摘要
随着互联网和IT技术的迅猛发展，数据增长量和计算要求迅速增长，导致计算机的数据处理能力和计算能力不能满足用户的需求，用户对网络资源的需求和资源的利用率出现了失衡的状态。云计算使得人们可以更为灵活地配置、调度和使用资源，从而可以很好地解决上述问题。
在云计算中，资源调度是一个NP-hard问题，因为随着用户的增多，大规模的任务调度具有很高的复杂性和不确定性。如何对用户任务进行合理的分配和保证资源负载均衡是研究的关键。
文中针对云计算中的任务调度，主要分为任务到资源的调度和资源监控两个过程。本文根据对用户任务的研究，建立用户任务输入向量，对用户任务进行了分类。在任务到资源的调度过程中对遗传算法进行了改进，加入了兼顾效率和公平的双重适应函数，通过实验验证了算法的高效性和公平性，提高了任务执行的效率和资源分配的公平性。
资源监控方面，文中提出资源动态迁移和动态分配相结合的方法。动态迁移是根据对物理资源的使用情况监控的结果，在物理资源使用情况大于最大阈值和小于最小阈值时根据选择和分配策略对虚拟机资源进行迁移，来提高用户服务等级协议，保证资源负载均衡和降低能耗。
动态扩展是当资源的某个参数使用情况都超过了阈值，考虑资源的动态分配，而分配资源的多少根据剩余任务的多少和虚拟机资源的处理能力来决定，从而保证资源分配的合理性和提高任务的执行效率。
最后通过CloudSim仿真平台对R_Migrate_Extend(资源动态迁移和动态分配)方法和R_NoMigrate(物理资源不迁移)、R_Migrate(物理资源迁移)、R_NoMigrate_Extend(物理资源不迁移和资源动态扩展)三个方法相比较。文中改进的方法R_Migrate_Extend明显的满足了用户服务等级协议，并且在能量消耗上稍微降低，从而达到了提高用户服务质量，保证资源负载平衡和降低能耗的目的。
关键词：任务调度；资源监控；遗传算法；资源动态迁移；资源动态分配
Abstract
With the rapid development of the Internet and information technologies,the data volume and the corresponding computation requirements have increased quickly.The computing power of traditional computation models cannot meet the needs of users,thus the supply and demand of network resources are imbalanced.Cloud computing enables people to configure,schedule,and utilize resources more flexibly and can solve the problem above.
In cloud computing,resource scheduling is a NP-hard problem,because large-scale task scheduling is very complex and uncertain with the increase of users.It is a key to study how to properly allocate and ensure the resource load balance.
In this thesis,the task scheduling in cloud computing is divided into two parts,i.e., task-to-resource scheduling and resource monitoring.Based on the research of user tasks, this thesis classifies user tasks and then establishes the corresponding input models.In the process of task resource scheduling,a genetic algorithm is extended,and a dual adaptive function is added,which takes both efficiency and fairness into account.The performance of the proposed algorithm is verified by experiments.
In terms of resource monitoring,the thesis introduces a combination of dynamic migration and dynamic allocation for resources.The dynamic migration is based on the usage of monitored physical resources.When the usage of physical resources crosses the maximum threshold or the minimum threshold,the resources of virtual machines are migrated according to the selection and allocation strategies,which can satisfy the users’service protocol,ensure resource load-balancing and reduce energy consumption.
When any parameter of a resource exceeds the pre-defined threshold,the dynamic allocation of resources will be executed.And how much resources to be allocated is determined by the number of remaining tasks and the processing capacity of resources.The allocation strategy proposed in this thesis can ensure the rationality of resource allocation and improve the efficiency of task execution.
Finally,R_Migrate_Extend(dynamic migration and allocation of resources)proposed in this thesis is compared with R_NoMigrate,R_Migrate and R_NoMigrate_Extend on the CloudSim simulation platform.Experiments show that R_Migrate_Extend can satisfy the users’service protocol and reduce the energy consumption slightly.The goals of improving the quality of services for users,ensuring resource load-balancing and reducing energy consumption have been reached.