Constant Number Of Machines Research Articles

Performance Analysis of Modified SRPT in Multiple-Processor Multitask Scheduling

In this paper we study the multiple-processor multitask scheduling problem in both deterministic and stochastic models, where each job have several tasks and is complete only when all its tasks are finished. We consider and analyze Modified Shortest Remaining Processing Time (M-SRPT) scheduling algorithm, a simple modification of SRPT, which always schedules jobs according to SRPT whenever possible, while satisfying the non-preemptive constraint. The M-SRPT algorithm is proved to achieve a competitive ratio of θ(log α + β) for minimizing response time, where α denotes the ratio between maximum job workload and minimum job workload, β represents the ratio between maximum non-preemptive task workload and minimum job workload. In addition, the competitive ratio achieved is shown to be optimal (up to a constant factor), when there are constant number of machines. We further consider the problem under Poisson arrival and general workload distribution (i. e., M/GI/N system), and show that M-SRPT achieves asymptotic optimal mean response time when the traffic intensity ρ approaches 1, if job size distribution has finite support. Beyond finite job workload, the asymptotic optimality of M-SRPT also holds for infinite job size distributions with certain probabilistic assumptions, for example, M/M/N system with finite task workload.

Improved bi-criteria approximation schemes for load balancing on unrelated machines with cost constraints

We study a generalized version of the load balancing problem on unrelated machines with cost constraints: Given a set of m machines (of certain types) and a set of n jobs, each job j processed on machine i requires pi,j time units and incurs a cost ci,j, and the goal is to find a schedule of jobs to machines, which is defined as an ordered partition of n jobs into m disjoint subsets, in such a way that some objective function of the vector of the completion times of the machines is optimized, subject to the constraint that the total costs by the schedule must be within a given budget B. Motivated by recent results from the literature, our focus is on the case when the number of machine types is a fixed constant and we develop a bi-criteria approximation scheme for the studied problem. Our result generalizes several known results for certain special cases, such as the case with identical machines, or the case with a constant number of machines with cost constraints. Building on the elegant technique recently proposed by Jansen and Maack [1], we construct a more general approach that can be used to derive approximation schemes for a wider class of load balancing problems with linear constraints.

Closing the Gap for Makespan Scheduling via Sparsification Techniques

Makespan scheduling on identical machines is one of the most basic and fundamental packing problems studied in the discrete optimization literature. It asks for an assignment of n jobs to a set of m identical machines that minimizes the makespan. The problem is strongly NP-hard, and thus we do not expect a ([Formula: see text])-approximation algorithm with a running time that depends polynomially on [Formula: see text]. It has been recently shown that a subexponential running time on [Formula: see text] would imply that the Exponential Time Hypothesis (ETH) fails. A long sequence of algorithms have been developed that try to obtain low dependencies on [Formula: see text], the better of which achieves a quadratic running time on the exponent. In this paper we obtain an algorithm with an almost-linear dependency on [Formula: see text] in the exponent, which is tight under ETH up to logarithmic factors. Our main technical contribution is a new structural result on the configuration-IP integer linear program. More precisely, we show the existence of a highly symmetric and sparse optimal solution, in which all but a constant number of machines are assigned a configuration with small support. This structure can then be exploited by integer programming techniques and enumeration. We believe that our structural result is of independent interest and should find applications to other settings. We exemplify this by applying our structural results to the minimum makespan problem on related machines and to a larger class of objective functions on parallel machines. For all these cases, we obtain an efficient PTAS with running time with an almost-linear dependency on [Formula: see text] and polynomial in n.

Complexity of server scheduling on parallel dedicated machines subject to fixed job sequences

We study server scheduling on parallel dedicated machines to minimize the makespan subject to given processing sequences. Before a job starts its processing on its designated machine, a loading operation must be performed, which is undertaken by a server shared by all the jobs. While the two-machine problem is polynomially solvable, we show that the problem becomes binary NP-hard when the number of machines is three, and propose a pseudo-polynomial algorithm to solve the problem with a constant number of machines.

Complexity and Inapproximability Results for Parallel Task Scheduling and Strip Packing

We study the Parallel Task Scheduling problem $Pm|size_j|C_{\max}$ with a constant number of machines. This problem is known to be strongly NP-complete for each $m \geq 5$, while it is solvable in pseudo-polynomial time for each $m \leq 3$. We give a positive answer to the long-standing open question whether this problem is strongly $NP$-complete for $m=4$. As a second result, we improve the lower bound of $\frac{12}{11}$ for approximating pseudo-polynomial Strip Packing to $\frac{5}{4}$. Since the best known approximation algorithm for this problem has a ratio of $\frac{4}{3} + \varepsilon$, this result narrows the gap between approximation ratio and inapproximability result by a significant step. Both results are proven by a reduction from the strongly $NP$-complete problem 3-Partition.

Notes on inverse bin-packing problems

In bin-packing problems, given items need to be packed using a minimum number of bins. Inverse bin-packing number problems, IBPN for short, assume a given set of items and number of bins. The objective is to achieve the minimum perturbation to the item-size vector so that all the items can be packed into the prescribed number of bins. In this paper, complexity status and approximation behavior for IBPN were investigated. Under the Lp-norm, ∀p∈{1,2,…,∞}, IBPN turns out to be NP-hard in the strong sense. IBPN under the L1-norm admits a polynomial time differential approximation scheme, and a fully polynomial time approximation scheme if a constant number of machines is provided as input. We also consider another IBPN variant where a specified feasible solution is given instead of a target bin number. The objective is to make the given solution optimal with minimum modification. We provide the hardness result for this problem.

Minimizing flow time on a constant number of machines with preemption

We consider offline algorithms for minimizing the total flow time on O ( 1 ) machines where jobs can be preempted arbitrarily but migrations are disallowed. Our main result is a quasi-polynomial time approximation scheme for minimizing the total flow time. We also consider more general settings and give some hardness results.

Letter to the Editor—A Proof of the Optimality of the Shortest Remaining Processing Time Discipline

Letter to the Editor—A Proof of the Optimality of the Shortest Remaining Processing Time Discipline