25,579 research outputs found
A hyper-heuristic for adaptive scheduling in computational grids
In this paper we present the design and implementation of an hyper-heuristic for efficiently scheduling independent jobs in computational grids. An efficient scheduling of jobs to grid resources depends on many parameters, among others, the characteristics of the resources and jobs (such as computing capacity, consistency of computing, workload, etc.). Moreover, these characteristics change over time due to the dynamic nature of grid environment, therefore the planning of jobs to resources should be adaptively done. Existing ad hoc scheduling methods (batch and immediate mode) have shown their efficacy for certain types of resource and job characteristics. However, as stand alone methods, they are not able to produce the best planning of jobs to resources for different types of Grid resources and job characteristics. In this work we have designed and implemented a hyper-heuristic that uses a set of ad hoc (immediate and batch mode) scheduling methods to provide the scheduling of jobs to Grid resources according to the Grid and job characteristics. The hyper-heuristic is a high level algorithm, which examines the state and characteristics of the Grid system (jobs and resources), and selects and applies the ad hoc method that yields the best planning of jobs. The resulting hyper-heuristic based scheduler can be thus used to develop network-aware applications that need efficient planning of jobs to resources. The hyper-heuristic has been tested and evaluated in a dynamic setting through a prototype of a Grid simulator. The experimental evaluation showed the usefulness of the hyper-heuristic for planning of jobs to resources as compared to planning without knowledge of the resource and job characteristics.Peer ReviewedPostprint (author's final draft
Managing Uncertainty: A Case for Probabilistic Grid Scheduling
The Grid technology is evolving into a global, service-orientated
architecture, a universal platform for delivering future high demand
computational services. Strong adoption of the Grid and the utility computing
concept is leading to an increasing number of Grid installations running a wide
range of applications of different size and complexity. In this paper we
address the problem of elivering deadline/economy based scheduling in a
heterogeneous application environment using statistical properties of job
historical executions and its associated meta-data. This approach is motivated
by a study of six-month computational load generated by Grid applications in a
multi-purpose Grid cluster serving a community of twenty e-Science projects.
The observed job statistics, resource utilisation and user behaviour is
discussed in the context of management approaches and models most suitable for
supporting a probabilistic and autonomous scheduling architecture
A fast, effective local search for scheduling independent jobs in heterogeneous computing environments
The efficient scheduling of independent computational jobs in a heterogeneous computing (HC) environment is an important problem in domains such as grid computing. Finding optimal schedules for such an environment is (in general) an NP-hard problem, and so heuristic approaches must be used. Work with other NP-hard problems has shown that solutions found by heuristic algorithms can often be improved by applying local search procedures to the solution found. This paper describes a simple but effective local search procedure for scheduling independent jobs in HC environments which, when combined with fast construction heuristics, can find shorter schedules on benchmark problems than other solution techniques found in the literature, and in significantly less time
Improving energy efficiency in wireless sensor networks through scheduling and routing
This paper is about the wireless sensor network in environmental monitoring
applications. A Wireless Sensor Network consists of many sensor nodes and a
base station. The number and type of sensor nodes and the design protocols for
any wireless sensor network is application specific. The sensor data in this
application may be light intensity, temperature, pressure, humidity and their
variations .Clustering and routing are the two areas which are given more
attention in this paper.Comment: 7 Pages, 2 Figures and 1 Tabl
Multiple Timescale Dispatch and Scheduling for Stochastic Reliability in Smart Grids with Wind Generation Integration
Integrating volatile renewable energy resources into the bulk power grid is
challenging, due to the reliability requirement that at each instant the load
and generation in the system remain balanced. In this study, we tackle this
challenge for smart grid with integrated wind generation, by leveraging
multi-timescale dispatch and scheduling. Specifically, we consider smart grids
with two classes of energy users - traditional energy users and opportunistic
energy users (e.g., smart meters or smart appliances), and investigate pricing
and dispatch at two timescales, via day-ahead scheduling and realtime
scheduling. In day-ahead scheduling, with the statistical information on wind
generation and energy demands, we characterize the optimal procurement of the
energy supply and the day-ahead retail price for the traditional energy users;
in realtime scheduling, with the realization of wind generation and the load of
traditional energy users, we optimize real-time prices to manage the
opportunistic energy users so as to achieve systemwide reliability. More
specifically, when the opportunistic users are non-persistent, i.e., a subset
of them leave the power market when the real-time price is not acceptable, we
obtain closedform solutions to the two-level scheduling problem. For the
persistent case, we treat the scheduling problem as a multitimescale Markov
decision process. We show that it can be recast, explicitly, as a classic
Markov decision process with continuous state and action spaces, the solution
to which can be found via standard techniques. We conclude that the proposed
multi-scale dispatch and scheduling with real-time pricing can effectively
address the volatility and uncertainty of wind generation and energy demand,
and has the potential to improve the penetration of renewable energy into smart
grids.Comment: Submitted to IEEE Infocom 2011. Contains 10 pages and 4 figures.
Replaces the previous arXiv submission (dated Aug-23-2010) with the same
titl
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Efficient Memory-Protected Integration of Add-On Software Subsystems in Small Embedded Automotive Applications
Current innovations in the automotive industry
evolve mainly in the electronics and software domain. This leads
to an increasing integration of additional software subsystems
into already existing electronic control units (ECUs) to cope with
the raised amount and complexity of present ECUs in modern
high-end vehicles. This paper discusses different approaches
which are required to integrate such add-on software subsystems
in an isolated memory domain, and considers particularly the
special needs of small embedded systems—including the limited
hardware support. Special focus is brought to the efficient detection
of malicious memory accesses, as well as the benefits of
a thereupon possible and adaptable failure-handling strategy.
All investigations are based on a developed memory-protection
framework which has been tailored to the special needs of a sample
vehicle dynamics control system. Its usage allows the combination
of. integrating additional subsystems without reducing the main
application’s availability
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