Showing papers on "Fair-share scheduling published in 1997"

Scheduling to minimize average completion time: off-line and on-line approximation algorithms

Abstract: In this paper we introduce two general techniques for the design and analysis of approximation algorithms for NP-hard scheduling problems in which the objective is to minimize the weighted sum of the job completion times. For a variety of scheduling models, these techniques yield the first algorithms that are guaranteed to find schedules that have objective function value within a constant factor of the optimum. In the first approach, we use an optimal solution to a linear programming relaxation in order to guide a simple list-scheduling rule. Consequently, we also obtain results about the strength of the relaxation. Our second approach yields on-line algorithms for these problems: in this setting, we are scheduling jobs that continually arrive to be processed and, for each time t, we must construct the schedule until time t without any knowledge of the jobs that will arrive afterwards. Our on-line technique yields constant performance guarantees for a variety of scheduling environments, and in some cases essentially matches the performance of our off-line LP-based algorithms.

New Benchmark Results for the Resource-Constrained Project Scheduling Problem

Abstract: This paper reports on new insights derived from computational results obtained with an updated version of the branch-and-bound procedure previously developed by Demeulemeester and Herroelen Demeulemeester, E., W. Herroelen. 1992. A branch-and-bound procedure for the multiple resource-constrained project scheduling problem. Management Sci.38 1803-1818. for solving the resource-constrained project scheduling problem RCPSP. The new code fully exploits the advantages of 32-bit programming provided by recent compilers running on platforms such as Windows NT® and OS/2®: flat memory, increased addressable memory, and fast program execution. We study the impact of three important variables on the computation time for the RCPSP: addressable computer memory, the search strategy depth-first, best-first, or hybrid, and the introduction of a stronger lower bound. We compare the results obtained by a truncated branch-and-bound procedure with the results generated by the minimum slack time heuristic and report on the dependency of its solution quality on the allotted CPU time.

Optimal time-critical scheduling via resource augmentation

Abstract: We consider two fundamental problems in dynamic scheduling: scheduling to meet deadlines in a preemptive multiprocessor setting, and scheduling to provide good response time in a number of scheduling environments. When viewed from the perspective of traditional worst-case analysis, no good on-line algorithms exist for these problems, and for some variants no good off-line algorithms exist unless {Rho} = {Nu}{Rho}. We study these problems using a relaxed notion of competitive analysis, introduced by Kalyanasundaram and Pruhs, in which the on-line algorithm is allowed more resources than the optimal off-line algorithm to which it is compared. Using this approach, we establish that several well-known on-line algorithms, that have poor performance from an absolute worst-case perspective, are optimal for the problems in question when allowed moderately more resources. For the optimization of average flow time, these are the first results of any sort, for any {Nu}{Rho}-hard version of the problem, that indicate that it might be possible to design good approximation algorithms.

Improved Utilization and Responsiveness with Gang Scheduling

Abstract: Most commercial multicomputers use space-slicing schemes in which each scheduling decision has an unknown impact on the future: should a job be scheduled, risking that it will block other larger jobs later, or should the processors be left idle for now in anticipation of future arrivals? This dilemma is solved by using gang scheduling, because then the impact of each decision is limited to its time slice, and future arrivals can be accommodated in other time slices. This added flexibility is shown to improve overall system utilization and responsiveness. Empirical evidence from using gang scheduling on a Cray T3D installed at Lawrence Livermore National Lab corroborates these results, and shows conclusively that gang scheduling can be very effective with current technology.

Current trends in deterministic scheduling

Abstract: Scheduling is concerned with allocating limited resources to tasks to optimize certain objective functions. Due to the popularity of the Total Quality Management concept, on-time delivery of jobs has become one of the crucial factors for customer satisfaction. Scheduling plays an important role in achieving this goal. Recent developments in scheduling theory have focused on extending the models to include more practical constraints. Furthermore, due to the complexity studies conducted during the last two decades, it is now widely understood that most practical problems are NP-hard. This is one of the reasons why local search methods have been studied so extensively during the last decade. In this paper, we review briefly some of the recent extensions of scheduling theory, the recent developments in local search techniques and the new developments of scheduling in practice. Particularly, we survey two recent extensions of theory: scheduling with a 1-job-on-r-machine pattern and machine scheduling with availability constraints. We also review several local search techniques, including simulated annealing, tabu search, genetic algorithms and constraint guided heuristic search. Finally, we study the robotic cell scheduling problem, the auto-mated guided vehicles scheduling problem, and the hoist scheduling problem.

Fair scheduling in wireless packet networks

Abstract: Fair scheduling of delay and rate-sensitive packet flows over a wireless channel is not addressed effectively by most contemporary wireline fair scheduling algorithms because of two unique characteristics of wireless media: (a) bursty channel errors, and (b) location-dependent channel capacity and errors. Besides, in packet cellular networks, the base station typically performs the task of packet scheduling for both downlink and uplink flows in a cell; however a base station has only a limited knowledge of the arrival processes of uplink flows.In this paper, we propose a new model for wireless fair scheduling based on an adaptation of fluid fair queueing to handle location-dependent error bursts. We describe an ideal wireless fair scheduling algorithm which provides a packetized implementation of the fluid model while assuming full knowledge of the current channel conditions. For this algorithm, we derive the worst-case throughput and delay bounds. Finally, we describe a practical wireless scheduling algorithm which approximates the ideal algorithm. Through simulations, we show that the algorithm achieves the desirable properties identified in the wireless fluid fair queueing model.

An exact algorithm for project scheduling with multiple modes

Abstract: We consider an extension of the classical resource-constrained project scheduling problem (RCPSP), which covers discrete resource-resource and time-resource tradeoffs. As a result a project scheduler is permitted to identify several alternatives or modes of accomplishment for each activity of the project. The solution procedure to be presented is a considerable generalization of the branch-and-bound algorithm proposed by Demeulemeester and Herroelen, which is currently the most powerful method for optimally solving the RCPSP. More precisely, we extend their concept of delay alternatives by introducing mode alternatives. The basic enumeration scheme is enhanced by dominance rules which increase the performance of the algorithm. We then report on our computational results obtained from the comparison with the most rapid procedure reported in the literature.

Job shop scheduling with a genetic algorithm and machine learning

Abstract: Dynamic job shop scheduling has been proven to be an intractable problem for analytical procedures. Recent advances in computing technology, especially in artificial intelligence, have alleviated this problem by intelligently restricting the search space considered, thus opening the possibility of obtaining better results. Researchers have used various techniques that were developed under the general rubric of artificial intelligence to solve job shop scheduling problems. The most common of these have been expert systems, genetic algorithms and machine learning. Of these, we identify machine learning and genetic algorithms to be promising for scheduling applications in a job shop. In this paper, we propose to combine complementarily the strengths of genetic algorithms and induced decision trees, a machine learning technique, to develop a job shop scheduling system. Empirical results, using machine learning for releasing jobs into the shop floor and a genetic algorithm to dispatch jobs at each machine, are...

Planning and scheduling

Abstract: In this chapter, we use the generic term planning to encompass both planning and scheduling problems, and the terms planner or planning system to refer to software for planning or scheduling. Planning is concerned with reasoning about the consequences of acting in order to choose from among a set of possible courses of action. In the simplest case, a planner might enumerate a set of possible courses of action, consider their consequences in turn, and choose one particular course of action that satis es a given set of requirements. Algorithmically, a planning problem has as input a set of possible courses of actions, a predictive model for the underlying dynamics, and a performance measure for evaluating courses of action. The output or solution to a planning problem is one or more courses of action that satisfy the speci ed requirements for performance. Most planning problems are combinatorial in the sense that the number of possible courses of actions or the time required to evaluate a given course of action is exponential in the description of the problem. Just because there is an exponential number of possible courses of action does not imply that a planner has to enumerate them all in order to nd a solution. However, many planning problems can be shown to be NP-hard, and, for these problems, all known exact algorithms take exponential time in the worst case. The computational complexity of planning problems often leads practitioners to consider approximations, computation time versus solution quality tradeo s, and heuristic methods.

Planning and Scheduling of Parallel Semicontinuous Processes. 2. Short-Term Scheduling

Abstract: In part 1 of this series, it was seen how minimum run length constraints may complicate conventional multiperiod models. For short-term scheduling, these constraints, along with sequencing issues, ...

A scheme for scheduling hard real-time applications in open system environment

Abstract: The paper focuses on the problem of providing run-time support to real-time applications and non-real-time applications in an open system. It describes a two-level hierarchical priority-driven scheme for scheduling independently developed applications. The scheme allows the developer of each real-time application to validate the schedulability of the application independently of other applications. Once a real-time application is created and accepted by the open system, its schedulability is guaranteed regardless of the behaviors of other applications that execute concurrently in the system.

β-Robust scheduling for single-machine systems with uncertain processing times

Abstract: In scheduling environments with processing time uncertainty, system performance is determined by both the sequence in which jobs are ordered and the actual processing times of jobs. For these situations, the risk of achieving substandard system performance can be an important measure of scheduling effectiveness. To hedge this risk requires an explicit consideration of both the mean and the variance of system performance associated with alternative schedules, and motivates a β-robustness objective to capture the likelihood that a schedule yields actual performance no worse than a given target level. In this paper we focus on β-robust scheduling issues in single-stage production environments with uncertain processing times. We define a general β-robust scheduling objective, formulate the β-robust scheduling problem that results when job processing times are independent random variables and the performance measure of interest is the total flow time across all jobs, establish problem complexity, and develop e...

Scheduling messages on controller area network for real-time CIM applications

Abstract: Scheduling messages on the controller area network (CAN) corresponds to assigning identifiers (ID's) to messages according to their priorities. In this paper we present the mixed traffic scheduler (MTS) which provides higher schedulability than fixed-priority schemes, like deadline-monotonic (DM), while incurring less overhead than dynamic earliest-deadline (ED) scheduling. Through simulations, we compare the performance of MTS with that of DM and ED* (an imaginary scheduler which works like ED, except it incurs less overhead). Our simulations show that MTS performs much better than DM and at the same level as ED*, except under high loads and tight deadlines, when ED* is superior.

Using multi-agent architecture in FMS for dynamic scheduling

Abstract: The proposed scheduling strategy is based on a multi-agent architecture. Each agent of this architecture is dedicated to a work centre (i.e. a set of resources of the manufacturing system); it selects locally and dynamically the most suitable dispatching rules. Depending on local and global considerations, a new selection is carried out each time a predefined event occurs (for example, a machine becomes available, or a machine breaks down). The selection depends on: (1) primary and secondary performance objectives, (2) the operating conditions, and (3) an analysis of the system state, which aims to detect particular symptoms from the values of certain system variables. We explain how the scheduling strategy is shared out between agents, how each agent performs a local dynamic scheduling by selecting an adequate dispatching rule, and how agents can coordinate their actions to perform a global dynamic scheduling of the manufacturing system. Each agent can be implemented through object-oriented formalisms. The selection method is improved through the optimization of the numerical thresholds used in the detection of symptoms. This approach is compared with the use of SPT, SIX, MOD, CEXSPT and CR/SPT on a jobshop problem, already used in other research works. The results indicate significant improvements.

Multi-processor scheduling kernel

Abstract: A scheduling kernel provides fair share scheduling of several virtual machines by a multi-processor scheduling module scheduling the virtual machines across the several processors of the multi-processor. A virtual machine scheduling module schedules threads of a virtual machine, and provides an independent scheduling policy for a virtual machine. Execution exclusion sets may be created and enforced by an execution exclusion set module to limit execution to a single thread at a time out of any particular execution exclusion set of threads.

Iterative repair optimization with particular application to scheduling for integrated capacity and inventory planning

Abstract: A schedule for a complex activity is obtained by a scheduling system using a method of constraint-based iterative repair. A predetermined initial schedule is iteratively repaired, repairs being made during each iteration only to portions of the schedule that produce a constraint violation, until an acceptable schedule is obtained. Since repairs are made to the schedule only to repair violated constraints, rather than to the entire schedule, schedule perturbations are minimized, thereby reducing problems with the dynamic performance of the scheduling system and minimizing disruption to the smooth operation of the activity. All constraints on the scheduling activity can be evaluated simultaneously to produce a solution that is near optimal with respect to all constraints. In particular, consumable resource constraints can be evaluated simultaneously with other constraints such as, for example, reusable resource constraints, temporal constraints, state constraints, milestone constraints and preemptive constraints. The scheduling system of the invention is much quicker than previous scheduling systems that use, for example, constructive scheduling method. The system of the invention can also be easily modified to add, delete or modify constraints. Because of the minimization of schedule perturbation, coupling of all constraints, speed of operation, and ease of modification, the scheduling system of the invention is particularly useful for scheduling applications that require frequent and rapid rescheduling.

A general methodology for designing efficient traffic scheduling and shaping algorithms

Abstract: We introduce a general methodology for designing integrated shaping and scheduling algorithms for packet networks that provide fairness, low end-to-end delay, and low burstiness. The methodology is based on integrating a shaping mechanism with a scheduler from the class of rate-proportional servers (RPS) defined by Stiliadis and Varma (see Proceedings of ACM SIGMETRICS '96, p.104-15, 1996). The resulting algorithms provide an end-to-end delay bound identical to that of weighted fair queueing. Their worst-case fairness, in terms of minimizing the worst-case delay to empty the session backlog, is much superior to that of weighted fair queueing, and equal to the best known for any scheduling algorithm. In addition, the algorithms achieve a level of fairness in the distribution of free bandwidth among competing sessions better than that of weighted fair queueing. We show that, under this framework, even an unfair scheduling algorithm belonging to the RPS class, such as VirtualClock, can yield worst-case fairness identical to that obtained with weighted fair queueing. We also develop an integrated shaper-scheduler that provides optimal output burstiness and is attractive for use in both network adapters and in switches that support traffic re-shaping. We describe an efficient implementation of this integrated shaping and scheduling algorithm with log/sub 2/(V) complexity, where V is the number of sessions sharing the outgoing link.

Rotation scheduling: a loop pipelining algorithm

Abstract: We consider the resource-constrained scheduling of loops with interiteration dependencies. A loop is modeled as a data flow graph (DFG), where edges are labeled with the number of iterations between dependencies. We design a novel and flexible technique, called rotation scheduling, for scheduling cyclic DFGs using loop pipelining. The rotation technique repeatedly transforms a schedule to a more compact schedule. We provide a theoretical basis for the operations based on retiming. We propose two heuristics to perform rotation scheduling and give experimental results showing that they have very good performance.

Log-time algorithms for scheduling single and multiple channel data broadcast

Abstract: With the increasing popularity of portable wireless computers, mechanisms to efficiently transmit information to such clients are of significant interest. The environment under consideration is asymmetric in that the information server has much more bandwidth available, as compared to the clients. It has been proposed that in such systems the server should broadcast the information periodically. A broadcast schedule determines what is broadcast by the server and when. This paper makes the simple, yet useful, observation that the problem of broadcast scheduling is closely related to the problem of fair queueing. Based on this observation, we present a log-time algorithm for scheduling broadcast, based on an existing fair queueing algorithm. This algorithm significantly improves the time-complexity over previously proposed broadcast scheduling algorithms. Also, for environments where different users may be listening to different number of broadcast channels, we present an algorithm to coordinate broadcasts over different channels. Simulation results are presented for proposed algorithms.

A study of integer programming formulations for scheduling problems

Abstract: With the advances of powerful computer capacity and efficient integer programming software, mathematical programming-based scheduling research is beginning to receive more and more attention from researchers. Although it is not an efficient solution method, mathematical programming formulation is a natural way to attack scheduling problems. The purpose of this paper is to present a study of five existing integer programming formulations for job-shop, flow-shop and permutation flow-shop scheduling problems, and a comparison of their model sizes for each particular setting. The results show that Manners model is not only the best formulation for both job-shop and flow-shop problems, but is also the best for the permutation flow-shop problem.

A rolling horizon job shop rescheduling strategy in the dynamic environment

Abstract: In this paper, the job shop scheduling problem in a dynamic environment is studied. Jobs arrive continuously, machines breakdown, machines are repaired and due dates of jobs may change during processing. Inspired by the rolling horizon optimisation method from predictive control technology, a periodic and event-driven rolling horizon scheduling strategy is presented and adapted to continuous processing in a changing environment. The scheduling algorithm is a hybrid of genetic algorithms and dispatching rules for solving the job shop scheduling problem with sequence-dependent set-up time and due date constraints. Simulation results show that the proposed strategy is more suitable for a dynamic job shop environment than the static scheduling strategy.

Scheduling techniques for variable voltage low power designs

Abstract: This paper presents an integer linear programming (ILP) model and a heuristic for the variable voltage scheduling problem. We present the variable voltage scheduling techniques that consider in turn timing constraints alone, resource constraints alone, and timing and resource constraints together for design space exploration. Experimental results show that our heuristic produces results competitive with those of the ILP method in a fraction of the run-time. The results also show that a wide range of design alternatives can be generated using our design space exploration method. Using different cost/delay combinations, power consumption in a single design can differ by as much as a factor of 6 when using mixed 3.3V and 5V supply voltages.

A genetic algorithm-based approach to flexible flow-line scheduling with variable lot sizes

Abstract: Genetic algorithms (GAs) have been used widely for such combinatorial optimization problems as the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and job shop scheduling. In all of these problems there is usually a well defined representation which GA's use to solve the problem. We present a novel approach for solving two related problems-lot sizing and sequencing-concurrently using GAs. The essence of our approach lies in the concept of using a unified representation for the information about both the lot sizes and the sequence and enabling GAs to evolve the chromosome by replacing primitive genes with good building blocks. In addition, a simulated annealing procedure is incorporated to further improve the performance. We evaluate the performance of applying the above approach to flexible flow line scheduling with variable lot sizes for an actual manufacturing facility, comparing it to such alternative approaches as pair wise exchange improvement, tabu search, and simulated annealing procedures. The results show the efficacy of this approach for flexible flow line scheduling.

Move-to-rear list scheduling

Abstract: A new scheduling method and policy for shared (server) resources, such as the CPU or disk memory of a multiprogrammed data processor. The scheduling is referred to as Move-To-Rear List Scheduling and it provides a cumulative service guarantee and well as more traditional guarantees such as fairness (proportional sharing) and bounded delay. In typical operation, a list is maintained for a server of processes seeking service from the server. Processes are admitted to the list only when maximum capacity constraints are not violated, and once on the list, are served in a front-to-back order. After receiving service, or upon the occurrence of other events, the position of the process on the list may be changed.

A comparison of general approaches to multiprocessor scheduling

Abstract: The paper demonstrates the effectiveness of the two phase method of scheduling, in which task clustering is performed prior to the actual scheduling process. Task clustering determines the optimal or near optimal number of processors on which to schedule the task graph. In other words, there is never a need to use more processors (even though they are available) than the number of clusters produced by the task clustering algorithm. The paper also indicates that when task clustering is performed prior to scheduling, load balancing (LB) is the preferred approach for cluster merging. LB is fast, easy to implement, and produces significantly better final schedules than communication traffic minimizing (CTM). In summary, the two phase method consisting of task clustering and load balancing is a simple, yet highly effective strategy for scheduling task graphs on distributed memory parallel architectures.