Consider the classical online scheduling problem where jobs that arrive one by one are assigned to identical parallel machines with the objective of minimizing the makespan. We generalize this problem by allowing the current assignment to be changed whenever a new job arrives, subject to the constraint that the total size of moved jobs is bounded by β times the size of the arriving job. For small values of β, we obtain several simple online algorithms with constant competitive ratio. We also present a linear time "online approximation scheme', that is, a family of online algorithms with competitive ratio 1+ε and constant migration factor β(ε), for any fixed ε>0
A malleable parallel job is one that may be assigned to any number of processors in a parallel compu...
AbstractWe study the online batch scheduling problem on parallel machines with delivery times. Onlin...
AbstractWe consider online scheduling on m parallel-batch machines where the batch capacity is unbou...
Consider the classical online scheduling problem where jobs that arrive one by one are assigned to i...
In the classic minimum makespan scheduling problem, we are given an input sequence of n jobs with si...
In the classic minimum makespan scheduling problem, we are given an input sequence of n jobs with si...
Online models that allow recourse are highly effective in situations where classical models are too ...
Makespan minimization on parallel identical machines is a classical and intensively studied problem ...
We investigate the power of migration in online scheduling for parallel identical machines. Our obje...
Scheduling a set of n jobs on m identical parallel machines so as to minimize the makespan or maximi...
We investigate the power of migration in online scheduling for parallel identical machines. Our obje...
We consider online scheduling on multiple machines for jobs arriving one-by-one with the objective o...
Abstract. We study a classical problem in online scheduling. A sequence of jobs must be scheduled on...
Makespan minimization onm identical machines is a fundamental scheduling problem. The goal is to ass...
We consider the following online scheduling problem in which the input consists of n jobs to be sche...
A malleable parallel job is one that may be assigned to any number of processors in a parallel compu...
AbstractWe study the online batch scheduling problem on parallel machines with delivery times. Onlin...
AbstractWe consider online scheduling on m parallel-batch machines where the batch capacity is unbou...
Consider the classical online scheduling problem where jobs that arrive one by one are assigned to i...
In the classic minimum makespan scheduling problem, we are given an input sequence of n jobs with si...
In the classic minimum makespan scheduling problem, we are given an input sequence of n jobs with si...
Online models that allow recourse are highly effective in situations where classical models are too ...
Makespan minimization on parallel identical machines is a classical and intensively studied problem ...
We investigate the power of migration in online scheduling for parallel identical machines. Our obje...
Scheduling a set of n jobs on m identical parallel machines so as to minimize the makespan or maximi...
We investigate the power of migration in online scheduling for parallel identical machines. Our obje...
We consider online scheduling on multiple machines for jobs arriving one-by-one with the objective o...
Abstract. We study a classical problem in online scheduling. A sequence of jobs must be scheduled on...
Makespan minimization onm identical machines is a fundamental scheduling problem. The goal is to ass...
We consider the following online scheduling problem in which the input consists of n jobs to be sche...
A malleable parallel job is one that may be assigned to any number of processors in a parallel compu...
AbstractWe study the online batch scheduling problem on parallel machines with delivery times. Onlin...
AbstractWe consider online scheduling on m parallel-batch machines where the batch capacity is unbou...