8 research outputs found
A survey of offline algorithms for energy minimization under deadline constraints
Modern computers allow software to adjust power management settings like speed and sleep modes to decrease the power consumption, possibly at the price of a decreased performance. The impact of these techniques mainly depends on the schedule of the tasks. In this article, a survey on underlying theoretical results on power management, as well as offline scheduling algorithms that aim at minimizing the energy consumption under real-time constraints, is given
On a reduction for a class of resource allocation problems
In the resource allocation problem (RAP), the goal is to divide a given
amount of resource over a set of activities while minimizing the cost of this
allocation and possibly satisfying constraints on allocations to subsets of the
activities. Most solution approaches for the RAP and its extensions allow each
activity to have its own cost function. However, in many applications, often
the structure of the objective function is the same for each activity and the
difference between the cost functions lies in different parameter choices such
as, e.g., the multiplicative factors. In this article, we introduce a new class
of objective functions that captures the majority of the objectives occurring
in studied applications. These objectives are characterized by a shared
structure of the cost function depending on two input parameters. We show that,
given the two input parameters, there exists a solution to the RAP that is
optimal for any choice of the shared structure. As a consequence, this problem
reduces to the quadratic RAP, making available the vast amount of solution
approaches and algorithms for the latter problem. We show the impact of our
reduction result on several applications and, in particular, we improve the
best known worst-case complexity bound of two important problems in vessel
routing and processor scheduling from to
A Fully Polynomial-Time Approximation Scheme for Speed Scaling with Sleep State
International audienc
Speed scaling with power down scheduling for agreeable deadlines
We consider the problem of scheduling on a single processor a given set of n
jobs. Each job j has a workload w_j and a release time r_j. The processor can
vary its speed and hibernate to reduce energy consumption. In a schedule
minimizing overall consumed energy, it might be that some jobs complete
arbitrarily far from their release time. So in order to guarantee some quality
of service, we would like to impose a deadline d_j=r_j+F for every job j, where
F is a guarantee on the *flow time*. We provide an O(n^3) algorithm for the
more general case of *agreeable deadlines*, where jobs have release times and
deadlines and can be ordered such that for every i<j, both r_i<=r_j and
d_i<=d_j
Speed scaling with power down scheduling for agreeable deadlines
International audienceWe consider the problem of scheduling on a single processor a given set of n jobs. Each job j has a workload wj and a release time r j. The processor can vary its speed and hibernate to reduce energy consumption. In a schedule minimizing overall consumed energy, it might be that some jobs complete arbitrarily far from their release time. So in order to guarantee some quality of service, we would like to impose a deadline d j = rj + F for every job j, where F is a guarantee on the flow time. We provide an O(n3) algorithm for the more general case of agreeable deadlines, where jobs have release times and deadlines and can be ordered such that for every i < j, both ri rj and di dj
Speed scaling with power down scheduling for agreeable deadlines
We consider the problem of scheduling on a single processor a given set of n jobs. Each job j has a workload w_j and a release time r_j. The processor can vary its speed and hibernate to reduce energy consumption. In a schedule minimizing overall consumed energy, it might be that some jobs complete arbitrarily far from their release time. So in order to guarantee some quality of service, we would like to impose a deadline d_j=r_j+F for every job j, where F is a guarantee on the *flow time*. We provide an O(n^3) algorithm for the more general case of *agreeable deadlines*, where jobs have release times and deadlines and can be ordered such that for every i<j, both r_i<=r_j and d_i<=d_j