive policies when the service time distributions are exponential. Lastly, we consider the problem of minimizing the blocking in a communication link with lossy channels and exponential holding times.

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43

Kaffes, Kostis, and Peter Alvaro. "Research for Practice: OS Scheduling." Communications of the ACM 66, no. 9 (August 23, 2023): 52–54. http://dx.doi.org/10.1145/3606945.

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44

Zachary, Stan, Simon H. Tindemans, Michael P. Evans, James R. Cruise, and David Angeli. "Scheduling of energy storage." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2202 (June 7, 2021): 20190435. http://dx.doi.org/10.1098/rsta.2019.0435.

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The increasing reliance on renewable energy generation means that storage may well play a much greater role in the balancing of future electricity systems. We show how heterogeneous stores, differing in capacity and rate constraints, may be optimally, or nearly optimally, scheduled to assist in such balancing, with the aim of minimizing the total imbalance (unserved energy) over any given period of time. It further turns out that in many cases the optimal policies are such that the optimal decision at each point in time is independent of the future evolution of the supply–demand balance in the system, so that these policies remain optimal in a stochastic environment. This article is part of the theme issue ‘The mathematics of energy systems’.

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45

Ashlagi, Itai, Moshe Tennenholtz, and Aviv Zohar. "Competing Schedulers." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 4, 2010): 691–96. http://dx.doi.org/10.1609/aaai.v24i1.7631.

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Previous work on machine scheduling has considered the case of agents who control the scheduled jobs and attempt to minimize their own completion time. We argue that in cloud and grid computing settings, different machines cannot be considered to be fully cooperative as they may belong to competing economic entities, and that agents can easily move their jobs between competing providers. We therefore consider a setting in which the machines are also controlled by selfish agents, and attempt to maximize their own gains by strategically selecting their scheduling policy. We analyze the equilibria that arise due to competition in this 2-sided setting. In particular, not only do we require that the jobs will be in equilibrium with one another, but also that the schedulers' policies will be in equilibrium. We also consider different mixtures of classic deterministic scheduling policies and random scheduling policies.

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46

more, Ruchira, and Milind Penurkar. "Scheduling and Dropping Policies in Delay Tolerant Network." IARJSET 2, no. 10 (October 20, 2015): 68–70. http://dx.doi.org/10.17148/iarjset.2015.21014.

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47

Carfang, Anthony J., and Eric W. Frew. "Fast Link Scheduling Policies for Persistent Data Ferrying." Journal of Aerospace Information Systems 13, no. 12 (December 2016): 433–49. http://dx.doi.org/10.2514/1.i010429.

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48

Savory, Paul, and Gene Saghi. "Simulating Queue Scheduling Policies for a Spacecraft Simulator." Interfaces 27, no. 5 (October 1997): 1–8. http://dx.doi.org/10.1287/inte.27.5.1.

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49

Yang, Howard H., Zuozhu Liu, Tony Q. S. Quek, and H. Vincent Poor. "Scheduling Policies for Federated Learning in Wireless Networks." IEEE Transactions on Communications 68, no. 1 (January 2020): 317–33. http://dx.doi.org/10.1109/tcomm.2019.2944169.

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50

Choffnes, David, Mark Astley, and Michael J. Ward. "Migration policies for multi-core fair-share scheduling." ACM SIGOPS Operating Systems Review 42, no. 1 (January 2008): 92–93. http://dx.doi.org/10.1145/1341312.1341328.

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