A Tighter Extra-Resource Analysis of Online Deadline Scheduling

This paper is concerned with online algorithms for scheduling jobs with deadlines on a single processor. It has been known for long that unless the system is underloaded, no online scheduling algorithm can be 1-competitive, i.e., matching the performance of the optimal offline algorithm. Nevertheless, recent work has revealed that some online algorithms using a moderately faster processor (or extra processors) can guarantee very competitive performance Kalyanasundaram and Pruhs, 2000 or even be 1-competitive Koo et al., 2002; Lam and To, 2001. This paper takes a further step to investigate online scheduling algorithms with an even higher performance guarantee (i.e., better than 1-competitive algorithms) and in particular, presents an extra-resource analysis of the earliest-deadline-first strategy (EDF) with respect to such a higher performance guarantee.A preliminary version of this paper has been accepted by The Australian Theory Symposium on Computing, 2004.This research was supported in part by Hong Kong RGC Grant HKU-7024/01E.     

Note: A note on the production and inventory systems taking account of time value

This paper deals with the optimal inventory decisions taking account of time value by applying the concept of the present value method, and modifies the bounds for the optimal cycle time described in Chung et al. (Production Planning & Control, 1998, 9, 580–584). A modified algorithm to compute the optimal cycle time is developed to improve the paper of Chung et al. Finally, a numerical example is given to illustrate the algorithm discussed in this paper.     

Competitive analysis for make-to-order scheduling with reliable lead time quotation

This paper makes extended studies on the discrete problem of online scheduling and reliable lead time quotation (discrete Q-SLTQ) introduced by Keskinocak et al. (Manag. Sci. 47(2):264–279, 2001). We first relax the assumption on revenue function from a linear decreasing function to any decreasing function. We present an online deterministic strategy which is optimal in competitiveness for concave revenue functions. The above results are further extended to the continuous Q-SLTQ model where orders are released at arbitrary time points. For the discrete Q-SLTQ problem, if orders are with nonuniform lengths, we prove the nonexistence of online strategies with bounded competitive ratios; otherwise if orders are with unit length but various weights, we present an optimal online strategy.     

Almost optimal solutions for bin coloring problems

In this paper we study two interesting bin coloring problems: Minimum Bin Coloring Problem (MinBC) and Online Maximum Bin Coloring Problem (OMaxBC), motivated from several applications in networking. For the MinBC problem, we present two near linear time approximation algorithms to achieve almost optimal solutions, i.e., no more than OPT+2 and OPT+1 respectively, where OPT is the optimal solution. For the OMaxBC problem, we first introduce a deterministic 2-competitive greedy algorithm, and then give lower bounds for any deterministic and randomized (against adaptive offline adversary) online algorithms. The lower bounds show that our deterministic algorithm achieves the best possible competitive ratio. The research of this paper was partially supported by an NSF CAREER award CCF-0546509.     

Separating online scheduling algorithms with the relative worst order ratio

The relative worst order ratio is a measure for the quality of online algorithms. Unlike the competitive ratio, it compares algorithms directly without involving an optimal offline algorithm. The measure has been successfully applied to problems like paging and bin packing. In this paper, we apply it to machine scheduling. We show that for preemptive scheduling, the measure separates multiple pairs of algorithms which have the same competitive ratios; with the relative worst order ratio, the algorithm which is “intuitively better” is also provably better. Moreover, we show one such example for non-preemptive scheduling.     

Online coupon consumption problem

Nowadays, it is popular that the dealer makes profits by selling a kind of discount coupons, which can be used as money to purchase commodities with total cost less than or equal to the face value of the coupon. We can purchase a coupon at a price of 0<s≤1 times its face value and the number of potential purchasable coupons is a given integer l. The customer has the option to buy the goods by cash completely or by a discount coupon. However, each piece of goods can only use one coupon and the coupon used must have enough balance for the goods. The objective is to minimize the total cost for purchasing all the goods. In this paper, we reduce the problem to a special bin packing model. We consider the online problems for all 0<s≤1 and 1≤l≤∞. We present optimal online algorithms for all 0<s≤1 when l=∞ and l=1. For 2≤l<∞, we give both a lower bound and an algorithm, and show the algorithm is optimal for l=2. A preliminary version of this paper appeared in the proceedings of the 1st International Symposium on Combinatorics, Algorithms, Probabilistic and Experimental Methodologies, Lecture Notes in Computer Science. Y. Jiang supported by Natural Science Foundation of Zhejiang Province (Y605316). Z. Tan supported by Natural Science Foundation of China (10671177, 60021201).     

Note on solving inventory models from operational research view

In this article, we will show that an optimal solution can be derived without complicated mathematical derivation, an improvement on the paper [Yang, G.K., et al., 2011. Note on inventory models with Weibull distribution deterioration. Production Planning & Control, 22 (4), 437–444] that was recently published in Production Planning & Control. We extend their inventory model with a generalised deterioration and backlog rates. Our findings will allow researchers to consider inventory models directly from the view of operational research instead of complex analysis.     

Scheduling Packets with Values and Deadlines in Size-Bounded Buffers

Motivated by providing quality-of-service differentiated services in the Internet, we consider buffer management algorithms for network switches. We study a multi-buffer model. A network switch consists of multiple size-bounded buffers such that at any time, the number of packets residing in each individual buffer cannot exceed its capacity. Packets arrive at the network switch over time; they have values, deadlines, and designated buffers. In each time step, at most one pending packet is allowed to be sent and this packet can be from any buffer. The objective is to maximize the total value of the packets sent by their respective deadlines. A 9.82-competitive online algorithm (Azar and Levy in Lect Notes Comput Sci 4059:5–16 2006) and a 4.73-competitive online algorithm (Li in Lect Notes Comput Sci 5564:265–278, 2009) have been provided for this model, but no offline algorithms have yet been described. In this paper, we study the offline setting of the multi-buffer model. Our contributions include a few optimal offline algorithms for some variants of the model. Each variant has its unique and interesting algorithmic feature.     

Online scheduling on parallel machines with two GoS levels

This paper investigates the online scheduling problem on parallel and identical machines with a new feature that service requests from various customers are entitled to many different grade of service (GoS) levels. Hence each job and machine are labeled with the GoS levels, and each job can be processed by a particular machine only when the GoS level of the job is not less than that of the machine. The goal is to minimize the makespan. In this paper, we consider the problem with two GoS levels. It assumes that the GoS levels of the first k machines and the last m−k machines are 1 and 2, respectively. And every job has a GoS level of 1 alternatively or 2. We first prove the lower bound of the problem under consideration is at least 2. Then we discuss the performance of algorithm AW presented in Azar et al. (J. Algorithms 18:221–237, 1995) for the problem and show it has a tight bound of 4−1/m. Finally, we present an approximation algorithm with competitive ratio . Research supported by Natural Science Foundation of Zhejiang Province (Y605316) and its preliminary version appeared in Proceedings of AAIM2006, LNCS, 4041, 11-21.     

On the Robust Single Machine Scheduling Problem

The single machine scheduling problem with sum of completion times criterion (SS) can be solved easily by the Shortest Processing Time (SPT) rule. In the case of significant uncertainty of the processing times, a robustness approach is appropriate. In this paper, we show that the robust version of the (SS) problem is NP-complete even for very restricted cases. We present an algorithm for finding optimal solutions for the robust (SS) problem using dynamic programming. We also provide two polynomial time heuristics and demonstrate their effectiveness.     

Optimal algorithms for online time series search and one-way trading with interrelated prices

The basic models of online time series search and one-way trading are introduced by El-Yaniv et al. in Algorithmica 30(1), 101–139 (2001) where it is assumed that the prices are bounded within interval [m,M] (0<m<M). In this paper, we consider another case where every two consecutive prices are interrelated, that is, the variation range of each price depends on its preceding price. We present optimal deterministic online algorithms for the two problems, respectively. According to one conclusion in Algorithmica 30(1), 101–139 (2001), we further point out that for the case we considered, an optimal deterministic algorithm for the one-way trading problem can be regarded as an optimal randomized one for the time series search problem, and randomization is useless for the one-way trading problem.     

An Alternative Exact Solution of the Two-Stage Clonal Growth Model of Cancer

The approximate solution of the two-stage clonal expansion model of cancer may substantially deviate from the exact solution, and may therefore lead to erroneous conclusions in particular applications. However, for time-varying parameters the exact solution (method of characteristics) is not easy to implement, hampering the accessibility of the model to nonmathematicians. Based on intuitive reasoning, Clewell et al. (1995) proposed an improved approximate solution that is easy to implement whatever time-varying behavior the parameters may have. Here we provide the mathematical foundation for the approximation suggested by Clewell et al. (1995) and show that, after a slight modification, it is in fact an exact solution for the case of time-constant parameters. We were not able to prove that it is an exact solution for time-varying parameters as well. However, several computer simulations showed that the numerical results do not differ from the exact solution as proposed by Moolgavkar and Luebeck (1990). The advantage of this alternative solution is that the hazard rate of the first malignant cell can be evaluated by numerically integrating a single differential equation.     

Scheduling in Switching Networks with Set-Up Delays

We consider the (preemptive bipartite scheduling problem PBS) (Crescenzi et al., On approximating a scheduling problem, Journal of Combinatorial Optimization, vol. 5, pp. 287–297, 2001) arising in switching communication systems, where each input and output port can be involved in at most one communication at the same time. Given a set of communication tasks to be communicated from the transmitters to the receivers of such a system, we aim to find a schedule minimizing the overall transmission time. To achieve this, we allow the preemption of communication tasks. However, in practice preemption comes with a cost, d, and this renders the problem NP-hard (Gopal et al., An optimal switching algorithm for multibeam satellite systems with variable bandwidth beams, IEEE Trans. Commun., vol.30, pp. 2475–2481, 1982). In this paper, we present a approximation algorithm, which is the first one for the PBS problem with approximation ratio strictly less than two. Furthermore, we propose a simple optimal polynomial time algorithm for a subclass of instances of the PBS problem.This work has been partially supported by the the European Union (FET-Working Group APPOL II), and the Greek General Secretariat of Research and Technology.     

Optimal semi-online algorithm for scheduling with rejection on two uniform machines

In this paper we consider a semi-online scheduling problem with rejection on two uniform machines with speed 1 and s≥1, respectively. A sequence of independent jobs are given and each job is characterized by its size (processing time) and its penalty, in the sense that, jobs arrive one by one and can be either rejected by paying a certain penalty or assigned to some machine. No preemption is allowed. The objective is to minimize the sum of the makespan of schedule, which is yielded by all accepted jobs and the total penalties of all rejected ones. Further, two rejection strategies are permitted thus an algorithm can propose two different schemes, from which the better solution is chosen. For the above version, we present an optimal semi-online algorithm H that achieves a competitive ratio ρ _H (s) as a piecewise function in terms of the speed ratio s.     

New algorithms for online rectangle filling with <Emphasis Type="BoldItalic">k</Emphasis>-lookahead

We study the online rectangle filling problem which arises in channel aware scheduling of wireless networks, and present deterministic and randomized results for algorithms that are allowed a k-lookahead for k≥2. Our main result is a deterministic min {1.848,1+2/(k−1)}-competitive online algorithm. This is the first algorithm for this problem with a competitive ratio approaching 1 as k approaches +∞. The previous best-known solution for this problem has a competitive ratio of 2 for any k≥2. We also present a randomized online algorithm with a competitive ratio of 1+1/(k+1). Our final result is a closely matching lower bound (also proved in this paper) of $1+1/(\sqrt{k+2}+\sqrt{k+1})^{2}>1+1/(4(k+2))$1+1/(\sqrt{k+2}+\sqrt{k+1})^{2}>1+1/(4(k+2)) on the competitive ratio of any randomized online algorithm against an oblivious adversary. These are the first known results for randomized algorithms for this problem.     

Online scheduling with chain precedence constraints of equal-length jobs on parallel machines to minimize makespan

We study the online scheduling problem on m identical parallel machines to minimize makespan, i.e., the maximum completion time of the jobs, where m is given in advance and the jobs arrive online over time. We assume that the jobs, which arrive at some nonnegative real times, are of equal-length and are restricted by chain precedence constraints. Moreover, the jobs arriving at distinct times are independent, and so, only the jobs arriving at a common time are restricted by the chain precedence constraints. In the literature, a best possible online algorithm of a competitive ratio 1.3028 is given for the case \(m=2\). But the problem is unaddressed for \(m\ge 3\). In this paper, we present a best possible online algorithm for the problem with \(m\ge 3\), where the algorithm has a competitive ratio of 1.3028 for \(3\le m\le 5\) and 1.3146 for \(m\ge 6\).     

Online lazy bureaucrat scheduling with a machine deadline

The lazy bureaucrat scheduling problem was first introduced by Arkin et al. (Inf Comput 184:129–146, 2003). Since then, a number of variants have been addressed. However, very little is known on the online version. In this note we focus on the scenario of online scheduling, in which the jobs arrive over time. The bureaucrat (machine) has a working time interval. Namely, he has a deadline by which all scheduled jobs must be completed. A decision is only based on released jobs without any information on the future. We consider two objective functions of [min-makespan] and [min-time-spent]. Both admit best possible online algorithms with competitive ratio of \(\frac{\sqrt{5}+1}{2}\approx 1.618\).     

Multi-phase Algorithms for Throughput Maximization for Real-Time Scheduling

We consider the problem of off-line throughput maximization for job scheduling on one or more machines, where each job has a release time, a deadline and a profit. Most of the versions of the problem discussed here were already treated by Bar-Noy et al. (Proc. 31st ACM STOC, 1999, pp. 622–631; http://www.eng.tau.ac.il/amotz/). Our main contribution is to provide algorithms that do not use linear programming, are simple and much faster than the corresponding ones proposed in Bar-Noy et al. (ibid., 1999), while either having the same quality of approximation or improving it. More precisely, compared to the results of in Bar-Noy et al. (ibid., 1999), our pseudo-polynomial algorithm for multiple unrelated machines and all of our strongly-polynomial algorithms have better performance ratios, all of our algorithms run much faster, are combinatorial in nature and avoid linear programming. Finally, we show that algorithms with better performance ratios than 2 are possible if the stretch factors of the jobs are bounded; a straightforward consequence of this result is an improvement of the ratio of an optimal solution of the integer programming formulation of the JISP2 problem (see Spieksma, Journal of Scheduling, vol. 2, pp. 215–227, 1999) to its linear programming relaxation.     

Online MapReduce scheduling problem of minimizing the makespan

MapReduce system is a popular big data processing framework, and the performance of it is closely related to the efficiency of the centralized scheduler. In practice, the centralized scheduler often has little information in advance, which means each job may be known only after being released. In this paper, hence, we consider the online MapReduce scheduling problem of minimizing the makespan, where jobs are released over time. Both preemptive and non-preemptive version of the problem are considered. In addition, we assume that reduce tasks cannot be parallelized because they are often complex and hard to be decomposed. For the non-preemptive version, we prove the lower bound is \(\frac{m+m(\Psi (m)-\Psi (k))}{k+m(\Psi (m)-\Psi (k))}\), higher than the basic online machine scheduling problem, where k is the root of the equation \(k=\big \lfloor {\frac{m-k}{1+\Psi (m)-\Psi (k)}+1 }\big \rfloor \) and m is the quantity of machines. Then we devise an \((2-\frac{1}{m})\)-competitive online algorithm called MF-LPT (Map First-Longest Processing Time) based on the LPT. For the preemptive version, we present a 1-competitive algorithm for two machines.     

Reviewing performance: an analysis of the structure and functions of performance management reviews

This article proposes an analytical framework for conceptualising performance management reviews (PMRs). Drawing on multiple literatures, the article applies Pettigrew et al.'s (Pettigrew, A., Whipp R., and Rosenfield R., 1989. Competitiveness and the management of strategic change processes. In: A. Francis, and P.K.M. Tharakan, eds. The competitiveness of European industry: country policies and company strategies. London: Routledge) framework to develop our understanding of the context, process and content of PMR. The process elements of PMRs are the levers for managerial intervention as they determine the immediate outcome of PMR. They accomplish this task by acting on the content elements, transforming the subject of PMR into specific outcomes. The scope of the intervention, however, is constrained by factors in the organisation's context. This framework sheds light on various functions of PMRs, most notably strategy implementation.