Average Weighted Completion Time Research Articles

Scheduling MapReduce Jobs on Identical and Unrelated Processors

We consider non-preemptive scheduling of MapReduce jobs consisting of multiple map-reduce rounds so as to minimize their average weighted completion time on identical or unrelated processor environments. For identical processors, we present LP-based O(1)-approximation algorithms, while for unrelated processors the approximation ratio naturally depends on the maximum number of rounds of any job (which is a small constant in practice). For the single-round case, we substantially improve on previously best known approximation ratios, while also we introduce into our model the crucial cost of the data shuffle phase, i.e., the cost for the transmission of intermediate data from Map to Reduce tasks. Finally, we evaluate our algorithms via simulations in the general case of unrelated processors, comparing them with a lower bound on the optimal cost of the problem as well as with a fast algorithm which combines a simple online assignment of tasks to processors with a standard scheduling policy. As we observe, for random instances that capture data locality issues, our algorithm achieves an excellent average performance.

On the relationship between combinatorial and LP-based lower bounds for NP-hard scheduling problems

Enumerative approaches to solving optimization problems, such as branch and bound, require a subroutine that produces a lower bound on the value of the optimal solution. In the domain of scheduling problems the requisite lower bound has typically been derived from either the solution to a linear-programming (LP) relaxation of the problem or the solution to a combinatorial relaxation. In this paper we investigate, from a theoretical perspective, the relationship between several LP-based lower bounds and combinatorial lower bounds for three scheduling problems in which the goal is to minimize the average weighted completion time of the jobs scheduled. We establish a number of facts about the relationship between these different sorts of lower bounds, including the equivalence of certain LP-based lower bounds for these problems to combinatorial lower bounds used in successful branch-and-bound algorithms. As a result, we obtain the first worst-case analysis of the quality of the lower bounds delivered by these combinatorial relaxations.

Bicriteria approximation algorithms for scheduling problems with communications delays

We study the problem of simultaneously minimizing the makespan and the average weighted completion time for the precedence multiprocessor constrained scheduling problem with unit execution times and unit communication delays, known as the UET--UCT problem (Munier and Konig, Operations Research, 45(1), 145--148 (1997)). We propose a simple (16/9, 16/9)-approximation algorithm for the problem with an unrestricted number of machines. We improve our algorithm by adapting a technique first introduced by Aslam et al. (Proceedings of ACM-SODA, pp. 846--847, 1999) and provide a (1.745, 1.745)-approximate solution. For the considered scheduling problem, we prove the existence of a (1.445, 1.445)-approximate solution, improving the generic existence result of Aslam et al. (Proceedings of ACM-SODA, pp. 846--847, 1999). Also notice that our results for the case of an unrestricted number of processors hold for the more general scheduling problem with small communication delays (SCT problem), and for two other classical optimality criteria: maximum lateness and weighted lateness. Finally, we propose an approximation algorithm for the UET--UCT problem with a restricted number of processors.

An Experimental Study of LP-Based Approximation Algorithms for Scheduling Problems

Recently there has been much progress on the design of approximation algorithms for a variety of scheduling problems in which the goal is to minimize the average weighted completion time of the jobs scheduled. Many of these approximation algorithms have been inspired by polyhedral formulations of the scheduling problems and their use in computing optimal solutions to small instances. In this paper we demonstrate that the progress in the design and analysis of approximation algorithms for these problems also yields techniques with improved computational efficacy. Specifically, we give a comprehensive experimental study of a number of these approximation algorithms for 1|rj∑wjCj, the problem of scheduling jobs with release dates on one machine so as to minimize the average weighted completion time of the jobs scheduled. We study both the quality of lower bounds given for this problem by different linear-programming relaxations and combinatorial relaxations, and the quality of upper bounds delivered by a number of approximation algorithms based on them. The best algorithms, on almost all instances, come within a few percent of the optimal average weighted completion time. Furthermore, we show that this can usually be achieved with O(n log n) computation. In addition we observe that on most kinds of synthetic data used in experimental studies a simple greedy heuristic, used in successful combinatorial branch-and-bound algorithms for the problem, outperforms (on average) all of the LP-based heuristics. We identify, however, other classes of problems on which the LP-based heuristics are superior and report on experiments that give a qualitative sense of the range of dominance of each. We consider the impact of local improvement on the solutions as well. We also consider the performance of the algorithms for the average weighted flow-time criterion, which, although equivalent to average weighted completion time at optimality, is provably much harder to approximate. Nonetheless, we demonstrate that for most instances we consider that the algorithms give very good results for this criterion as well. Finally, we extend the techniques to a rather different and more complex problem that arises from an actual manufacturing application: resource-constrained project scheduling. In this setting as well, the techniques yield algorithms with improved performance; we give the best-known solutions for a set of instances provided by BASF AG, Germany.

Approximation results for a bicriteria job scheduling problem on a single machine without preemption

We study the problem of minimizing the average weighted completion time on a single machine under the additional constraint that the sum of completion times does not exceed a given bound B ( 1 | ∑ C j ⩽ B | ∑ w j C j ) for which we propose a ( 2 , 1 ) -approximation algorithm. We also address the problem 1 | ∑ c j C j ⩽ B | ∑ w j C j for which we present a ( 2 , 2 ) -approximation algorithm. After showing that the problem of minimizing two different sums of weighted completion times is intractable, we present an algorithm which computes a ( 2 ( 1 + ɛ ) , 1 ) (respectively ( 2 ( 1 + ɛ ) , 2 ) )-approximate Pareto curve for the problem 1 ‖ ( ∑ C j , ∑ w j C j ) (respectively 1 ‖ ( ∑ c j C j , ∑ w j C j ) ).

Single-Machine Scheduling with Precedence Constraints

We discuss the problem of sequencing precedence-constrained jobs on a single machine to minimize the average weighted completion time. This problem has attracted much attention in the mathematical programming community since Sidney’s pioneering work in 1975 (Sidney, J. B. 1975. Decomposition algorithms for single machine scheduling with precedence relations and deferral costs. Operations Research 23 283–298). We look at the problem from a polyhedral perspective and uncover a relation between Sidney’s decomposition theorem and different linear programming relaxations. More specifically, we present a generalization of Sidney’s result, which particularly allows us to reason that virtually all known 2-approximation algorithms are consistent with his decomposition. Moreover, we establish a connection between the single-machine scheduling problem and the vertex cover problem. Indeed, in the special case of series-parallel precedence constraints, we prove that the sequencing problem can be seen as a special case of the vertex cover problem. We also argue that this result is true for general precedence constraints if one can show that a certain integer program represents a valid formulation of the sequencing problem. Finally, we give a 3/2-approximation algorithm for two-dimensional partial orders, and we also provide a characterization of the active inequalities of a linear programming relaxation in completion time variables.

Single Machine Scheduling with Precedence Constraints

We discuss the problem of sequencing precedence-constrained jobs on a single machine to minimize the average weighted completion time. This problem has attracted much attention in the mathematical programming community since Sidney's pioneering work in 1975. We look at the problem from a polyhedral perspective and uncover a relation between Sidney's decomposition theorem and different linear programming relaxations. More specifically, we present a generalization of Sidney's result, which particularly allows us to reason that virtually all known 2-approximation algorithms comply with his decomposition. Moreover, we establish a connection between the single-machine scheduling problem and the vertex cover problem. Indeed, in the special case of series-parallel precedence constraints, we prove that the sequencing problem can be seen as a special case of the vertex cover problem. We also argue that this result is true for general precedence constraints if one can show that a certain integer program represents a valid formulation of the sequencing problem. Finally, we give a 3/2-approximation algorithm for two-dimensional partial orders, and we also provide a characterization of the active inequalities of a linear programming relaxation in completion time variables.

Practical comparison of approximation algorithms for scheduling problems

In this paper we consider an experimental study of approximation algorithms for scheduling problems in parallel machines minimizing the average weighted completion time. We implemented approximation algorithms for the following problems: P|r j|sigmaCj, P||sigmaw jCj, P|r j|sigmaw jCj, R||sigmaw jCj and R|r j|sigmaw jCj. We generated more than 1000 tests over more than 200 different instances and present some practical aspects of the implemented algorithms. We also made an experimental comparison on two lower bounds based on the formulations used by the algorithms. The first one is a semidefinite formulation for the problem R||sigmaw jCj and the other one is a linear formulation for the problem R|r j|sigmaw jCj. For all tests, the algorithms obtained very good results. We notice that algorithms using more refined techniques, when compared to algorithms with simple strategies, do not necessary lead to better results. We also present two heuristics, based on approximation algorithms, that generate solutions with better quality in almost all instances considered.

Scheduling to Minimize Average Completion Time Revisited: Deterministic On-line Algorithms

We consider the scheduling problem of minimizing the average weighted completion time on identical parallel machines when jobs are arriving over time. For both the preemptive and the nonpreemptive setting, we show that straightforward extensions of Smith's ratio rule yield smaller competitive ratios compared to the previously best-known deterministic on-line algorithms, which are (4+epsilon)-competitive in either case. Our preemptive algorithm is 2-competitive, which actually meets the competitive ratio of the currently best randomized on-line algorithm for this scenario. Our nonpreemptive algorithm has a competitive ratio of 3.28. Both results are characterized by a surprisingly simple analysis; moreover, the preemptive algorithm also works in the less clairvoyant environment in which only the ratio of weight to processing time of a job becomes known at its release date, but neither its actual weight nor its processing time. In the corresponding nonpreemptive situation, every on-line algorithm has an unbounded competitive ratio.

A fully combinatorial 2-approximation algorithm for precedence-constrained scheduling a single machine to minimize average weighted completion time

We study the problem of scheduling a single machine with the precedence relation on the set of jobs to minimize average weighted completion time. The problem is strongly NP-hard. The first combinatorial 2-approximation algorithm for this scheduling problem was developed by the author in 1992 (in fact, this algorithm solves a more general problem). Here we give an efficient implementation of this algorithm and show that its running time is O(n MF(n,m)) , where n is the number of jobs, m is the number of arcs in the precedence relation graph, and MF( n, m) denotes the complexity of the maximal flow computation in a network with n nodes and m arcs. Thus, our algorithm is competitive to the best 2-approximation algorithms for this scheduling problem developed starting since 1997.

Single Machine Scheduling with Release Dates

We consider the scheduling problem of minimizing the average weighted completion time of n jobs with release dates on a single machine. We first study two linear programming relaxations of the problem, one based on a time-indexed formulation, the other on a completion-time formulation. We show their equivalence by proving that a O(n log n) greedy algorithm leads to optimal solutions to both relaxations. The proof relies on the notion of mean busy times of jobs, a concept which enhances our understanding of these LP relaxations. Based on the greedy solution, we describe two simple randomized approximation algorithms, which are guaranteed to deliver feasible schedules with expected objective function value within factors of 1.7451 and 1.6853, respectively, of the optimum. They are based on the concept of common and independent $\alpha$-points, respectively. The analysis implies in particular that the worst-case relative error of the LP relaxations is at most 1.6853, and we provide instances showing that it is at least $e/(e-1) \approx 1.5819$. Both algorithms may be derandomized; their deterministic versions run in O(n2) time. The randomized algorithms also apply to the on-line setting, in which jobs arrive dynamically over time and one must decide which job to process without knowledge of jobs that will be released afterwards.

Scheduling Unrelated Machines by Randomized Rounding

We present a new class of randomized approximation algorithms for unrelated parallel machine scheduling problems with the average weighted completion time objective. The key idea is to assign jobs randomly to machines with probabilities derived from an optimal solution to a linear programming (LP) relaxation in time-indexed variables. Our main results are a $(2+\varepsilon)$-approximation algorithm for the model with individual job release dates and a $(3/2+\varepsilon)$-approximation algorithm if all jobs are released simultaneously. We obtain corresponding bounds on the quality of the LP relaxation. It is an interesting implication for identical parallel machine scheduling that jobs are randomly assigned to machines, in which each machine is equally likely. In addition, in this case the algorithm has running time O(n log n) and performance guarantee 2. Moreover, the approximation result for identical parallel machine scheduling applies to the on-line setting in which jobs arrive over time as well, with no difference in performance guarantee.

A PTAS for the average weighted completion time problem on unrelated machines

We study the problem of scheduling n independent weighted jobs on a constant number of unrelated parallel machines so as to minimize the weighted sum of job completion times Rm∣∣∑wjCj. We present an O(n log n) time approximation scheme for the non-preemptive case of this problem. Notice that when the number of machines is not a constant (i.e. R∣∣∑wjCj) then no PTAS is possible, unless =. Copyright © 2000 John Wiley & Sons, Ltd.

Scheduling series–parallel orders subject to 0/1-communication delays

We consider the problem P∞|prec, c ij ∈{0,1}| κ of scheduling jobs with arbitrary processing times on sufficiently many parallel processors subject to series–parallel precedence constraints and 0/1-communication delays in order to minimize a regular performance measure κ. Such schedules without processor restrictions are used for generating approximate solutions for a restricted number of processors. For unit time communication delays we derive polynomial algorithms to construct optimal schedules when the performance measure κ is the makespan or the average weighted completion time. For n jobs and r precedence constraints, the run times of these algorithms are O(n+r) and O(n 3) , respectively. On the other hand, both problems are shown to be NP-hard in the same model for 0/1-communication delays.

Task Scheduling in Networks

Scheduling a set of tasks on a set of machines so as to yield an efficient schedule is a basic problem in computer science and operations research. Most of the research on this problem incorporates the potentially unrealistic assumption that communication between the different machines is instantaneous. In this paper we remove this assumption and study the problem of network scheduling, where each job originates at some node of a network, and in order to be processed at another node must take the time to travel through the network to that node. Our main contribution is to give approximation algorithms and hardness proofs for fully general forms of the fundamental problems in network scheduling. We consider two basic scheduling objectives: minimizing the makespan and minimizing the average completion time. For the makespan, we prove small constant factor hardness-to-approximate and approximation results. For the average completion time, we give a log-squared approximation algorithm for the most general form of the problem. The techniques used in this approximation are fairly general and have several other applications. For example, we give the first nontrivial approximation algorithm to minimize the average weighted completion time of a set of jobs on related or unrelated machines, with or without a network.