1,253 research outputs found
Speed-scaling with no Preemptions
We revisit the non-preemptive speed-scaling problem, in which a set of jobs
have to be executed on a single or a set of parallel speed-scalable
processor(s) between their release dates and deadlines so that the energy
consumption to be minimized. We adopt the speed-scaling mechanism first
introduced in [Yao et al., FOCS 1995] according to which the power dissipated
is a convex function of the processor's speed. Intuitively, the higher is the
speed of a processor, the higher is the energy consumption. For the
single-processor case, we improve the best known approximation algorithm by
providing a -approximation algorithm,
where is a generalization of the Bell number. For the
multiprocessor case, we present an approximation algorithm of ratio
improving the best known result by a factor of
. Notice that our
result holds for the fully heterogeneous environment while the previous known
result holds only in the more restricted case of parallel processors with
identical power functions
Randomized algorithms for fully online multiprocessor scheduling with testing
We contribute the first randomized algorithm that is an integration of
arbitrarily many deterministic algorithms for the fully online multiprocessor
scheduling with testing problem. When there are two machines, we show that with
two component algorithms its expected competitive ratio is already strictly
smaller than the best proven deterministic competitive ratio lower bound. Such
algorithmic results are rarely seen in the literature. Multiprocessor
scheduling is one of the first combinatorial optimization problems that have
received numerous studies. Recently, several research groups examined its
testing variant, in which each job arrives with an upper bound on
the processing time and a testing operation of length ; one can choose to
execute for time, or to test for time to obtain the
exact processing time followed by immediately executing the job for
time. Our target problem is the fully online version, in which the jobs arrive
in sequence so that the testing decision needs to be made at the job arrival as
well as the designated machine. We propose an expected -competitive randomized algorithm as a non-uniform
probability distribution over arbitrarily many deterministic algorithms, where
is the Golden ratio. When there are two
machines, we show that our randomized algorithm based on two deterministic
algorithms is already expected -competitive. Besides, we use Yao's principle to prove lower
bounds of and on the expected competitive ratio for any
randomized algorithm at the presence of at least three machines and only two
machines, respectively, and prove a lower bound of on the competitive
ratio for any deterministic algorithm when there are only two machines.Comment: 21 pages with 1 plot; an extended abstract to be submitte
Parallel algorithms for two processors precedence constraint scheduling
The final publication is available at link.springer.comPeer ReviewedPostprint (author's final draft
Improved Rejection Penalty Algorithm with Multiprocessor Rejection Technique
This paper deals with multiprocessor scheduling with rejection technique where each job is provided with processing time and a given penalty cost. If the job satisfies the acceptance condition, it will schedule in the least loaded identical parallel machine else job is rejected. In this way its penalty cost is calculated. Our objective is to minimize the makespan of the scheduled job and to minimize the sum of the penalties of rejected jobs. We have merged ‘CHOOSE ‘and ‘REJECTION PENALTY’ algorithm to reduce the sum of penalties cost and makespan. Our proposed ‘Improved Reject penalty algorithm’ reduce competitive ratio, which in turn enhances the efficiency of the on-line algorithm. By applying our new on-line technique, we got the lower bound of our algorithm is is 1.286 which is far better from the existing algorithms whose competitive ratio is at 1.819. In our approach we have consider non-preemption scheduling technique
Improved CRPD analysis and a secure scheduler against information leakage in real-time systems
Real-time systems are widely applied to the time-critical fields. In order to guarantee that all tasks can be completed on time, predictability becomes a necessary factor when designing a real-time system. Due to more and more requirements about the performance in the real-time embedded system, the cache memory is introduced to the real-time embedded systems.
However, the cache behavior is difficult to predict since the data will be loaded either on the cache or the memory. In order to taking the unexpected overhead, execution time are often enlarged by a certain (huge) factor. However, this will cause a waste of computation resource. Hence, in this thesis, we first integrate the cache-related preemption delay to the previous global earliest deadline first schedulability analysis in the direct-mapped cache. Moreover, several analyses for tighter G-EDF schedulability tests are conducted based on the refined estimation of the maximal number of preemptions. The experimental study is conducted to demonstrate the performance of the proposed methods.
Furthermore, Under the classic scheduling mechanisms, the execution patterns of tasks on such a system can be easily derived. Therefore, in the second part of the thesis, a novel scheduler, roulette wheel scheduler (RWS), is proposed to randomize the task execution pattern. Unlike traditional schedulers, RWS assigns probabilities to each task at predefined scheduling points, and the choice for execution is randomized, such that the execution pattern is no longer fixed. We apply the concept of schedule entropy to measure the amount of uncertainty introduced by any randomized scheduler, which reflects the unlikelihood of for such attacks to success. Comparing to existing randomized scheduler that gives all eligible tasks equal likelihood at a given time point, the proposed method adjusted such values so that the entropy can be greatly increased --Abstract, page iii
Energy-efficient algorithms for non-preemptive speed-scaling
We improve complexity bounds for energy-efficient speed scheduling problems
for both the single processor and multi-processor cases. Energy conservation
has become a major concern, so revisiting traditional scheduling problems to
take into account the energy consumption has been part of the agenda of the
scheduling community for the past few years.
We consider the energy minimizing speed scaling problem introduced by Yao et
al. where we wish to schedule a set of jobs, each with a release date, deadline
and work volume, on a set of identical processors. The processors may change
speed as a function of time and the energy they consume is the th power
of its speed. The objective is then to find a feasible schedule which minimizes
the total energy used.
We show that in the setting with an arbitrary number of processors where all
work volumes are equal, there is a approximation algorithm, where
is the generalized Bell number. This is the first constant
factor algorithm for this problem. This algorithm extends to general unequal
processor-dependent work volumes, up to losing a factor of
in the approximation, where is the maximum
ratio between two work volumes. We then show this latter problem is APX-hard,
even in the special case when all release dates and deadlines are equal and
is 4.
In the single processor case, we introduce a new linear programming
formulation of speed scaling and prove that its integrality gap is at most
. As a corollary, we obtain a
approximation algorithm where there is a single processor, improving on the
previous best bound of
when
- …