一种基于服务选取的SBS云资源优化分配方法

获取满足全局优化目标的资源分配策略,是影响云环境中基于服务的软件系统(service-based software system,简称SBS)运行时优化效果的关键.然而,由于SBS内部复杂的业务逻辑关系和云环境中的资源约束,现有分配方法无法得到最优资源分配量.以满足SLA约束和最小化资源成本为目标,根据不同资源状态对应不同组件服务性能的特点,将组件服务可能的资源分配量、相应性能及成本转换为备选逻辑服务集,进而提出了一种云环境中基于服务选取的SBS资源优化分配模型,并设计了一种求解模型的混合遗传算法.算法采用整数编码以提高求解效率,并在选择算子中引入了精英保留策略,从而保证收敛到全局最优解.为提高遗传算法的局部搜索能力、加快收敛速度,以局部搜索策略改进了标准变异算子.实验验证了所提出的资源优化分配模型和求解算法的有效性,并表明:与分支定界法及精英保留策略遗传算法相比,混合遗传算法能够在较大规模的问题上快速获得具有较低资源成本的资源分配策略.

Service Selection Based Resource Allocation for SBS in Cloud Environments

Runtime adaptation of service-based software systems (SBS) in cloud environments is a key to acquire resource allocation strategy that meets global optimization goals. However, because of the complex business logic in SBS as well as cloud resource constraints, the optimal resource allocation cannot be obtained using existing methods. This paper offers an approach to meet SLA and minimize resource costs by exploiting the fact that different resource states result in different performance of component services. With the new method, the potential resource allocation for component services, together with responding performance and resource costs, are first transformed into candidate logical service sets. Then a service selection based resource allocation model for SBS in the cloud is constructed. A hybrid genetic algorithm is also designed for solving the model. Integer encoding is applied in the algorithm to improve the efficiency and an elitism maintenance strategy is introduced into selection operator to ensure its convergence to the global optimal solution. In order to improve the local search ability of genetic algorithm and speed up the convergence speed, the standard mutation operator is replaced by local search. Experiments validate the effectiveness of the proposed resource allocation model and its algorithm, and show that the presented algorithm can obtain the resource allocation strategy quickly with lower cost on large-scale problems than the branch and bound method and elitism genetic algorithm.