We propose and study a predator prey model with state-dependent delay where the prey population is assumed to have an age structure. The state-dependent delay appears due to the mature condition that the prey must spe...We propose and study a predator prey model with state-dependent delay where the prey population is assumed to have an age structure. The state-dependent delay appears due to the mature condition that the prey must spend an amount of time in the immature stage sufficient to accumulate a threshold amount of food. We perform a qualitative analysis of the solutions, which includes studying positivity and boundedness, existence and local stability of equilibria. For the global dynamics of the system, we discuss an attracting region which is determined by solutions, and the region collapses to the interior equilibrium in the constant delay case.展开更多
基于固态硬盘(solid-state drive,SSD)和硬盘(hard disk drive,HDD)混合存储的数据中心已经成为大数据计算领域的高性能载体,数据中心负载应该可将不同特性的数据按需持久化到SSD或HDD,以提升系统整体性能.Spark是目前产业界广泛使用的...基于固态硬盘(solid-state drive,SSD)和硬盘(hard disk drive,HDD)混合存储的数据中心已经成为大数据计算领域的高性能载体,数据中心负载应该可将不同特性的数据按需持久化到SSD或HDD,以提升系统整体性能.Spark是目前产业界广泛使用的高效大数据计算框架,尤其适用于多次迭代计算的应用领域,其原因在于Spark可以将中间数据持久化在内存或硬盘中,且持久化数据到硬盘打破了内存容量不足对数据集规模的限制.然而,当前的Spark实现并未专门提供显式的面向SSD的持久化接口,尽管可根据配置信息将数据按比例分布到不同的存储介质中,但是用户无法根据数据特征按需指定RDD的持久化存储介质,针对性和灵活性不足.这不仅成为进一步提升Spark性能的瓶颈,而且严重影响了混合存储系统性能的发挥.有鉴于此,首次提出面向SSD的数据持久化策略.探索了Spark数据持久化原理,基于混合存储系统优化了Spark的持久化架构,最终通过提供特定的持久化API实现用户可显式、灵活指定RDD的持久化介质.基于SparkBench的实验结果表明,经本方案优化后的Spark与原生版本相比,其性能平均提升14.02%.展开更多
文摘We propose and study a predator prey model with state-dependent delay where the prey population is assumed to have an age structure. The state-dependent delay appears due to the mature condition that the prey must spend an amount of time in the immature stage sufficient to accumulate a threshold amount of food. We perform a qualitative analysis of the solutions, which includes studying positivity and boundedness, existence and local stability of equilibria. For the global dynamics of the system, we discuss an attracting region which is determined by solutions, and the region collapses to the interior equilibrium in the constant delay case.
文摘基于固态硬盘(solid-state drive,SSD)和硬盘(hard disk drive,HDD)混合存储的数据中心已经成为大数据计算领域的高性能载体,数据中心负载应该可将不同特性的数据按需持久化到SSD或HDD,以提升系统整体性能.Spark是目前产业界广泛使用的高效大数据计算框架,尤其适用于多次迭代计算的应用领域,其原因在于Spark可以将中间数据持久化在内存或硬盘中,且持久化数据到硬盘打破了内存容量不足对数据集规模的限制.然而,当前的Spark实现并未专门提供显式的面向SSD的持久化接口,尽管可根据配置信息将数据按比例分布到不同的存储介质中,但是用户无法根据数据特征按需指定RDD的持久化存储介质,针对性和灵活性不足.这不仅成为进一步提升Spark性能的瓶颈,而且严重影响了混合存储系统性能的发挥.有鉴于此,首次提出面向SSD的数据持久化策略.探索了Spark数据持久化原理,基于混合存储系统优化了Spark的持久化架构,最终通过提供特定的持久化API实现用户可显式、灵活指定RDD的持久化介质.基于SparkBench的实验结果表明,经本方案优化后的Spark与原生版本相比,其性能平均提升14.02%.
