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COPS: Cost Based Object Placement Strategies on Hybrid Storage System for DBaaS Cloud

机译:COPS:DBAAS云混合存储系统的基于成本的对象放置策略

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Solid State Drives (SSD) are integrated together with Hard Disk Drives (HDD) in Hybrid Storage Systems (HSS) for Cloud environment. When it comes to storing data, some placement strategies are used to find the best location (SSD or HDD). These strategies should minimize the cost of data placement while satisfying Service Level Objectives (SLO). This paper presents two Cost based Object Placement Strategies (COPS) for DBaaS objects in HSS: a Genetic based approach (G-COPS) and an ad-hoc Heuristic approach (H-COPS) based on incremental optimization. While G-COPS proved to be closer to the optimal solution in case of small instances, HCOPS showed a better scalability as it approached the exact solution even for large instances (by 10% in average). In addition, H-COPS showed small execution times (few seconds) even for large instances which makes it a good candidate to be used in runtime. Both H-COPS and G-COPS performed better than state-of-the-art solutions as they satisfied SLOs while reducing the overall cost by more than 40% for problems of small and large instances.
机译:固态驱动器(SSD)与混合存储系统(HSS)中的硬盘驱动器(HDD)集成在一起,用于云环境。谈到存储数据时,某些放置策略用于查找最佳位置(SSD或HDD)。这些策略应尽量减少数据展示位置的成本,同时满足服务级别目标(SLO)。本文为HSS中的DBAAS对象提供了两种基于成本的对象放置策略(COPS):基于基于遗传的方法(G-COP)和基于增量优化的Ad-hoc启发式方法(H-COP)。虽然在小型情况下,G-COPS被证明更接近最佳解决方案,但HCOPS表示更好的可扩展性,因为它即使大型实例(平均10%)也接近精确的解决方案。此外,H-COPS即使对于大型实例而言,H-COPS也显示出小的执行时间(几秒钟),这使得它在运行时使用的良好候选者。 H-COPS和G-COPS均优于最先进的解决方案,因为它们满足SLOS,同时将整体成本降低超过40%,对于小型和大型实例的问题。

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