A Knowledge-based Adaptive Discrete Water Wave Optimization for Solving Cloud Workflow Scheduling

Abstract : Workflow scheduling in cloud environments has become a significant topic in both commercial and industrial applications. However, it is still an extraordinarily challenge to generate effective and economical scheduling schemes under the deadline constraint especially for the large scale workflow applications. To address the issue, this paper investigates the cloud workflow scheduling problem with the aim of minimizing the whole cost of workflow execution whereas maintaining its execution time under a predetermined deadline. A novel knowledge-based adaptive discrete water wave optimization (KADWWO) algorithm is developed based on the problem-specific knowledge of cloud workflow scheduling. In the proposed KADWWO, a discrete propagation operator is designed based on the idle time knowledge of hourly-based cost model to adaptively explore the huge search space. The adaptive refraction operator is employed to avoid stagnation and expand the available resource pool. Meanwhile, the dynamic grouping based breaking operator is designed to exploit the excellent block structure knowledge of task allocation scheme and corresponding resource to intensify the local region and accelerate convergence. Extensive simulation experiments on the well-known scientific workflow demonstrate that the KADWWO approach outperforms several recent state-of-the-art algorithms.

 EXISTING SYSTEM :

 ? Some efforts have been dedicated so far towards classifying the state of the art on nature- and bio-inspired optimization in a taxonomy with well-defined criteria, allowing researchers to classify existing algorithms and newly proposed schemes. ? This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. ? To the best of our knowledge, there has been no previous attempt as ambitious as the one presented in this overview to organize the existing literature on nature- and bio-inspired optimization.

 DISADVANTAGE :

 ? The flow-shop scheduling problem (FSP) has been a focus of extensive study in the operations research literature over the past several decades. ? Exact solutions are obtained by the exact methods in the optimization process; however, the computational complexity and storage of the algorithms increases with increasing problem size. ? Constructive heuristics consume less time to obtain the best solutions, but the solution quality of the specific problems usually does not satisfy the requirements of the problem. ? However, setting reasonable upper and lower bounds for the parameters according to the scale of the problem is still a key issue.

 PROPOSED SYSTEM :

 • The proposed AGA makes balance of Quality of Service attributes without violating soft and hard constraints. • A major fraction of the publications comprising this plot proposed new bio-inspired algorithms at their time. • Considering the classifications obtained in our wide study, we have critically examined the reviewed literature classification in the different taxonomies proposed in this work. • In our classification, categories laying at the same level are disjoint sets, which involves that each proposed algorithm can be only a member of one of these categories. • When new algorithms are proposed, unfortunately many of them are only compared to very basic and classical algorithms.

 ADVANTAGE :

 ? The performance of SWWO benefits from the combination of the self-adaptability of WWO with the depth search of the block-shift operator. ? The results show that the convergence performance and stability of SWWO are better than state-of-the-art algorithms for the NWFSP. ? The superior performance of SWWO comes from the combination of the self-adaptability of WWO with the depth search of the block-shift operator. ? Although various methods have been proposed and applied to solve the NWFSP, practitioners and researchers are still pursuing efficient methods. ? The direct motivation for SWWO is to continually explore efficient algorithms to solve the NWFSP in certain manufacturing environments.