Caching function is a promising way to mitigate coldstart overhead in serverless computing. However, as caching also increases the resource cost significantly, how to make caching decisions is still challenging. We find that the prior "local cache control" designs are insufficient to achieve high cache efficiency due to the workload skewness across servers.

While several test case selection algorithms (heuristic and optimal) and formulations (linear and non-linear) have been proposed, no multi-criteria framework enables Pareto search — the state-of-the-art approach of doing multi-criteria optimization. Therefore, we introduce the highly parallelizable, openly available Many-Criteria Test-Optimization Algorithm (MC-TOA) framework that combines heuristic Pareto search and optimality gap knowledge per criterion. MC-TOA is largely agnostic to the criteria formulations and can incorporate many criteria where existing approaches offer limited scope (single or few objectives/constraints), lack flexibility in the expression and assurance of constraints, or run into problem complexity issues. For two large-scale systems with up to seven criteria and thousands of system test cases, MC-TOA not only produces, over the board, superior Pareto fronts in terms of HVI score compared to the state-of-the-art many-objective heuristic baseline, it also does that within minutes of runtime for worst-case executions, i.e., assuming that a regression affects the entire test-suite. MC-TOA depends on convex solvers. We find that the evaluated open-source solvers are slower but suffice for smaller systems, while being less robust for larger systems. Linear formulations execute faster and obtain near-optimal results, which led to faster and better overall convergence of MC-TOA compared to integer formulations. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

This paper describes significant improvement in On-line handwritten Japanese text recognition that is free from line direction and character orientation constraints. The original system [1, 2] separates freely written text into text line elements, estimates and normalizes character orientation and line direction. Then, it hypothetically segments each text line element into primitive segments, constructs a segmentation-recognition candidate lattice and evaluates the likelihood of candidate segmentation-recognition paths by combining the scores of character recognition, geometric features, as well as linguistic context. In this scheme, we have updated the over-segmentation for each text line element and applied a robust context integration model to recognize each text line element. Experimental results on text from the HANDS-Kondate_t_bf-2001-11 database demonstrate large improvement in the character recognition rate compared with the previous system [1, 2].