3,792 research outputs found
Energy Efficient Scheduling and Routing via Randomized Rounding
We propose a unifying framework based on configuration linear programs and
randomized rounding, for different energy optimization problems in the dynamic
speed-scaling setting. We apply our framework to various scheduling and routing
problems in heterogeneous computing and networking environments. We first
consider the energy minimization problem of scheduling a set of jobs on a set
of parallel speed scalable processors in a fully heterogeneous setting. For
both the preemptive-non-migratory and the preemptive-migratory variants, our
approach allows us to obtain solutions of almost the same quality as for the
homogeneous environment. By exploiting the result for the
preemptive-non-migratory variant, we are able to improve the best known
approximation ratio for the single processor non-preemptive problem.
Furthermore, we show that our approach allows to obtain a constant-factor
approximation algorithm for the power-aware preemptive job shop scheduling
problem. Finally, we consider the min-power routing problem where we are given
a network modeled by an undirected graph and a set of uniform demands that have
to be routed on integral routes from their sources to their destinations so
that the energy consumption is minimized. We improve the best known
approximation ratio for this problem.Comment: 27 page
Multiprocessor speed scaling for jobs with arbitrary sizes and deadlines
In this paper we study energy efficient deadline scheduling on multiprocessors in which the processors consumes power at a rate of sα when running at speeds, where α ≥ 2. The problem is to dispatch jobs to processors and determine the speed and jobs to run for each processor so as to complete all jobs by their deadlines using the minimum energy. The problem has been well studied for the single processor case. For the multiprocessor setting, constant competitive online algorithms for special cases of unit size jobs or arbitrary size jobs with agreeable deadlines have been proposed by Albers et al. (2007). A randomized algorithm has been proposed for jobs of arbitrary sizes and arbitrary deadlines by Greiner et al. (2009). We propose a deterministic online algorithm for the general setting and show that it is O(logαP)-competitive, where P is the ratio of the maximum and minimum job size
Speed-Oblivious Online Scheduling: Knowing (Precise) Speeds is not Necessary
We consider online scheduling on unrelated (heterogeneous) machines in a
speed-oblivious setting, where an algorithm is unaware of the exact
job-dependent processing speeds. We show strong impossibility results for
clairvoyant and non-clairvoyant algorithms and overcome them in models inspired
by practical settings: (i) we provide competitive learning-augmented
algorithms, assuming that (possibly erroneous) predictions on the speeds are
given, and (ii) we provide competitive algorithms for the speed-ordered model,
where a single global order of machines according to their unknown
job-dependent speeds is known. We prove strong theoretical guarantees and
evaluate our findings on a representative heterogeneous multi-core processor.
These seem to be the first empirical results for scheduling algorithms with
predictions that are evaluated in a non-synthetic hardware environment.Comment: To appear at ICML 202
08071 Abstracts Collection -- Scheduling
From 10.02. to 15.02., the Dagstuhl Seminar 08071 ``Scheduling\u27\u27 was held
in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
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