Captain Buzz

This is the author accepted manuscript. The final version is available from ACM at https://dl.acm.org/citation.cfm?id=2750682.Fully autonomous hobbyist drones are typically controlled using bespoke microcontrollers, or general purpose low-level controllers such as the Arduino. However, these devices only have limited compute power and sensing capabilities, and do not easily provide cellular connectivity options. We present Captain Buzz, an Android smartphone app capable of piloting a delta-wing glider autonomously. Captain Buzz can control servos directly via pulse width modulation sig- nals transmitted over the smartphone audio port. Compared with traditional approaches to building an autopilot, Cap- tain Buzz allows users to leverage existing Android libraries for flight attitude determination, provides innovative use- cases, allows users to reprogram their autopilot mid-flight for rapid prototyping, and reduces the cost of building drones.This work was supported by Google Inc., the Engineering and Physical Sciences Research Council; and CSR, Cambridge

Research data supporting "Soroban: Attributing Latency in Virtualized Environments"

This is data required for reproducing the figures in the "Soroban: Attributing Latency in Virtualized Environments" paper. It consists of two archives: [rscfl-exp.zip] containing (i) raw data from kernel level measurements of lighttpd serving requests while running in a virtualized environment (in json format, the .dat and .mdat metadata files) (ii) post-processed measurements with the data filtered and added into python pandas.DataFrame objects for querying and plotting (in standard binary python pickle serialization format, .sdat files) (iii) attribution model files containing serializations of python sklearn.gaussian_process.GaussianProcess objects after training (in standard binary python pickle serialization format, .pickle files) (iv) python scripts for processing and plotting the data (.py files) AND [rscfl-repr.zip] containing (i) experiment definition files for figures in the paper, containing the information required to reproduce them (.json format) (ii) python script for reproducing experiments (rscfl_exp.py)Additional information: As an example, for reproducing Figure 3 from the paper, one needs to unpack the two archives (rscfl-repr and rscfl-exp) and run $ ./rscfl_exp.py -c fig3.config.json -s /path/to/rscfl_exp --fg_load_vm localhostEPSRC EP/K503009/