A note on hierarchical scheduling on two uniform machines

This paper studies online hierarchical scheduling on two uniform machines with the objective to minimize makespan. Machines are provided with different capability, i.e., the one with speed s can schedule all jobs, while the other one with speed 1 can only process partial jobs. Optimal algorithms for any 0<s<∞ are given in the paper. For 0<s<1, it has a competitive ratio of min{1+s,1+\frac1+s1+s+s²}\min\{1+s,1+\frac{1+s}{1+s+s^{2}}\} . For s>1, the competitive ratio is min{\frac1+ss,1+\frac2s1+s+s²}\min\{\frac{1+s}{s},1+\frac{2s}{1+s+s^{2}}\} .     

Algorithms with limited number of preemptions for scheduling on parallel machines

In previous study on comparing the makespan of the schedule allowed to be preempted at most i times and that of the optimal schedule with unlimited number of preemptions, the worst case ratio was usually obtained by analyzing the structures of the optimal schedules. For m identical machines case, the worst case ratio was shown to be 2m/(m+i+1) for any 0≤i≤m?1 (Braun and Schmidt in SIAM J. Comput. 32(3):671–680, 2003), and they showed that LPT algorithm is an exact algorithm which can guarantee the worst case ratio for i=0. In this paper, we propose a simpler method which is based on the design and analysis of the algorithm and finding an instance in the worst case. It can not only obtain the worst case ratio but also give a linear algorithm which can guarantee this ratio for any 0≤i≤m?1, and thus we generalize the previous results. We also make a discussion on the trade-off between the objective value and the number of preemptions. In addition, we consider the i-preemptive scheduling on two uniform machines. For both i=0 and i=1, we give two linear algorithms and present the worst-case ratios with respect to s, i.e., the ratio of the speeds of two machines.     

Preemptive Machine Covering on Parallel Machines

This paper investigates the preemptive parallel machine scheduling to maximize the minimum machine completion time. We first show the off-line version can be solved in O(mn) time for general m-uniform-machine case. Then we study the on-line version. We show that any randomized on-line algorithm must have a competitive ratio m for m-uniform-machine case and ∑_{i = 1}^m1/i for m-identical-machine case. Lastly, we focus on two-uniform-machine case. We present an on-line deterministic algorithm whose competitive ratio matches the lower bound of the on-line problem for every machine speed ratio s≥ 1. We further consider the case that idle time is allowed to be introduced in the procedure of assigning jobs and the objective becomes to maximize the continuous period of time (starting from time zero) when both machines are busy. We present an on-line deterministic algorithm whose competitive ratio matches the lower bound of the problem for every s≥ 1. We show that randomization does not help.     

The cost of selfishness for maximizing the minimum load on uniformly related machines

Consider the following scheduling game. A set of jobs, each controlled by a selfish agent, are to be assigned to m uniformly related machines. The cost of a job is defined as the total load of the machine that its job is assigned to. A job is interested in minimizing its cost, while the social objective is maximizing the minimum load (the value of the cover) over the machines. This goal is different from the regular makespan minimization goal, which was extensively studied in a game theoretic context. We study the price of anarchy (poa) and the price of stability (pos) for uniformly related machines. The results are expressed in terms of s, which is the maximum speed ratio between any two machines. For uniformly related machines, we prove that the pos is unbounded for s>2, and the poa is unbounded for s≥2. For the remaining cases we show that while the poa grows to infinity as s tends to 2, the pos is at most 2 for any s≤2.     

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.     

On the construction of k-connected m-dominating sets in wireless networks

Connected dominating sets (CDS) that serve as a virtual backbone are now widely used to facilitate routing in wireless networks. A k-connected m-dominating set (kmCDS) is necessary for fault tolerance and routing flexibility. In order to construct a kmCDS with the minimum size, some approximation algorithms have been proposed in literature. However, the proposed algorithms either only consider some special cases where k=1, 2 or k≤m, or not easy to implement, or cannot provide performance ratio. In this paper, we propose a centralized heuristic algorithm, CSAA, which is easy to implement, and two distributed algorithms, DDA and DPA, which are deterministic and probabilistic methods respectively, to construct a kmCDS for general k and m. Theoretical analysis and simulation results indicate that our algorithms are efficient and effective.     

Parallel-machine parallel-batching scheduling with family jobs and release dates to minimize makespan

We consider the scheduling of n family jobs with release dates on m identical parallel batching machines. Each batching machine can process up to b jobs simultaneously as a batch. In the bounded model, b<n, and in the unbounded model, b=∞. Jobs from different families cannot be placed in the same batch. The objective is to minimize the maximum completion time (makespan). When the number of families is a constant, for both bounded model and unbounded model, we present polynomial-time approximation schemes (PTAS).     

Ordinal On-Line Scheduling for Maximizing the Minimum Machine Completion Time

This paper considers the on-line problem of scheduling nonpreemptively n independent jobs on m > 1 identical and parallel machines with the objective to maximize the minimum machine completion time. It is assumed that the values of the processing times are unknown but the order of the jobs by their processing times is known in advance. We are asked to decide the assignment of all the jobs to some machines at time zero by utilizing only ordinal data rather than the actual magnitudes of jobs. Algorithms to slove the problem are called ordinal algorithms. In this paper, we give lower bounds and ordinal algorithms. We first propose an algorithm MIN which is at most -competitive for any m machine case, while the lower bound is _i=1 ^m 1/i. Both are on the order of (ln m). Furthermore, for m = 3, we present an optimal algorithm.     

Two uniform machines with nearly equal speeds: unified approach to known sum and known optimum in semi on-line scheduling

We consider semi on-line scheduling on two uniform machines. The speed of the slow machine is normalized to 1 while the speed of the fast machine is assumed to be s≥1. Jobs of size J ₁,J ₂,… arrive one at a time, and each J _i (i≥1) has to be assigned to one of the machines before J _i+1 arrives. The assignment cannot be changed later. The processing time of the ith job is J _i on the slow machine and J _i /s on the fast one. The objective is to minimize the makespan. We study both the case where the only information known in advance is the total size ∑_i≥1 J _i of the jobs and the case where the only information known in advance is the optimum makespan. For each of these two cases, we almost completely determine the best possible competitive ratio of semi on-line algorithms compared to the off-line optimum, as a function of s in the range \(1\le s<\frac{1+\sqrt{17}}{4}\approx1.2808\), except for a very short subinterval around s=1.08. We also prove that the best competitive ratio achievable for known optimum is at least as good as the one for known sum, even for any number of uniform machines of any speeds.     

Semi-online scheduling on two identical machines with rejection

In this paper we consider two semi-online scheduling problems with rejection on two identical machines. 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. In the first problem one can reassign several scheduled jobs in rejection tache, in the second a buffer with length k is available in rejection tache. Two optimal algorithms both with competitive ratio $\frac{3}{2}$ are presented.     

Online scheduling with a buffer on related machines

Online scheduling with a buffer is a semi-online problem which is strongly related to the basic online scheduling problem. Jobs arrive one by one and are to be assigned to parallel machines. A buffer of a fixed capacity K is available for storing at most K input jobs. An arriving job must be either assigned to a machine immediately upon arrival, or it can be stored in the buffer for unlimited time. A stored job which is removed from the buffer (possibly, in order to allocate a space in the buffer for a new job) must be assigned immediately as well. We study the case of two uniformly related machines of speed ratio s≥1, with the goal of makespan minimization.     

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 \(m\ge 6\).     

Efficient Job Scheduling Algorithms with Multi-Type Contentions

In this paper, we consider an interesting generalization of the classic job scheduling problem in which each job needs to compete not only for machines but also for other types of resources. The contentions among jobs for machines and for resources could interfere with each other, which complicates the problem dramatically. We present a family of approximation algorithms for solving several variants of the problem by using a generic algorithmic framework. Our algorithms achieve a constant approximation ratio (i.e., 3) when there is only one type of resources or certain dependency relation exists among multiple types of resources. When the r resources are unrelated, the approximation ratio of our algorithm becomes k+2, where k≤ r is a constant depending on the problem instance. As an application, we also show that our techniques can be easily applied to optical burst switching (OBS) networks to design more efficient wavelength scheduling algorithms.This research was supported in part by an IBM faculty partnership award, and an IRCAF award from SUNY Buffalo.     

Semi-online scheduling with “end of sequence” information

We study a variant of classical scheduling, which is called scheduling with “end of sequence” information. It is known in advance that the last job has the longest processing time. Moreover, the last job is marked, and thus it is known for every new job whether it is the final job of the sequence. We explore this model on two uniformly related machines, that is, two machines with possibly different speeds. Two objectives are considered, maximizing the minimum completion time and minimizing the maximum completion time (makespan). Let s be the speed ratio between the two machines, we consider the competitive ratios which are possible to achieve for the two problems as functions of s. We present algorithms for different values of s and lower bounds on the competitive ratio. The proposed algorithms are best possible for a wide range of values of s. For the overall competitive ratio, we show tight bounds of ϕ + 1 ≈ 2.618 for the first problem, and upper and lower bounds of 1.5 and 1.46557 for the second problem. The authors would like to dedicate this paper to the memory of our colleague and friend Yong He who passed away in August 2005 after struggling with illness. D. Ye: Research was supported in part by NSFC (10601048).     

2-Distance vertex-distinguishing index of subcubic graphs

This paper addresses the performance of scheduling algorithms for a two-stage no-wait hybrid flowshop environment with inter-stage flexibility, where there exist several parallel machines at each stage. Each job, composed of two operations, must be processed from start to completion without any interruption either on or between the two stages. For each job, the total processing time of its two operations is fixed, and the stage-1 operation is divided into two sub-parts: an obligatory part and an optional part (which is to be determined by a solution), with a constraint that no optional part of a job can be processed in parallel with an idleness of any stage-2 machine. The objective is to minimize the makespan. We prove that even for the special case with only one machine at each stage, this problem is strongly NP-hard. For the case with one machine at stage 1 and m machines at stage 2, we propose two polynomial time approximation algorithms with worst case ratio of \(3-\frac{2}{m+1}\) and \(2-\frac{1}{m+1}\), respectively. For the case with m machines at stage 1 and one machine at stage 2, we propose a polynomial time approximation algorithm with worst case ratio of 2. We also prove that all the worst case ratios are tight.     

Approximation algorithm for the parallel-machine scheduling problem with release dates and submodular rejection penalties

In this paper, we consider the parallel-machine scheduling problem with release dates and submodular rejection penalties. In this problem, we are given m identical parallel machines and n jobs. Each job has a processing time and a release date. A job is either rejected, in which case a rejection penalty has to be paid, or accepted and processed on one of the m identical parallel machines. The objective is to minimize the sum of the makespan of the accepted jobs and the rejection penalty of the rejected jobs which is determined by a submodular function. Our main work is to design a 2-approximation algorithm based on the primal-dual framework.

     

Semi-online scheduling with known maximum job size on two uniform machines

In this paper, we investigate the semi-online scheduling problem with known maximum job size on two uniform machines with the speed ratio s≥1. The objective is to minimize the makespan. Two algorithms are presented, where the first is optimal for \(1\leq s\leq\sqrt{2}\), and the second is optimal for 1.559≤s≤2 and \(s\ge \frac{3+\sqrt{17}}{2}\). In addition, the improvement on lower bounds is made for \(2.     

Circular L(j,k)-labeling number of direct product of path and cycle

Let j, k and m be positive numbers, a circular m-L(j,k)-labeling of a graph G is a function f:V(G)→[0,m) such that |f(u)?f(v)| _m ≥j if u and v are adjacent, and |f(u)?f(v)| _m ≥k if u and v are at distance two, where |a?b| _m =min{|a?b|,m?|a?b|}. The minimum m such that there exist a circular m-L(j,k)-labeling of G is called the circular L(j,k)-labeling number of G and is denoted by σ _j,k (G). In this paper, for any two positive numbers j and k with j≤k, we give some results about the circular L(j,k)-labeling number of direct product of path and cycle.     

Virtual charismatic leadership and signaling theory: A prospective meta-analysis in five countries

Drawing upon signaling theory, charismatic leadership tactics (CLTs) have been identified as a trainable set of skills. Although organizations rely on technology-mediated communication, the effects of CLTs have not been examined in a virtual context. Preregistered experiments were conducted in face-to-face (Study 1; n = 121) and virtual settings (Study 2; n = 128) in the United States. In Study 3, we conducted virtual replications in Austria (n = 134), France (n = 137), India (n = 128), and Mexico (n = 124). Combined with past experiments, the meta-analytic effect of CLTs on performance (Cohen’s d = 0.52 in-person, k = 4; Cohen’s d = 0.21 overall, k = 10) and engagement in an extra-role task (Cohen’s d = 0.19 overall; k = 6) indicate large to moderate effects. Yet, for performance in a virtual context Cohen’s d ranged from ?0.25 to 0.17 (Cohen’s d = 0.01 overall; k = 6). Study 4 (n = 129) provided mixed support for signaling theory in a virtual context, linking CLTs to some positive evaluations. We conclude with guidance for future research on charismatic leadership and signaling theory.     

Total completion time minimization in online hierarchical scheduling of unit-size jobs

This paper investigates an online hierarchical scheduling problem on m parallel identical machines. Our goal is to minimize the total completion time of all jobs. Each job has a unit processing time and a hierarchy. The job with a lower hierarchy can only be processed on the first machine and the job with a higher hierarchy can be processed on any one of m machines. We first show that the lower bound of this problem is at least \(1+\min \{\frac{1}{m}, \max \{\frac{2}{\lceil x\rceil +\frac{x}{\lceil x\rceil }+3}, \frac{2}{\lfloor x\rfloor +\frac{x}{\lfloor x\rfloor }+3}\}\), where \(x=\sqrt{2m+4}\). We then present a greedy algorithm with tight competitive ratio of \(1+\frac{2(m-1)}{m(\sqrt{4m-3}+1)}\). The competitive ratio is obtained in a way of analyzing the structure of the instance in the worst case, which is different from the most common method of competitive analysis. In particular, when \(m=2\), we propose an optimal online algorithm with competitive ratio of \(16\) \(/\) \(13\), which complements the previous result which provided an asymptotically optimal algorithm with competitive ratio of 1.1573 for the case where the number of jobs n is infinite, i.e., \(n\rightarrow \infty \).