Showing papers on "Fair-share scheduling published in 2008"

Planning and Scheduling in Manufacturing and Services

Abstract: Introduction.- Manufacturing Models.- Service Models.- Project Planning and Scheduling.- Machine Scheduling and Job Shop Scheduling.- Scheduling of Flexible Assembly Systems.- Economic Lot Scheduling.- Planning and Scheduling in Supply Chains.- Interval Scheduling, Reservations, and Timetabling.- Planning and Scheduling in Sports and Entertainment.- Planning, Scheduling, and Timetabling in Transportation.- Workforce Scheduling.- Systems Design and Implementation.- Advanced Concepts in Systems Design.- What Lies Ahead?- Mathematical Programming Formulations.- Exact Optimization Methods.- Heuristic Methods.- Constraint Programing Methods.- Selected Scheduuling Sytems.- The LEKIN Systems User's Guide.- Notation.- References.- Index.

Workflow scheduling algorithms for grid computing

Abstract: Workflow scheduling is one of the key issues in the management of workflow execution. Scheduling is a process that maps and manages execution of inter-dependent tasks on distributed resources. It introduces allocating suitable resources to workflow tasks so that the execution can be completed to satisfy objective functions specified by users. Proper scheduling can have significant impact on the performance of the system. In this chapter, we investigate existing workflow scheduling algorithms developed and deployed by various Grid projects.

Variation-Aware Application Scheduling and Power Management for Chip Multiprocessors

Abstract: Within-die process variation causes individual cores in a ChipMultiprocessor (CMP) to differ substantially in both static powerconsumed and maximum frequency supported. In this environment,ignoring variation effects whenscheduling applications or when managing power withDynamic Voltage and Frequency Scaling (DVFS) is suboptimal. This paper proposes variation-aware algorithms for applicationscheduling and power management. One such power managementalgorithm, called {\em LinOpt}, uses linear programmingto find the best voltage and frequency levels for each of thecores in the CMP --- maximizing throughput at a given power budget.In a 20-core CMP, the combination of variation-awareapplication scheduling and {\em LinOpt} increases the averagethroughput by 12--17\% and reduces the average $ED^2$ by 30--38\%--- all relative to using variation-awarescheduling together with a simple extension to Intel's Foxtonpower management algorithm.

Time-Dependent Scheduling

Abstract: Time-dependent scheduling involves problems in which the processing times of jobs depend on when those jobs are started This book is a comprehensive study of complexity results and optimal and suboptimal algorithms concerning time-dependent scheduling in single-, parallel- and dedicated-machine environments In addition to complexity issues and exact or heuristic algorithms which are typically presented in scheduling books, the author also includes more advanced topics such as matrix methods in time-dependent scheduling, and time-dependent scheduling with two criteria The reader should be familiar with basic notions of calculus, discrete mathematics and combinatorial optimization theory, while the book offers introductory material on NP-complete problems, and the basics of scheduling theory The author includes numerous examples, figures and tables, he presents different classes of algorithms using pseudocode, and he completes the book with an extensive bibliography, and author, symbol and subject indexes The book is suitable for researchers working on scheduling, problem complexity, optimization, heuristics and local search algorithms

Opportunistic Scheduling with Reliability Guarantees in Cognitive Radio Networks

Abstract: We develop opportunistic scheduling policies for cognitive radio networks that maximize the throughput utility of the secondary (unlicensed) users subject to maximum collision constraints with the primary (licensed) users. We consider a cognitive network with static primary users and potentially mobile secondary users. We use the technique of Lyapunov Optimization to design an online flow control, scheduling and resource allocation algorithm that meets the desired objectives and provides explicit performance guarantees.

Throughput and Fairness Guarantees Through Maximal Scheduling in Wireless Networks

Abstract: The question of providing throughput guarantees through distributed scheduling, which has remained an open problem for some time, is addressed in this paper. It is shown that a simple distributed scheduling strategy, maximal scheduling, attains a guaranteed fraction of the maximum throughput region in arbitrary wireless networks. The guaranteed fraction depends on the ldquointerference degreerdquo of the network, which is the maximum number of transmitter-receiver pairs that interfere with any given transmitter-receiver pair in the network and do not interfere with each other. Depending on the nature of communication, the transmission powers and the propagation models, the guaranteed fraction can be lower-bounded by the maximum link degrees in the underlying topology, or even by constants that are independent of the topology. The guarantees are tight in that they cannot be improved any further with maximal scheduling. The results can be generalized to end-to-end multihop sessions. Finally, enhancements to maximal scheduling that can guarantee fairness of rate allocation among different sessions, are discussed.

A Task Scheduling Algorithm Based on PSO for Grid Computing

Abstract: Grid computing is a high performance computing environment to solve larger scale computational demands. Grid computing contains resource management, task scheduling, security problems, information management and so on. Task scheduling is a fundamental issue in achieving high performance in grid computing systems. However, it is a big challenge for efficient scheduling algorithm design and implementation. In this paper, a heuristic approach based on particle swarm optimization algorithm is adopted to solving task scheduling problem in grid environment. Each particle is represented a possible solution, and the position vector is transformed from the continuous variable to the discrete variable. This approach aims to generate an optimal schedule so as to get the minimum completion time while completing the tasks. The results of simulated experiments show that the particle swarm optimization algorithm is able to get the better schedule than genetic algorithm.

A measurement study of interference modeling and scheduling in low-power wireless networks

Abstract: Accurate interference models are important for use in transmission scheduling algorithms in wireless networks. In this work, we perform extensive modeling and experimentation on two 20-node TelosB motes testbeds -- one indoor and the other outdoor -- to compare a suite of interference models for their modeling accuracies. We first empirically build and validate the physical interference model via a packet reception rate vs. SINR relationship using a measurement driven method. We then similarly instantiate other simpler models, such as hop-based, range-based, protocol model, etc. The modeling accuracies are then evaluated on the two testbeds using transmission scheduling experiments. We observe that while the physical interference model is the most accurate, it is still far from perfect, providing a 90-percentile error about 20-25% (and 80 percentile error 7-12%), depending on the scenario. The accuracy of the other models is worse and scenario-specific. The second best model trails the physical model by roughly 12-18 percentile points for similar accuracy targets. Somewhat similar throughput performance differential between models is also observed when used with greedy scheduling algorithms. Carrying on further, we look closely into the the two incarnations of the physical model -- 'thresholded' (conservative, but typically considered in literature) and 'graded' (more realistic). We show via solving the one shot scheduling problem, that the graded version can improve `expected throughput' over the thresholded version by scheduling imperfect links.

Throughput Guarantees Through Maximal Scheduling in Wireless Networks

Abstract: We address the question of providing throughput guarantees through distributed scheduling, which has remained an open problem for some time. We consider a simple distributed scheduling strategy, maximal scheduling, and prove that it attains a guaranteed fraction of the maximum throughput region in arbitrary wireless networks. The guaranteed fraction depends on “interference degree” of the network which is the maximum number of sessions that interfere with any given session in the network and do not interfere with each other. Depending on the nature of communication, the transmission powers and the propagation models, the guaranteed fraction can be lower bounded by the maximum link degrees in the underlying topology, or even by constants that are independent of the topology. The guarantees also hold in networks with multicast communication and an arbitrary number of frequencies. We prove that the guarantees are tight in that they can not be improved any further with maximal scheduling.

Hierarchical Scheduling Framework for Virtual Clustering of Multiprocessors

Abstract: Scheduling of sporadic task systems on multiprocessor platforms is an area which has received much attention in the recent past. It is widely believed that finding an optimal scheduler is hard, and therefore most studies have focused on developing algorithms with good utilization bounds. These algorithms can be broadly classified into two categories: partitioned scheduling in which tasks are statically assigned to individual processors, and globalscheduling in which each task is allowed to execute on any processor in the platform. In this paper we consider a third, more general, approach called cluster-based scheduling. In this approach each task is statically assigned to a processor cluster, tasks in each cluster areglobally scheduled among themselves, and clusters in turn are scheduled on the multiprocessor platform. We develop techniques to support such cluster-based scheduling algorithms, and also consider properties that minimize processor utilization of individual clusters. Since neither partitioned nor global strategies dominate over the other, cluster-based scheduling is a natural direction for research towards achieving improved utilization bounds.

The performance of bags-of-tasks in large-scale distributed systems

Abstract: Ever more scientists are employing large-scale distributed systems such as grids for their computational work, instead of tightly coupled high-performance computing systems. However, while these distributed systems are more cost-effective, their heterogeneity in terms of hardware, software, and systems administration, and the lack of accurate resource information leads to inefficient scheduling. In addition, and in contrast to the workloads of tightly coupled high-performance computing systems, a large part of the workloads submitted to these distributed systems consists of large sets (bags) of sequential tasks. Therefore, a realistic performance analysis of scheduling bags-of-tasks in large-scale distributed systems is important. Towards this end, we introduce in this paper a realistic workload model for bags-of-tasks, and we explore through trace-based simulations the design space of scheduling bags-of-tasks. Finally, we identify three new scheduling policies that use only inaccurate information when scheduling, and we compare them against known classes of proposed scheduling policies.

Resource-Constrained Project Scheduling: Models, Algorithms, Extensions and Applications

Abstract: This title presents a large variety of models and algorithms dedicated to the resource-constrained project scheduling problem (RCPSP), which aims at scheduling at minimal duration a set of activities subject to precedence constraints and limited resource availabilities In the first part, the standard variant of RCPSP is presented and analyzed as a combinatorial optimization problem Constraint programming and integer linear programming formulations are given Relaxations based on these formulations and also on related scheduling problems are presented Exact methods and heuristics are surveyed Computational experiments, aiming at providing an empirical insight on the difficulty of the problem, are provided The second part of the book focuses on several other variants of the RCPSP and on their solution methods Each variant takes account of real-life characteristics which are not considered in the standard version, such as possible interruptions of activities, production and consumption of resources, cost-based approaches and uncertainty considerations The last part presents industrial case studies where the RCPSP plays a central part Applications are presented in various domains such as assembly shop and rolling ingots production scheduling, project management in information technology companies and instruction scheduling for VLIW processor architectures

Dynamic Thermal Management through Task Scheduling

Abstract: The evolution of microprocessors has been hindered by their increasing power consumption and the heat generation speed on-die. High temperature impairs the processor's reliability and reduces its lifetime. While hardware level dynamic thermal management (DTM) techniques, such as voltage and frequency scaling, can effectively lower the chip temperature when it surpasses the thermal threshold, they inevitably come at the cost of performance degradation. We propose an OS level technique that performs thermal- aware job scheduling to reduce the number of thermal trespasses. Our scheduler reduces the amount of hardware DTMs and achieves higher performance while keeping the temperature low. Our methods leverage the natural discrepancies in thermal behavior among different workloads, and schedule them to keep the chip temperature below a given budget. We develop a heuristic algorithm based on the observation that there is a difference in the resulting temperature when a hot and a cool job are executed in a different order. To evaluate our scheduling algorithms, we developed a lightweight runtime temperature monitor to enable informed scheduling decisions. We have implemented our scheduling algorithm and the entire temperature monitoring framework in the Linux kernel. Our proposed scheduler can remove 10.5-73.6% of the hardware DTMs in various combinations of workloads in a medium thermal environment. As a result, the CPU throughput was improved by up to 7.6% (4.1% on average) even under a severe thermal environment.

Optimal and Distributed Scheduling for Multicell Capacity Maximization

Abstract: We address the problem of multicell co-channel scheduling in view of mitigating interference in a wireless data network with full spectrum reuse. The centralized joint multicell scheduling optimization problem, based on the complete co-channel gain information, has so far been justly considered impractical due to complexity and real-time cell-to-cell signaling overhead. However, we expose here the following remarkable result for a large network with a standard power control policy. The capacity maximizing joint multicell scheduling problem admits a simple and fully distributed solution. This result is proved analytically for an idealized network. From the constructive proof, we propose a practical algorithm that is shown to achieve near maximum capacity for realistic cases of simulated networks of even small sizes.

An on-line learning algorithm for energy efficient delay constrained scheduling over a fading channel

Abstract: In this paper, we consider the problem of energy efficient scheduling under average delay constraint for a single user fading channel. We propose a new approach for on-line implementation of the optimal packet scheduling algorithm. This approach is based on reformulating the value iteration equation by introducing a virtual state called post-decision state. The resultant value iteration equation becomes amenable to online implementation based on stochastic approximation. This approach has an advantage that an explicit knowledge of the probability distribution of the channel state as well as the arrivals is not required for the implementation. We prove that the on-line algorithm indeed converges to the optimal policy.

Task Scheduling for Control Oriented Requirements for Cyber-Physical Systems

Abstract: The wide applications of cyber-physical systems (CPS) call for effective design strategies that optimize the performance of both computing units and physical plants.We study the task scheduling problem for a class of CPS whose behaviors are regulated by feedback control laws. We co-design the control law and the task scheduling algorithm for predictable performance and power consumption for both the computing and the physical systems. We use a typical example, multiple inverted pendulums controlled by one processor, to illustrate our method.

Polyhedral Approaches to Machine Scheduling

Abstract: We provide a review and synthesis of polyhedral approaches to machine scheduling problems. The choice of decision variables is the prime determinant of various formulations for such problems. Constraints, such as facet inducing inequalities for corresponding polyhedra, are often needed, in addition to those just required for the validity of the initial formulation, in order to obtain useful lower bounds and structural insights. We review formulations based on time–indexed variables; on linear ordering, start time and completion time variables; on assignment and positional date variables; and on traveling salesman variables. We point out relationship between various models, and provide a number of new results, as well as simplified new proofs of known results. In particular, we emphasize the important role that supermodular polyhedra and greedy algorithms play in many formulations and we analyze the strength of the lower and upper bounds obtained from different formulations and relaxations. We discuss separation algorithms for several classes of inequalities, and their potential applicability in generating cutting planes for the practical solution of such scheduling problems. We also review some recent results on approximation algorithms based on some of these formulations.

Robustness and stability measures for scheduling: single-machine environment

Abstract: This paper addresses the issue of finding robust and stable schedules with respect to random disruptions. Specifically, two surrogate measures for robustness and stability are developed. The proposed surrogate measures, which consider both busy and repair time distributions, are embedded in a tabu-search-based scheduling algorithm, which generates schedules in a single-machine environment subject to machine breakdowns. The performance of the proposed scheduling algorithm and the surrogate measures are tested under a wide range of experimental conditions. The results indicate that one of the proposed surrogate measures performs better than existing methods for the total tardiness and total flowtime criteria in a periodic scheduling environment. A comprehensive bibliography is also presented.

Simple Methods for Shift Scheduling in Multiskill Call Centers

Abstract: This paper introduces a new method for shift scheduling in multiskill call centers. The method consists of two steps. First, staffing levels are determined, and next, in the second step, the outcomes are used as input for the scheduling problem. The scheduling problem relies on a linear programming model that is easy to implement and has short computation times, i.e., a fraction of a second. Therefore, it is useful for different purposes and it can be part of an iterative procedure: for example, one that combines shifts into rosters.

Enhancing the Data Collection Rate of Tree-Based Aggregation in Wireless Sensor Networks

Abstract: What is the fastest rate at which we can collect a stream of aggregated data from a set of wireless sensors organized as a tree? We explore a hierarchy of techniques using realistic simulation models to address this question. We begin by considering TDMA scheduling on a single channel, reducing the original problem to minimizing the number of time slots needed to schedule each link of the aggregation tree. The second technique is to combine the scheduling with transmission power control to reduce the effects of interference. To better cope with interference, we then study the impact of utilizing multiple frequency channels by introducing a simple receiver-based frequency and time scheduling approach. We find that for networks of about a hundred nodes, the use of multi-frequency scheduling can suffice to eliminate most of the interference. The data collection rate then becomes limited not by interference, but by the maximum degree of the routing tree. Therefore we consider finally how the data collection rate can be further enhanced by the use of degree-constrained routing trees. Considering deployments at different densities, we show that these enhancements can improve the streaming aggregated data collection by as much as 10 times compared to the baseline of single-channel data collection over non-degree constrained routing trees. Addition to our primary conclusion, in the frequency scheduling domain we evaluate the impact of different interference models on the scheduling performance and give topology-specific bounds on time slot and frequency channel requirements.

Just-in-Time Scheduling for Multichannel EPONs

Abstract: We investigate optical network unit (ONU) grant scheduling techniques for multichannel Ethernet passive optical networks (EPONs), such as wavelength division multiplexed (WDM) EPONs. We take a scheduling theoretic approach to solving the grant scheduling problem. We introduce a two-layer structure of the scheduling problem and investigate techniques to be used at both layers. We present an extensive ONU grant scheduling simulation study that provides: 1) insight into the nature of the ONU grant scheduling problem and 2) indication of which scheduling techniques are best for certain conditions. We find that the choice of scheduling framework has typically the largest impact on average queueing delay and achievable channel utilization. An offline scheduling framework is not work conserving and consequently wastes channel resources while waiting for all ONU REPORT messages before making access decisions. An online scheduling framework, although work conserving, does not provide the best performance since scheduling decisions are made with the information contained in a single ONU REPORT. We propose a novel online just-in-time (JIT) scheduling framework that is work conserving while increasing scheduling control by allowing the channel availability to drive the scheduling process. In online JIT, multiple ONU REPORTs can be considered together when making scheduling decisions, resulting in lower average queueing delay under certain conditions and a more effective service differentiation of ONUs.