Resource allocation in a distributed computing system is the process of allocating the workload across multiple computing resources to optimize the required performance criteria. In this article, a resource allocation problem that arises in a distributed system consisting of multiple heterogeneous servers is addressed. The problem is modeled as a multi-objective problem with two conflicting objectives: (a) to minimize the users’ expected response time and (b) to reduce the utilization imbalance between servers. To satisfy these objectives simultaneously, first, both the objectives are considered in an integrated manner, and an optimization problem is formulated. Second, the optimization problem is cast into a game-theoretic setting and modeled as a non-cooperative game, called a non-cooperative resource allocation game. Finally, to solve the game, a differential evolution-based co-evolutionary framework (DECEF) is proposed. To evaluate the performance of DECEF, a rigorous simulation study is carried out. Furthermore, to assess the relative performance of DECEF, it is compared against two existing approaches, from various aspects, including system utilization, system heterogeneity, and system size. The experimental results show that DECEF provides better system-wide performance while optimizing both the objectives.

中文翻译：

---

分布式计算系统中优化多目标资源分配的进化方法

分布式计算系统中的资源分配是跨多个计算资源分配工作负载以优化所需性能标准的过程。在本文中，解决了在由多个异构服务器组成的分布式系统中出现的资源分配问题。该问题被建模为具有两个相互冲突目标的多目标问题：(a) 最小化用户的预期响应时间和 (b) 减少服务器之间的利用率不平衡。为了同时满足这些目标，首先综合考虑这两个目标，并制定优化问题。其次，将优化问题转化为博弈论设置并建模为非合作博弈，称为非合作资源分配博弈。最后，为了解决游戏，提出了一种基于差分进化的协同进化框架（DECEF）。为了评估 DECEF 的性能，进行了严格的模拟研究。此外，为了评估 DECEF 的相对性能，它与两种现有方法进行了比较，从各个方面，包括系统利用率、系统异构性和系统规模。实验结果表明 DECEF 在优化两个目标的同时提供了更好的系统性能。