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Performance and energy consumption of a solid state disk(SSD) highly depend on file systems and I/O schedulers in operating systems. To find an optimal combination of a file system and an I/O scheduler for SSDs, we use a metric called the aggregative indicator(AI), which is the ratio of SSD performance value(e.g., data transfer rate in MB/s or throughput in IOPS) to that of energy consumption for an SSD. This metric aims to evaluate SSD performance per energy consumption and to study the SSD which delivers high performance at low energy consumption in a combination of a file system and an I/O scheduler. We also propose a metric called Cemp to study the changes of energy consumption and mean performance for an Intel SSD(SSD-I) when it provides the largest AI, lowest power, and highest performance, respectively. Using Cemp, we attempt to find the combination of a file system and an I/O scheduler to make SSD-I deliver a smooth change in energy consumption. We employ Filebench as a workload generator to simulate a wide range of workloads(i.e., varmail, fileserver, and webserver), and explore optimal combinations of file systems and I/O schedulers(i.e., optimal values of AI) for tested SSDs under different workloads. Experimental results reveal that the proposed aggregative indicator is comprehensive for exploring the optimal combination of a file system and an I/O scheduler for SSDs, compared with an individual metric.
Performance and energy consumption of a solid state disk (SSD) highly depend on file systems and I / O schedulers in operating systems. To find an optimal combination of a file system and an I / O scheduler for SSDs, we use a metric called the aggregative indicator (AI), which is the ratio of SSD performance value (eg, data transfer rate in MB / s or throughput in IOPS) to that of energy consumption for an SSD. This metric aims to evaluate SSD performance per energy consumption and to study the SSD which delivers high performance at low energy consumption in a combination of a file system and an I / O scheduler. We also propose a metric called Cemp to study the changes of energy consumption and mean performance for an Intel SSD (SSD-I Using Cemp, we attempt to find the combination of a file system and an I / O scheduler to make SSD-I deliver a smooth change in energy consumption. We employ Filebench as a workloa d generator to simulate a wide range of workloads (ie, varmail, fileserver, and webserver), and explore optimal combinations of file systems and I / O schedulers (ie, optimal values of AI) for tested SSDs under different workloads. that the proposed aggregative indicator is comprehensive for exploring the optimal combination of a file system and an I / O scheduler for SSDs, compared with an individual metric.