Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Online strategies for dynamic power management in systems with multiple power-saving states
ACM Transactions on Embedded Computing Systems (TECS)
Proceedings of the conference on Design, automation and test in Europe
Scheduling Algorithms
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Algorithmic problems in power management
ACM SIGACT News
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
An Efficient Algorithm for Computing Optimal Discrete Voltage Schedules
SIAM Journal on Computing
Scheduling to minimize gaps and power consumption
Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
On single-machine scheduling without intermediate delays
Discrete Applied Mathematics
Race to idle: New algorithms for speed scaling with a sleep state
ACM Transactions on Algorithms (TALG)
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The aim of power management policies is to reduce the amount of energy consumed by computer systems while maintaining a satisfactory level of performance. One common method for saving energy is to simply suspend the system during idle times. No energy is consumed in the suspend mode. However, the process of waking up the system itself requires a certain fixed amount of energy, and thus suspending the system is beneficial only if the idle time is long enough to compensate for this additional energy expenditure. In the specific problem studied in the article, we have a set of jobs with release times and deadlines that need to be executed on a single processor. Preemptions are allowed. The processor requires energy L to be woken up and, when it is on, it uses one unit of energy per one unit of time. It has been an open problem whether a schedule minimizing the overall energy consumption can be computed in polynomial time. We solve this problem in positive, by providing an O(n5)-time algorithm. In addition we provide an O(n4)-time algorithm for computing the minimum energy schedule when all jobs have unit length.