Towards Sustainable In-Situ Server Systems in the Big Data Era

Recent years have seen an explosion of data volumes from a myriad of distributed sources such as ubiquitous cameras and various sensors. The challenges of analyzing these geographically dispersed datasets are increasing due to the significant data movement overhead, time-consuming data aggregation, and escalating energy needs. Rather than constantly move a tremendous amount of raw data to remote warehouse-scale computing systems for processing, it would be beneficial to leverage in-situ server systems (inS) to pre-process data, i.e., bringing computation to where the data is located. This paper takes the first step towards designing server clusters for data processing in the field. We investigate two representative in-situ computing applications, where data is normally generated from environmentally sensitive areas or remote places that lack established utility irifrastructure. These very special operating environments of in-situ servers urge us to explore standalone (i.e., off-grid) systems that offer the opportunity to benefit from local, self-generated energy sources. in this work we implement a heavily instrumented proof-of-concept prototype called inSURE: in-situ server systems using renewable energy. We develop a novel energy buffering mechanism and a unique joint spatio-temporal power management strategy to coordinate standalone power supplies and in-situ servers. We present detailed deployment experiences to quantify how our design fits with in-situ processing in the real world. Overall, inSURE yields 20%similar to 60% improvements over a state-of-the-art baseline. it maintains impressive control effectiveness in under-provisioned environment and can economically scale along with the data processing needs. The proposed design is well complementary to today's grid-connected cloud data centers and provides competitive cost-effectiveness.