Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Real-Time 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> BIB001 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Real-Time Scheduling <s> The development of autonomous unmanned aerial vehicles (UAVs) is of high interest to many governmental and military organizations around the world. An essential aspect of UAV autonomy is the ability for automatic path planning. In this paper, we use the genetic algorithm (GA) and the particle swarm optimization algorithm (PSO) to cope with the complexity of the problem and compute feasible and quasi-optimal trajectories for fixed wing UAVs in a complex 3D environment, while considering the dynamic properties of the vehicle. The characteristics of the optimal path are represented in the form of a multiobjective cost function that we developed. The paths produced are composed of line segments, circular arcs and vertical helices. We reduce the execution time of our solutions by using the “single-program, multiple-data” parallel programming paradigm and we achieve real-time performance on standard commercial off-the-shelf multicore CPUs. After achieving a quasi-linear speedup of 7.3 on 8 cores and an execution time of 10 s for both algorithms, we conclude that by using a parallel implementation on standard multicore CPUs, real-time path planning for UAVs is possible. Moreover, our rigorous comparison of the two algorithms shows, with statistical significance, that the GA produces superior trajectories to the PSO. <s> BIB002
In a cloud computing environment, the cloud resources and the user requests can change dynamically. Therefore, a scheduling approach should be smart enough to make real-time responses to a changing environment. Most of the existing EC works in scheduling cloud resources are centralized approaches because they consider the scheduling problem as a whole and find an optimal solution offline. Although centralized algorithms can use global information to help the scheduling, they often face difficulties online for a real-time response to environmental changes. So far, real-time optimization is still a challenge to EC algorithms because of the population-and iteration-based characteristics of EC algorithms, despite efforts made on real-time algorithm design BIB002 . One way to tackle such a challenge is to adapt to an online micro EC algorithm with a tiny population size or to augment with local learning on top of an offline solution. However, as cloud scheduling can face challenges of a high volume, velocity, and variety of data arriving during the runtime, a more efficient use of search information and problem information during the evolutionary process should be explored BIB001 . Moreover, machine learning, data mining, and certain other techniques can be used on these data to predict the cloud user's requirements, cloud resource variations, and other characteristics to guide the search ahead or faster for more efficient cloud resource scheduling.