Serverless computing promises auto-scalability and costefficiency (in "pay-as-you-go" manner) for high-productive software development. Because of its virtue, serverless computing has motivated increasingly new applications and services in the cloud. This, however, also presents new challenges including how to efficiently design high-performance serverless platforms and how to efficiently program on the platforms. This paper proposes ServerlessBench, an open-source benchmark suite for characterizing serverless platforms. It includes test cases exploring characteristic metrics of serverless computing, e.g., communication efficiency, startup latency, stateless overhead, and performance isolation. We have applied the benchmark suite to evaluate the most popular serverless computing platforms, including AWS Lambda, Open-Whisk, and Fn, and present new serverless implications from the study. For example, we show scenarios where decoupling an application into a composition of serverless functions can be beneficial in cost-saving and performance, and that the "stateless" property in serverless computing can hurt the execution performance of serverless functions. These implications form several design guidelines, which may help platform

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The increase in the capacity of main memory coupled with the decrease in cost has fueled the development of inmemory database systems that manage data entirely in memory, thereby eliminating the disk I/O bottleneck. However, as we shall explain, in the Big Data era, maintaining all data in memory is impossible, and even unnecessary. Ideally we would like to have the high access speed of memory, with the large capacity and low price of disk. This hinges on the ability to effectively utilize both the main memory and disk.In this paper, we analyze state-of-the-art approaches to achieving this goal for in-memory databases, which is called as "Anti-Caching" to distinguish it from traditional caching mechanisms. We conduct extensive experiments to study the effect of each fine-grained component of the entire process of "Anti-Caching" on both performance and prediction accuracy. To avoid the interference from other unrelated components of specific systems, we implement these approaches on a uniform platform to ensure a fair comparison. We also study the usability of each approach, and how intrusive it is to the systems that intend to incorporate it. Based on our findings, we propose some guidelines on designing a good "Anti-Caching" approach, and sketch a general and efficient approach, which can be utilized in most in-memory database systems without much code modification.978-1-4799-7964-6/15/$31.00

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