28,692 research outputs found
Heavy-tailed Distributions In Stochastic Dynamical Models
Heavy-tailed distributions are found throughout many naturally occurring
phenomena. We have reviewed the models of stochastic dynamics that lead to
heavy-tailed distributions (and power law distributions, in particular)
including the multiplicative noise models, the models subjected to the
Degree-Mass-Action principle (the generalized preferential attachment
principle), the intermittent behavior occurring in complex physical systems
near a bifurcation point, queuing systems, and the models of Self-organized
criticality. Heavy-tailed distributions appear in them as the emergent
phenomena sensitive for coupling rules essential for the entire dynamics
Bulk Scheduling with the DIANA Scheduler
Results from the research and development of a Data Intensive and Network
Aware (DIANA) scheduling engine, to be used primarily for data intensive
sciences such as physics analysis, are described. In Grid analyses, tasks can
involve thousands of computing, data handling, and network resources. The
central problem in the scheduling of these resources is the coordinated
management of computation and data at multiple locations and not just data
replication or movement. However, this can prove to be a rather costly
operation and efficient sing can be a challenge if compute and data resources
are mapped without considering network costs. We have implemented an adaptive
algorithm within the so-called DIANA Scheduler which takes into account data
location and size, network performance and computation capability in order to
enable efficient global scheduling. DIANA is a performance-aware and
economy-guided Meta Scheduler. It iteratively allocates each job to the site
that is most likely to produce the best performance as well as optimizing the
global queue for any remaining jobs. Therefore it is equally suitable whether a
single job is being submitted or bulk scheduling is being performed. Results
indicate that considerable performance improvements can be gained by adopting
the DIANA scheduling approach.Comment: 12 pages, 11 figures. To be published in the IEEE Transactions in
Nuclear Science, IEEE Press. 200
Recommended from our members
Single machine scheduling with general positional deterioration and rate-modifying maintenance
We present polynomial-time algorithms for single machine problems with generalized positional deterioration effects and machine maintenance. The decisions should be taken regarding possible sequences of jobs and on the number of maintenance activities to be included into a schedule in order to minimize the overall makespan. We deal with general non-decreasing functions to represent deterioration rates of job processing times. Another novel extension of existing models is our assumption that a maintenance activity does not necessarily fully restore the machine to its original perfect state. In the resulting schedules, the jobs are split into groups, a particular group to be sequenced after a particular maintenance period, and the actual processing time of a job is affected by the group that job is placed into and its position within the group
PSBS: Practical Size-Based Scheduling
Size-based schedulers have very desirable performance properties: optimal or
near-optimal response time can be coupled with strong fairness guarantees.
Despite this, such systems are very rarely implemented in practical settings,
because they require knowing a priori the amount of work needed to complete
jobs: this assumption is very difficult to satisfy in concrete systems. It is
definitely more likely to inform the system with an estimate of the job sizes,
but existing studies point to somewhat pessimistic results if existing
scheduler policies are used based on imprecise job size estimations. We take
the goal of designing scheduling policies that are explicitly designed to deal
with inexact job sizes: first, we show that existing size-based schedulers can
have bad performance with inexact job size information when job sizes are
heavily skewed; we show that this issue, and the pessimistic results shown in
the literature, are due to problematic behavior when large jobs are
underestimated. Once the problem is identified, it is possible to amend
existing size-based schedulers to solve the issue. We generalize FSP -- a fair
and efficient size-based scheduling policy -- in order to solve the problem
highlighted above; in addition, our solution deals with different job weights
(that can be assigned to a job independently from its size). We provide an
efficient implementation of the resulting protocol, which we call Practical
Size-Based Scheduler (PSBS). Through simulations evaluated on synthetic and
real workloads, we show that PSBS has near-optimal performance in a large
variety of cases with inaccurate size information, that it performs fairly and
it handles correctly job weights. We believe that this work shows that PSBS is
indeed pratical, and we maintain that it could inspire the design of schedulers
in a wide array of real-world use cases.Comment: arXiv admin note: substantial text overlap with arXiv:1403.599
Recruiting Older Workers: Realities and Needs of the Future Workforce
This chapter examines literature pertaining to the recruitment of older workers. It begins by addressing the question of relevance and why older worker recruitment matters. It then examines what is known about older workers, including their attitudes, motivations, and behaviors. Next the chapter addresses what employers are looking for in older workers and, more specifically, discusses the continuum of employers’ practices from those that aggressively try to attract and retain older workers and apply a conservation model of older worker management to those that apply a depreciation model and focus primarily on retrenchment and downsizing older employees. Finally, it addresses how employers can recruit older workers through changes in organizational policies and practices
Single machine scheduling with job-dependent machine deterioration
We consider the single machine scheduling problem with job-dependent machine
deterioration. In the problem, we are given a single machine with an initial
non-negative maintenance level, and a set of jobs each with a non-preemptive
processing time and a machine deterioration. Such a machine deterioration
quantifies the decrement in the machine maintenance level after processing the
job. To avoid machine breakdown, one should guarantee a non-negative
maintenance level at any time point; and whenever necessary, a maintenance
activity must be allocated for restoring the machine maintenance level. The
goal of the problem is to schedule the jobs and the maintenance activities such
that the total completion time of jobs is minimized. There are two variants of
maintenance activities: in the partial maintenance case each activity can be
allocated to increase the machine maintenance level to any level not exceeding
the maximum; in the full maintenance case every activity must be allocated to
increase the machine maintenance level to the maximum. In a recent work, the
problem in the full maintenance case has been proven NP-hard; several special
cases of the problem in the partial maintenance case were shown solvable in
polynomial time, but the complexity of the general problem is left open. In
this paper we first prove that the problem in the partial maintenance case is
NP-hard, thus settling the open problem; we then design a -approximation
algorithm.Comment: 15 page
The Legal Framework for States as Employers-of-Choice in Workplace Flexibility: A Case Study of Arizona and Michigan
Outlines the statutes, regulations, executive actions, and collective bargaining agreements that authorize flexible work arrangements, time off, and career flexibility in the two state workforces; the elements of model programs; and their benefits
Revisiting Size-Based Scheduling with Estimated Job Sizes
We study size-based schedulers, and focus on the impact of inaccurate job
size information on response time and fairness. Our intent is to revisit
previous results, which allude to performance degradation for even small errors
on job size estimates, thus limiting the applicability of size-based
schedulers.
We show that scheduling performance is tightly connected to workload
characteristics: in the absence of large skew in the job size distribution,
even extremely imprecise estimates suffice to outperform size-oblivious
disciplines. Instead, when job sizes are heavily skewed, known size-based
disciplines suffer.
In this context, we show -- for the first time -- the dichotomy of
over-estimation versus under-estimation. The former is, in general, less
problematic than the latter, as its effects are localized to individual jobs.
Instead, under-estimation leads to severe problems that may affect a large
number of jobs.
We present an approach to mitigate these problems: our technique requires no
complex modifications to original scheduling policies and performs very well.
To support our claim, we proceed with a simulation-based evaluation that covers
an unprecedented large parameter space, which takes into account a variety of
synthetic and real workloads.
As a consequence, we show that size-based scheduling is practical and
outperforms alternatives in a wide array of use-cases, even in presence of
inaccurate size information.Comment: To be published in the proceedings of IEEE MASCOTS 201
- …