Variable neighborhood search (VNS) is a recent metaheuristic for solving combinatorial and global optimization problems whose basic idea is systematic change of neighborhood within a local search. In this survey paper we present basic rules of VNS and some of its extensions. Moreover, applications are briefly summarized. They comprise heuristic solution of a variety of optimization problems, ways to accelerate exact algorithms and to analyze heuristic solution processes, as well as computer-assisted discovery of conjectures in graph theory.

Devices]: Modes of Computation---Parallelism and concurrency General Terms: Algorithms, Design, Performance, Theory Additional Key Words and Phrases: Automatic parallelization, DAG, multiprocessors, parallel processing, software tools, static scheduling, task graphs This research was supported by the Hong Kong Research Grants Council under contract numbers HKUST 734/96E, HKUST 6076/97E, and HKU 7124/99E. Authors' addresses: Y.-K. Kwok, Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong; email: ykwok@eee.hku.hk; I. Ahmad, Department of Computer Science, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. Permission to make digital / hard copy of part or all of this work for personal or classroom use is granted without fee provided that the copies are not made or distributed for profit or commercial advantage, the copyright notice, the title of the publication, and its date appear, and notice is given that copying is by permission of the ACM, Inc. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and / or a fee. 2000 ACM 0360-0300/99/1200--0406 $5.00 ACM Computing Surveys, Vol. 31, No. 4, December 1999 1.

We present a user-level thread scheduler for shared-memory multiprocessors, and we analyze its performance under multiprogramming. We model multiprogramming with two scheduling levels: our scheduler runs at user-level and schedules threads onto a fixed collection of processes, while below, the operating system kernel schedules processes onto a fixed collection of processors. We consider the kernel to be an adversary, and our goal is to schedule threads onto processes such that we make efficient use of whatever processor resources are provided by the kernel. Our thread scheduler is a non-blocking implementation of the work-stealing algorithm. For any multithreaded computation with work ¢¤ £ and critical-path length ¢¦ ¥ , and for any number § of processes, our scheduler executes the computation in expected time ¨�©�¢�£���§¤����¢�¥�§���§¤�� � , where §� � is the average number of processors allocated to the computation by the kernel. This time bound is optimal to within a constant factor, and achieves linear speedup whenever § is small relative to the parallelism 1

Advances in hardware and software technologies have made it possible to deploy parallel applications over increasingly large sets of distributed resources. Consequently, the study of scheduling algorithms for such applications has been an active area of research. Given the nature of most scheduling problems one must resort to simulation to effectively evaluate and compare their efficacy over a wide range of scenarios. It has thus become necessary to simulate those algorithms for increasingly complex distributed, dynamic, heterogeneous environments. In this paper we present Simgrid, a simulation toolkit for the study of scheduling algorithms for distributed application. This paper gives the main concepts and models behind Simgrid, describes its API and highlights current implementation issues. We also give some experimental results and describe work that builds on Simgrid's functionalities. 1.

It is now more than a quarter of a century since researchers started publishing papers on mapping strategies for distributing computation across the computation resource of multiprocessor systems. There exists a large body of literature on the subject, but there is no commonly-accepted framework whereby results in the field can be compared. Nor is it always easy to assess the relevance of a new result to a particular problem. Furthermore, changes in parallel computing technology have made some of the earlier work of less relevance to current multiprocessor systems. Versions of the mapping problem are classified, and research in the field is considered in terms of its relevance to the problem of programming currently available hardware in the form of a distributed memory multiple instruction stream multiple data stream computer: a multicomputer.

A real-time application is typically composed of a number of cooperating activities that must execute within specific time intervals. Since there are usually more activities to be executed than there are processors on which to execute them, several activities must share a single processor. Necessarily, satisfying the activities' timing constraints is a prime concern in making the scheduling decisions for that processor.

The problem of scheduling a parallel program represented by a weighted directed acyclic graph (DAG) to a set of homogeneous processors for minimizing the completion time of the program has been extensively studied. The NP-completeness of the problem has stimulated researchers to propose a myriad of heuristic algorithms. While most of these algorithms are reported to be efficient, it is not clear how they compare against each other. A meaningful performance evaluation and comparison of these algorithms is a complex task and it must take into account a number of issues. First, most scheduling algorithms are based upon diverse assumptions, making the performance comparison rather purposeless. Second, there does not exist a standard set of benchmarks to examine these algorithms. Third, most algorithms are evaluated using small problem sizes, and, therefore, their scalability is unknown. In this paper, we first provide a taxonomy for classifying various algorithms into distinct categories a...

Abstract. Given a set 3- = {T1, T2, , T,} of tasks, each T/having execution time 1, an integer start-time si->0 and adeadlinedi> 0, alongwithprecedence constraintsamongthe tasks,weexamine the problem of determining whether there exists a schedule on two identical processors that executes each task in the time intervalbetween its start-timeand deadline.We present an O(n3) algorithm that constructs such a schedule whenever one exists. The algorithm may also be used in a binary search mode to find the shortest such schedule or to find a schedule that minimizesmaximum "tardiness".A number of natural extensions of this problem are seen to be NP-complete and hence probably intractable. Key words, multiprocessing systems, scheduling algorithms, NP-complete problems 1. Introduction. Since publication of the book Theory ofScheduling [4] by Conway, Maxwell, andMiller in 1967, considerableprogresshasbeenmade inthe mathematical analysis of abstract multiprocessing systems. One combinatorial model which is central to much of this work consists of a numberm of identical, independent processors, a finite set- {T1, T2, Tn} of tasks to be executed,

Introduction Scheduling theory is concerned with the optimal allocation of scarce resources to activities over time. The practice of this field dates to the first time two humans contended for a shared resource and developed a plan to share it without bloodshed. The theory of the design of algorithms for scheduling is younger, but still has a significant history---the earliest papers in the field were published more than forty years ago. Scheduling problems arise in a variety of settings, as is illustrated by the following examples: Example 1: Consider the central processing unit of a computer that must process a sequence of jobs that arrive over time. In what order should the jobs be processed in order to minimize, on average, the time that a job is in the system from arrival to completion? Example 2: Consider a team of five astronauts preparing for the reentry of their space shuttle into the at

In integer linear programming (ILP), formulating a "good" model is of crucial importance to solving that model [1]. In this paper, we begin with a mathematical analysis of the structure of the assignment, timing, and resource constraints in high-level synthesis, and then evaluate the structure of the scheduling polytope described by these constraints. We then show how the structure of the constraints can be exploited to develop a well-structured ILP formulation, which can serve as a solid theoretical foundation for future improvement. As a start in that direction, we also present two methods to further tighten the formulation. The contribution of this paper is twofold: (1) it provides the first in-depth formal analysis of the structure of the constraints, and it shows how to exploit that structure in a well-designed ILP formulation, and (2) it shows how to further improve a well-structured formulation by adding new valid inequalities. I. Introduction The scheduling problem in high-le...