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1. To Ship or Not to (Function) Ship

   Liu, Feilong; Kamat, Niranjan; Blanas, Spyros; Nandi, Arnab (September 2018, 2018 IEEE High Performance Extreme Computing Conference (HPEC '18))

   Sampling is often used to reduce query latency for interactive big data analytics. The established parallel data processing paradigm relies on function shipping, where a coordinator dispatches queries to worker nodes and then collects the results. The commoditization of high-performance networking makes data shipping possible, where the coordinator directly reads data in the workers’ memory using RDMA while workers process other queries. In this work, we explore when to use function shipping or data shipping for interactive query processing with sampling. Whether function shipping or data shipping should be preferred depends on the amount of data transferred, the current CPU utilization and the sampling method. The results show that data shipping is up to 6.5× faster when performing clustered sampling with heavily-utilized workers. 

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2. Ship Compute or Ship Data? Why Not Both?

   You, Jie; Wu, Jingfeng; Jin, Xin; Chowdhury, Mosharaf (January 2021, NSDI 2021)

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3. Ship Compute or Ship Data? Why Not Both?

   You, Jie; Wu, Jingfeng; Jin, Xin; Chowdhury, Mosharaf (April 2021, 18th USENIX Symposium on Networked Systems Design and Implementation)

   null (Ed.)

   How cloud applications should interact with their data remains an active area of research. Over the last decade, many have suggested relying on a key-value (KV) interface to interact with data stored in remote storage servers, while others have vouched for the benefits of using remote procedure call (RPC). Instead of choosing one over the other, in this paper, we observe that an ideal solution must adaptively combine both of them in order to maximize throughput while meeting application latency requirements. To this end, we propose a new system called Kayak that proactively adjusts the rate of requests and the fraction of requests to be executed using RPC or KV, all in a fully decentralized and self-regulated manner. We theoretically prove that Kayak can quickly converge to the optimal parameters. We implement a system prototype of Kayak. Our evaluations show that Kayak achieves sub-second convergence and improves overall throughput by 32.5%-63.4% for compute-intensive workloads and up to 12.2% for non-compute-intensive and transactional workloads over the state-of-the-art. 

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4. Abandoning Galileo's Ship

   Ramirez, Sebastian; Teh, Nicholas (December 2021, The British journal for the philosophy of science)
5. Navigating Arctic Waters: A Summary of Ship Activities and Ice-Ship Interactions in Alaskan Waters

   Peel, G. J.; Gedikli, E. D.; Hendrikse, H. (June 2023, Proceedings of the International Conference on Port and Ocean Engineering under Arctic Conditions)