Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Large-Scale Scheduling <s> Cloud computing has become a main medium for Software as a Service (SaaS) hosting as it can provide the scalability a SaaS requires. One of the challenges in hosting the SaaS is the placement process where the placement has to consider SaaS interactions between its components and SaaS interactions with its data components. A previous research has tackled this problem using a classical genetic algorithm (GA) approach. This paper proposes a cooperative coevolutionary algorithm (CCEA) approach. The CCEA has been implemented and evaluated and the result has shown that the CCEA has produced higher quality solutions compared to the GA. <s> BIB001 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Large-Scale Scheduling <s> Evolutionary computation (EC) is a kind of optimization methodology inspired by the mechanisms of biological evolution and behaviors of living organisms. In the literature, the terminology evolutionary algorithms is frequently treated the same as EC. This article focuses on making a survey of researches based on using ML techniques to enhance EC algorithms. In the framework of an ML-technique enhanced-EC algorithm (MLEC), the main idea is that the EC algorithm has stored ample data about the search space, problem features, and population information during the iterative search process, thus the ML technique is helpful in analyzing these data for enhancing the search performance. The paper presents a survey of five categories: ML for population initialization, ML for fitness evaluation and selection, ML for population reproduction and variation, ML for algorithm adaptation, and ML for local search. <s> BIB002 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Large-Scale Scheduling <s> This paper presents a new cooperative coevolving particle swarm optimization (CCPSO) algorithm in an attempt to address the issue of scaling up particle swarm optimization (PSO) algorithms in solving large-scale optimization problems (up to 2000 real-valued variables). The proposed CCPSO2 builds on the success of an early CCPSO that employs an effective variable grouping technique random grouping. CCPSO2 adopts a new PSO position update rule that relies on Cauchy and Gaussian distributions to sample new points in the search space, and a scheme to dynamically determine the coevolving subcomponent sizes of the variables. On high-dimensional problems (ranging from 100 to 2000 variables), the performance of CCPSO2 compared favorably against a state-of-the-art evolutionary algorithm sep-CMA-ES, two existing PSO algorithms, and a cooperative coevolving differential evolution algorithm. In particular, CCPSO2 performed significantly better than sep-CMA-ES and two existing PSO algorithms on more complex multimodal problems (which more closely resemble real-world problems), though not as well as the existing algorithms on unimodal functions. Our experimental results and analysis suggest that CCPSO2 is a highly competitive optimization algorithm for solving large-scale and complex multimodal optimization problems. <s> BIB003
With the rapid progress of cloud computing, the scales of resources, users, tasks, and workflows have continuously grown. In the future, more and more tasks will be processed in the cloud environment and more and more cloud resources need to be managed and scheduled in the Internet-pervasive environment. Such large-scale cloud resources and demands challenge the current scheduling approaches. Research on developing algorithms for large-scale cloud computing scheduling will emerge as a new trend. A challenge of large-scale scheduling can be that the search landscape of the optimization problem becomes too complex, for example, with too many local optima, that scheduling approaches cannot efficiently find good global solutions. Fortunately, EC is a promising paradigm for large-scale scheduling, because it offers acceptable solutions in (nondeterministic) polynomial time, although how to design a time-efficient large-scale EC algorithm is still an open problem in the EC community BIB003 . In future research, designing efficient partition strategies to divide large-scale resources into smaller ones and scheduling these small-scale problems with coevolution BIB001 BIB002 ] could be a promising way forward. Moreover, integrating characteristics of cloud resources into algorithm design could also be a useful means.