Benchmarks are widely used to perform apples-to-apples comparison in a controlled and reliable fashion. Benchmarks must model real world workload behavior. In recent years, to meet web scale demands, Key-Value (KV) stores have emerged as a vital component of cloud serving systems. The Yahoo! Cloud Serving Benchmark (YCSB) has emerged as the standard benchmark for evaluating key-value systems, and has been preferred by both the industry and academia. Though YCSB provides a variety of options to generate realistic workloads, like most benchmarks it has ignored the temporal characteristics of generated workloads. YCSB's constant-rate request arrival process is unrealistic and fails to capture the real world arrival patterns. Existing workload studies on disk, filesystem, key-value system, network, and web traffic all show that they all exhibit some common temporal properties such as burstiness, self similarity, long range dependence, and diurnal activity. In this work, we show that the commonly observed traffic patterns can be modeled using the three categories of arrival processes: a)Poisson, b)Self similar, and c)Envelope-guided process. The three categories presented are a necessary and sufficient set of request arrival models that all storage benchmarks should provide. To demonstrate the ease of incorporating the models in benchmarks, we have modified YCSB to generate workloads based on all three models, and show the effect of realistic request arrivals through an example database evaluation.
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