In this paper, we consider energy management algo-rithms for scheduling jobs in power-scare scenarios such as embedded computer systems and sensor networks. We focus on investigating the impact of buffer resources in minimizing the total energy cost in an online setting. The online algorithms do not have any assumptions on job arrivals; their worst-case performance is measured in term of competitive ratio, when they are compared with the optimal algorithms with clairvoyance. We prove that with appropriate extra buffer space, an online algo-rithm can beat an weak optimal offline algorithm in terms of the total energy required. Our research result helps to quantitatively estimate the optimal on-chip buffer re-sources allocated in real-time sy...
We consider two devices, which has states ON and OFF. In the ON state, the devices use their full po...
In this paper, we address energy-aware online scheduling of jobs with resource contention. We propos...
Lecture Notes in Computer Science, vol. 6534 entitled: Approximation and Online Algorithms: 8th inte...
We consider the problem of online dynamic power management that provides hard real-time guarantees. ...
Existing work on scheduling with energy concern has focused on minimizing the energy for completing ...
Online flow-time scheduling is a fundamental problem in computer science and has been extensively st...
Abstract. We introduce and solve a new problem inspired by energy pricing schemes in which a client ...
Efficient job scheduling reduces energy consumption and enhances the performance of machines in data...
We consider online scheduling algorithms in the dynamic speed scaling model, where a processor can s...
Abstract—We show that a natural nonclairvoyant online algorithm for scheduling jobs on a power-heter...
Efficient job scheduling reduces energy consumption and enhances the performance of machines in data...
We first consider online speed scaling algorithms to min-imize the energy used subject to the constr...
We consider online job scheduling together with power management on multiple machines. In this model...
We consider the problem of online scheduling of jobs on unrelated machines with dynamic speed scalin...
We consider the problem of online dynamic power management that provides hard real-time guarantees f...
We consider two devices, which has states ON and OFF. In the ON state, the devices use their full po...
In this paper, we address energy-aware online scheduling of jobs with resource contention. We propos...
Lecture Notes in Computer Science, vol. 6534 entitled: Approximation and Online Algorithms: 8th inte...
We consider the problem of online dynamic power management that provides hard real-time guarantees. ...
Existing work on scheduling with energy concern has focused on minimizing the energy for completing ...
Online flow-time scheduling is a fundamental problem in computer science and has been extensively st...
Abstract. We introduce and solve a new problem inspired by energy pricing schemes in which a client ...
Efficient job scheduling reduces energy consumption and enhances the performance of machines in data...
We consider online scheduling algorithms in the dynamic speed scaling model, where a processor can s...
Abstract—We show that a natural nonclairvoyant online algorithm for scheduling jobs on a power-heter...
Efficient job scheduling reduces energy consumption and enhances the performance of machines in data...
We first consider online speed scaling algorithms to min-imize the energy used subject to the constr...
We consider online job scheduling together with power management on multiple machines. In this model...
We consider the problem of online scheduling of jobs on unrelated machines with dynamic speed scalin...
We consider the problem of online dynamic power management that provides hard real-time guarantees f...
We consider two devices, which has states ON and OFF. In the ON state, the devices use their full po...
In this paper, we address energy-aware online scheduling of jobs with resource contention. We propos...
Lecture Notes in Computer Science, vol. 6534 entitled: Approximation and Online Algorithms: 8th inte...