28,261 research outputs found
Cloud computing resource scheduling and a survey of its evolutionary approaches
A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon
A WOA-based optimization approach for task scheduling in cloud Computing systems
Task scheduling in cloud computing can directly
affect the resource usage and operational cost of a system. To
improve the efficiency of task executions in a cloud, various
metaheuristic algorithms, as well as their variations, have been
proposed to optimize the scheduling. In this work, for the
first time, we apply the latest metaheuristics WOA (the whale
optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that
basis, we propose an advanced approach called IWC (Improved
WOA for Cloud task scheduling) to further improve the optimal
solution search capability of the WOA-based method. We present
the detailed implementation of IWC and our simulation-based
experiments show that the proposed IWC has better convergence
speed and accuracy in searching for the optimal task scheduling
plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource
utilization, in the presence of both small and large-scale tasks
Efficient Generation of Parallel Spin-images Using Dynamic Loop Scheduling
High performance computing (HPC) systems underwent a significant increase in
their processing capabilities. Modern HPC systems combine large numbers of
homogeneous and heterogeneous computing resources. Scalability is, therefore,
an essential aspect of scientific applications to efficiently exploit the
massive parallelism of modern HPC systems. This work introduces an efficient
version of the parallel spin-image algorithm (PSIA), called EPSIA. The PSIA is
a parallel version of the spin-image algorithm (SIA). The (P)SIA is used in
various domains, such as 3D object recognition, categorization, and 3D face
recognition. EPSIA refers to the extended version of the PSIA that integrates
various well-known dynamic loop scheduling (DLS) techniques. The present work:
(1) Proposes EPSIA, a novel flexible version of PSIA; (2) Showcases the
benefits of applying DLS techniques for optimizing the performance of the PSIA;
(3) Assesses the performance of the proposed EPSIA by conducting several
scalability experiments. The performance results are promising and show that
using well-known DLS techniques, the performance of the EPSIA outperforms the
performance of the PSIA by a factor of 1.2 and 2 for homogeneous and
heterogeneous computing resources, respectively
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