Distributed and Multiprocessor Scheduling

This chapter discusses CPU scheduling in parallel and distributed systems. CPU scheduling is part of a broader class of resource allocation problems, and is probably the most carefully studied such problem. The main motivation for multiprocessor scheduling is the desire for increased speed in the execution of a workload. Parts of the workload, called tasks, can be spread across several processors and thus be executed more quickly than on a single processor. In this chapter, we will examine techniques for providing this facility. The scheduling problem for multiprocessor systems can be generally stated as \How can we execute a set of tasks T on a set of processors P subject to some set of optimizing criteria C? The most common goal of scheduling is to minimize the expected runtime of a task set. Examples of other scheduling criteria include minimizing the cost, minimizing communication delay, giving priority to certain users\u27 processes, or needs for specialized hardware devices. The scheduling policy for a multiprocessor system usually embodies a mixture of several of these criteria. Section 2 outlines general issues in multiprocessor scheduling and gives background material, including issues specific to either parallel or distributed scheduling. Section 3 describes the best practices from prior work in the area, including a broad survey of existing scheduling algorithms and mechanisms. Section 4 outlines research issues and gives a summary. Section 5 lists the terms defined in this chapter, while sections 6 and 7 give references to important research publications in the area

Multi-processor task scheduling with maximum tardiness criteria.

by Wong Tin-Lam.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 70-73).Abstract --- p.iiAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Scheduling Problems --- p.1Chapter 1.2 --- Literature Review --- p.4Chapter 1.2.1 --- Sized Multiprocessor Task Scheduling --- p.5Chapter 1.2.2 --- Fixed Multiprocessor Task Scheduling --- p.6Chapter 1.2.3 --- Set Multiprocessor Task Scheduling --- p.8Chapter 1.3 --- Organization of Thesis --- p.10Chapter 2 --- Overview --- p.11Chapter 2.1 --- Machine Environment --- p.11Chapter 2.2 --- The Jobs and Their Requirements --- p.12Chapter 2.3 --- Assumptions --- p.13Chapter 2.4 --- Constraints --- p.14Chapter 2.5 --- Objective --- p.15Chapter 2.6 --- An Illustrative Example --- p.17Chapter 2.7 --- NP-Hardness --- p.20Chapter 3 --- Methodology --- p.22Chapter 3.1 --- Dynamic Programming --- p.22Chapter 3.1.1 --- Problem Analysis --- p.24Chapter 3.2 --- Key Idea to solve the problem --- p.27Chapter 3.3 --- Algorithm --- p.28Chapter 3.3.1 --- Phase 1 --- p.28Chapter 3.3.2 --- Phase 2 --- p.37Chapter 4 --- Extensions --- p.46Chapter 4.1 --- "Polynomially Solvable Cases P2 --- p.46Chapter 4.1.1 --- Dynamic Programming --- p.47Chapter 4.2 --- "Set Problem P2/setj,prmp/TmaX" --- p.55Chapter 4.2.1 --- Processing times for set jobs --- p.56Chapter 4.2.2 --- Algorithm --- p.58Chapter 4.3 --- k´ؤMachine Problem with only two types of jobs --- p.64Chapter 5 --- Conclusion and Future Work --- p.67Chapter 5.1 --- Conclusion --- p.67Chapter 5.2 --- Some Future Work --- p.68Bibliography --- p.7

Multi-processor job scheduling with genetic algorithms.

by Hoi Wing, Yung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 56-60).Abstracts in English and Chinese.List of Figures --- p.vList of Tables --- p.viChapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview --- p.1Chapter 1.2 --- Literature Review --- p.3Chapter 1.2.1 --- On the Fixed Multiprocessor Job Scheduling Problems --- p.6Chapter 1.2.2 --- On the Nonfixed Multiprocessor Job Scheduling Problems --- p.8Chapter 1.3 --- Problem Formulation --- p.12Chapter 1.4 --- Organization of the Thesis --- p.13Chapter 2 --- Genetic Algorithms --- p.15Chapter 2.1 --- Basic Concepts --- p.15Chapter 2.2 --- Main components --- p.17Chapter 3 --- A New Genetic Algorithm --- p.24Chapter 3.1 --- Coding --- p.25Chapter 3.1.1 --- Simple Example --- p.28Chapter 3.2 --- Similarity of Chromosomes --- p.30Chapter 3.3 --- Fitness Evaluation --- p.33Chapter 3.4 --- Configurations --- p.35Chapter 3.4.1 --- Parent Selection --- p.35Chapter 3.4.2 --- Multipoint Crossover --- p.36Chapter 3.4.3 --- Multipoint Mutation --- p.38Chapter 3.4.4 --- Replacement Step --- p.38Chapter 3.4.5 --- Termination Criterion --- p.39Chapter 4 --- Experimental Results --- p.41Chapter 4.1 --- Total Weighted Completion Time --- p.41Chapter 4.1.1 --- Lee and Cai's Algorithm --- p.42Chapter 4.1.2 --- Computational Results --- p.44Chapter 4.1.3 --- On the Problem of Minimizing the Total Completion Time --- p.46Chapter 4.2 --- Makespan --- p.48Chapter 4.2.1 --- Mahesh's Algorithms and Linn & Chen's Algorithm --- p.48Chapter 4.2.2 --- Computational Results --- p.52Chapter 5 --- Conclusion --- p.54Bibliography --- p.5

Control of multiclass queueing systems with abandonments and adversarial customers

This thesis considers the defensive surveillance of multiple public areas which are the open, exposed targets of adversarial attacks. We address the operational problem of identifying a real time decision-making rule for a security team in order to minimise the damage an adversary can inflict within the public areas. We model the surveillance scenario as a multiclass queueing system with customer abandonments, wherein the operational problem translates into developing service policies for a server in order to minimise the expected damage an adversarial customer can inflict on the system. We consider three different surveillance scenarios which may occur in realworld security operations. In each scenario it is only possible to calculate optimal policies in small systems or in special cases, hence we focus on developing heuristic policies which can be computed and demonstrate their effectiveness in numerical experiments. In the random adversary scenario, the adversary attacks the system according to a probability distribution known to the server. This problem is a special case of a more general stochastic scheduling problem. We develop new results which complement the existing literature based on priority policies and an effective approximate policy improvement algorithm. We also consider the scenario of a strategic adversary who chooses where to attack. We model the interaction of the server and adversary as a two-person zero-sum game. We develop an effective heuristic based on an iterative algorithm which populates a small set of service policies to be randomised over. Finally, we consider the scenario of a strategic adversary who chooses both where and when to attack and formulate it as a robust optimisation problem. In this case, we demonstrate the optimality of the last-come first-served policy in single queue systems. In systems with multiple queues, we develop effective heuristic policies based on the last-come first-served policy which incorporates randomisation both within service policies and across service policies

Space sharing job scheduling policies for parallel computers

The distinguishing characteristic of space sharing parallel job scheduling policies is that applications are allocated non-overlapping processor subsets. The interference among jobs is reduced, the synchronization delays and message latencies can be predictable, and distinct processors may be allocated to cooperating processes so as to avoid the overhead of context switches associated with traditional time-multiplexing;The processor allocation strategy, the job selection criteria, and workload characteristics are fundamental factors that influence system performance under space sharing. Allocation can be static or dynamic. The processor subset allocated to an application is fixed under static space sharing, whereas it can change during execution under dynamic space sharing. Static allocation can produce more predictable run times, permits a wide range of compiler optimizations (e.g., static data distribution and binding), and avoids the processor releases and reallocations associated with dynamic allocation. Its major problem is that it can induce high processor fragmentation;In this dissertation, alternative static and dynamic space sharing policies that differ in the allocation discipline and the job selection criteria are studied. The results show that significantly superior performance can be achieved under static space sharing if applications can be folded (i.e., allocated fewer processors than they requested). Folding typically increases program efficiency and can reduce processor fragmentation. Policies that increase folding with the system load are proposed and compared to schemes that use unconstrained folding, no folding, and fixed maximum folding factors. The adaptive policies produced higher and more stable system utilization, significantly shorter mean response times, and good fairness curves. However, unconstrained folding resulted in considerably more severe processor fragmentation than no folding. Its advantage is that it exploits the efficiency improvement that typically results when an application is allocated fewer processors. Consequently, it can produce shorter mean response times than no folding under medium to heavy loads;Also because of this efficiency improvement, dynamic policies that reduce waiting times by executing a large number of jobs simultaneously are more promising than schemes that limit the number of active jobs. However, limiting the number of active applications can be the superior approach when folding does not improve application efficiency

Task assignment in parallel processor systems

A generic object-oriented simulation platform is developed in order to conduct experiments on the performance of assignment schemes. The simulation platform, called Genesis, is generic in the sense that it can model the key parameters that describe a parallel system: the architecture, the program, the assignment scheme and the message routing strategy. Genesis uses as its basis a sound architectural representation scheme developed in the thesis. The thesis reports results from a number of experiments assessing the performance of assignment schemes using Genesis. The comparison results indicate that the new assignment scheme proposed in this thesis is a promising alternative to the work-greedy assignment schemes. The proposed scheme has a time-complexity less than those of the work-greedy schemes and achieves an average performance better than, or comparable to, those of the work-greedy schemes. To generate an assignment, some parameters describing the program model will be required. In many cases, accurate estimation of these parameters is hard. It is thought that inaccuracies in the estimation would lead to poor assignments. The thesis investigates this speculation and presents experimental evidence that shows such inaccuracies do not greatly affect the quality of the assignments