Accelerating Big-Data Sorting Through Programmable Switches (2103.14071v1)

Abstract: Sorting is a fundamental and well studied problem that has been studied extensively. Sorting plays an important role in the area of databases, as many queries can be served much faster if the relations are first sorted. One of the most popular sorting algorithm in databases is merge sort. In modern data-centers, data is stored in storage servers, while processing takes place in compute servers. Hence, in order to compute queries on the data, it must travel through the network from the storage servers to the compute servers. This creates a potential for utilizing programmable switches to perform partial sorting in order to accelerate the sorting process at the server side. This is possible because, as mentioned above, data packets pass through the switch in any case on their way to the server. Alas, programmable switches offer a very restricted and non-intuitive programming model, which is why realizing this is not-trivial. We devised a novel partial sorting algorithm that fits the programming model and restrictions of programmable switches and can expedite merge sort at the server. We also utilize built-in parallelism in the switch to divide the data into sequential ranges. Thus, the server needs to sort each range separately and then concatenate them to one sorted stream. This way, the server needs to sort smaller sections and each of these sections is already partially sorted. Hence, the server does less work, and the access pattern becomes more virtual-memory friendly. We evaluated the performance improvements obtained when utilizing our partial sorting algorithm over several data stream compositions with various switch configurations. Our study exhibits an improvement of 20%-75% in the sorting run-time when using our approach compared to plain sorting on the original stream.