Running applications close to users—in nearby datacenters, at edge points of presence, or in on-premises clusters—is attractive, as it reduces end-to-end latency. Moving strong consistent applications closer to users is difficult, as they incur high latencies either when accessing, or coordinating, their storage system. This restricts such applications to running co-located with their data, in a datacenter. Radical allows these applications to leverage the latency benefits that come from running near users. Radical uses its new LVI protocol to perform all necessary coordination in a single request. This request guarantees linearizability with a combination of locks, a validation step, and write intents. Radical hides the latency of the LVI request by overlapping it with speculative execution of the application. Our evaluation shows that Radical achieves 84–89% of the latency improvement obtainable by moving out of the datacenter, while providing Linearizability.

An efficient peer grading mechanism is proposed for grading the multitude of assignments in online courses. This novel approach is based on game theory and mechanism design. A set of assumptions and a mathematical model is ratified to simulate the dominant strategy behavior of students in a given mechanism. A benchmark function accounting for grade accuracy and workload is established to quantitatively compare effectiveness and scalability of various mechanisms. After multiple iterations of mechanisms under increasingly realistic assumptions, three are proposed: Calibration, Improved Calibration, and Deduction. The Calibration mechanism performs as predicted by game theory when tested in an online crowd-sourced experiment, but fails when students are assumed to communicate. The Improved Calibration mechanism addresses this assumption, but at the cost of more effort spent grading. The Deduction mechanism performs relatively well in the benchmark, outperforming the Calibration, Improved Calibration, traditional automated, and traditional peer grading systems. The mathematical model and benchmark opens the way for future derivative works to be performed and compared.