Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Provider Efficiency <s> In the cloud computing system, the schedule of computing resources is a critical portion of cloud computing study. An effective load balancing strategy is able to markedly improve the task throughput of cloud computing. Virtual machines are selected as a fundamental processing unit of cloud computing. The resources in cloud computing will increase sharply and vary dynamically due to the utilization of virtualization technology. Therefore, implementation of load balancing in cloud computing has become complicated and it is difficult to achieve. Multi-agent genetic algorithm (MAGA) is a hybrid algorithm of GA, whose performance is far superior to that of the traditional GA. This paper demonstrates the advantage of MAGA over traditional GA, and then exploits multi-agent genetic algorithms to solve the load balancing problem in cloud computing, by designing a load balancing model on the basis of virtualization resource management. Finally, by comparing MAGA with Minimum strategy, the experiment results prove that MAGA is able to achieve better performance of load balancing. <s> BIB001 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Provider Efficiency <s> Minimizing computing energy consumption has many benefits, such as environment protection, cost savings, etc. An important research problem is the energy aware task scheduling for cloud computing. For many diverse computers in a typical cloud computing system, great energy reduction can be achieved by smart optimization methods. The objective of energy aware task scheduling is to efficiently complete all assigned tasks to minimize energy consumption with various constraints. Genetic Algorithm (GA) is a popular and effective optimization algorithm. However, it is much slower than other traditional search algorithms such as heuristic algorithm. In this paper, we propose a shadow price guided algorithm (SGA) to improve the performance of energy aware task scheduling. Experiment results have shown that our energy aware task scheduling algorithm using the new SGA is more effective and faster than the standard GA. <s> BIB002 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Provider Efficiency <s> Reducing energy consumption is an increasingly important issue in cloud computing, more specifically when dealing with High Performance Computing (HPC). Minimizing energy consumption can significantly reduce the amount of energy bills and then increases the provider's profit. In addition, the reduction of energy decreases greenhouse gas emissions. Therefore, many researches are carried out to develop new methods in order to consume less energy. In this paper, we present a multi-objective genetic algorithm (MO-GA) that optimizes the energy consumption, CO 2 emissions and the generated profit of a geographically distributed cloud computing infrastructure. We also propose a greedy heuristic that aims to maximize the number of scheduled applications in order to compare it with the MO-GA. The two approaches have been experimented using realistic workload traces from Feitelson's PWA Parallel Workload Archive. The results show that MO-GA outperforms the greedy heuristic by a significant margin in terms of energy consumption and CO 2 emissions. In addition, MO-GA is also proved to be slightly better in terms of profit while scheduling more applications. <s> BIB003 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Provider Efficiency <s> In this paper, we proposed an algorithm for load distribution of workloads among nodes of a cloud by the use of Ant Colony Optimization (ACO). This is a modified approach of ant colony optimization that has been applied from the perspective of cloud or grid network systems with the main aim of load balancing of nodes. This modified algorithm has an edge over the original approach in which each ant build their own individual result set and it is later on built into a complete solution. However, in our approach the ants continuously update a single result set rather than updating their own result set. Further, as we know that a cloud is the collection of many nodes, which can support various types of application that is used by the clients on a basis of pay per use. Therefore, the system, which is incurring a cost for the user should function smoothly and should have algorithms that can continue the proper system functioning even at peak usage hours. <s> BIB004 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Provider Efficiency <s> With the development of Cloud Services, IaaS (Infrastructure as a Service) become a part of our daily life. And this paper is based on cloud management platform architecture which is in the testing phase. According to the experiment, we found a new way to solve the resources optimization problem about the resource scheduling of cloud management platform, and obtained satisfactory results. It indicated that ant colony algorithm could play a essential role in the problem about the resource scheduling of cloud management platform. <s> BIB005 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Provider Efficiency <s> It improves the algorithm because of the shortcoming that the ACO algorithm is easy to fall into local optimal solution in the cloud computing resource scheduling. The improved algorithm makes particle optimization inosculated into ant colony algorithm, which first finds out several groups of solutions using ACO algorithm according to the updated pheromone, and then gets more effective solutions using PSO algorithm to do crossover operation and mutation operation so as to avoid the algorithm prematurely into the local optimal solution. <s> BIB006 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Scheduling for Provider Efficiency <s> Cloud computing are becoming an important platform for scientific applications. Using virtualization in cloud computing it is possible to provide an on-demand provision of virtualized resources as a service, without any additional waiting time. Scheduling the tasks based on the deadline-constraint reduces the energy consumption, leading to a considerable decrease in energy cost. Energy consumption is the blue eye of task scheduling. But all the applications are not based on the deadline-constraint, so complexity of the problem arises. Hence the task scheduling need to be focused by considering the energy consumption and it has to be evaluated based on the quality of the schedules. This paper puts forth a hybrid algorithm which focuses on the reduction of energy consumption. This method is based on the voltage scaling factor to reduce the energy consumption. The result proves that by using this method, energy consumption can be minimized while scheduling the tasks. <s> BIB007
In addition to cloud resource scheduling oriented by the user's QoS, there also exist sizable works on scheduling the cloud resources from the viewpoint of provider efficiency. In this context, the scheduling objectives include (i) load balancing, (ii) utilization maximization, and (iii) minimization of energy consumption. The related works are summarized according to their optimization objectives in Table III and are described as follows. For optimizing the resource load balance, BIB001 proposed using a Multiagent GA (MAGA) to solve the scheduling problem. In order to reduce the size of tasks, a group strategy based on the task parameters was designed to divide the tasks into groups. The MAGA also uses an encoding scheme similar to that of Figure 5 , with the length being the same as the number of groups. However, MAGA does not use an integer for each dimension to indicate which VM the corresponding group is scheduled on, but uses a binary code. This is finer for crossover and mutation, but it needs to be converted to an integer when calculating the fitness. BIB004 also scheduled the cloud resource for an optimized load balance of different nodes, using an ACO-based algorithm. They argued that the ACO approach can first choose the node that has the greatest number of neighboring nodes. This way, the ant can travel in the most possible direction to find more nodes that are overloaded or underloaded. En route, the ant will detect the heavy-load nodes, and redistribute some load to the light-load nodes. In addition to load balancing, another objective in the provider efficiency oriented scheduling is resource utilization. BIB005 proposed using ACO to select proper physical resources for running the users' tasks. Their selection strategy aims to allocate heavy-load resources to the task, so as to save resources. That is, in every ant step their approach does not try to select the light-load resource to make load balance, but to select the heavy-load resource to maximize the resource utilization. BIB006 also improved the cloud resources utilization ratio by scheduling the cloud resources based on a hybrid algorithm consisting of ACO and PSO. Their approach uses ACO as the main process to select suitable resources for different tasks. The pheromone is associated with the resource node. When a resource node is selected for a new arrived task, the pheromone on this node is reduced. They argue that ACO may be trapped into local optimum, and hence hybridize the PSO process to avoid ACO prematurity. In the PSO, however, they did not use real numbers to calculate particle velocities and positions, but used crossover and mutation operations like in a GA to combine the search information of the globally best solution, the personal best solution, and the particle itself. Another objective for provider efficiency oriented scheduling is to save energy consumption of the cloud center. BIB002 argued that a conventional GA was slow, especially in large-scale cloud resource scheduling. Therefore, they proposed using a shadow price strategy to guide the GA evolution direction. The shadow price strategy is used to indicate the energy consumption of cloud resources. Their experiments show that their proposed GA-based resource scheduling approach is faster than the traditional GA-based scheduling approach to reduce energy consumption. divided the tasks into multijobs that contained "Map tasks" and "Reduce tasks" based on the provider Google's massive data processing framework. These tasks were scheduled by a modified GA with local search to optimize the energy consumption. BIB007 proposed a hybrid algorithm that combined the advantages of ACO and Cuckoo Search (CS) to schedule the tasks for saving energy. Their approach uses ACO as the main framework and applies CS instead of heuristic information to find the next resource for the task. However, the motivation and implementation details of the hybrid algorithm are not sufficiently clear in the article. In the work discussed previously, the researchers have focused on a single optimization objective oriented toward the cloud provider. In the literature, multiobjective optimization for cloud providers was also reported. In the study by BIB003 , a multiobjective GA was used to obtain Pareto solutions to minimal energy consumption, minimal CO2 emission, and maximal provider profits. Their method also used the encoding scheme similar to the one in Figure 4 . Therefore, the crossover and mutation operations are relatively straightforward to implement. Moreover, the approach has been evaluated with realistic workload traces from Feitelson's Parallel Workload Archive.