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Dynamic resource reservation via broker federation in cloud service: A fine-grained heuristic-based approach

机译:通过云服务中的代理联合来动态保留资源:一种基于启发式的细粒度方法

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In cloud computing, Infrastructure-as-a-Service (IaaS) cloud providers can offer two types of purchasing plans for cloud users, including on-demand plan and reservation plan. Generally reservation price is cheaper than on-demand price, while reservation plan may cause highly underutilized capacity problem. How to joint optimize the service cost and the resource utilization for clouds is a critical issue. To address this issue, a novel steady broker federation is developed to coordinate service demands in this paper. And the optimal reservation problem can be formulated as a nonlinear integer programming model. Then a fine-grained heuristic algorithm is proposed to reduce its computational complexity and obtain quasi-optimal solutions. Numerical simulations driven by large-scale Parallel Workloads Archive demonstrate that the proposed approach can save considerable costs for cloud users and improves the resource utilization for IaaS cloud providers.
机译:在云计算中,基础设施即服务(IaaS)云提供商可以为云用户提供两种类型的购买计划,包括按需计划和预留计划。通常,预留价格比按需价格便宜,而预留计划可能会导致利用率严重不足的问题。如何共同优化云的服务成本和资源利用率是一个关键问题。为了解决这个问题,本文建立了一个新颖的稳定经纪人联合会来协调服务需求。最优预留问题可以表述为非线性整数规划模型。然后提出了一种细粒度的启发式算法,以降低其计算复杂度并获得准最优解。大规模并行工作量档案驱动的数值模拟表明,该方法可以为云用户节省大量成本,并提高IaaS云提供商的资源利用率。

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