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Efficient Computation Offloading Using Grey Wolf Optimization Algorithm

机译:高效计算卸载使用灰狼优化算法

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The widely available smart-phones as well as other mobile devices can be utilized as valuable compute resources with the support of the powerful and elastic paradigm of cloud computing. This is possible by performing computations of a limited scale on mobile devices and migrating or offloading complex services to the cloud. Use of mobile devices for computations will enable a plethora of applications to execute on mobile devices alone without dependence on static infrastructure. However, this entails the cost of transfer of computations to the cloud along with the cost of cloud computing resources. This paper formulates computation offloading as an optimization problem and utilizes nature-inspired approach of Grey Wolf Algorithm (GWO) to achieve near-optimal solutions. Results precisely depict that although the best cost solutions are attained by the brute force technique but the number of computations is significantly higher as compared to Grey Wolf Algorithm. Moreover with the small increase in number of tasks, there is exponential increase in number of computations. Considering these tradeoffs, its more appropriate to use nature-inspired algorithms for computation offloading in a mobile cloud computing environment.
机译:随着云计算的强大和弹性范例,可以使用广泛可用的智能手机以及其他移动设备作为有价值的计算资源。这可以通过在移动设备上执行有限尺度的计算并将复载或卸载到云的复合服务来实现这一点。使用移动设备的计算将使多种应用程序能够单独在移动设备上执行,而无需依赖静态基础架构。但是,这需要将计算转移到云的成本以及云计算资源的成本。本文将计算卸载为优化问题,利用灰狼算法(GWO)的自然启发方法实现近乎最佳解决方案。结果精确描述了,尽管蛮力技术获得了最佳成本解决方案,但与灰狼算法相比,计算的数量显着提高。此外,随着任务数量的小幅增加,计算数量的指数增加。考虑到这些权衡,它更适合在移动云计算环境中使用自然启发算法进行计算卸载。

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