Planning for code buffer management in distributed virtual execution environments

Virtual execution environments have become increasingly useful in system implementation, with dynamic translation techniques being an important component for performance-critical systems. Many devices have exceptionally tight performance and memory constraints (e.g., smart cards and sensors in distributed systems), which require effective resource management. One approach to manage code memory is to download code partitions on-demand from a server and to cache the partitions in the resource-constrained device (client). However, due to the high cost of downloading code and re-translation, it is critical to intelligently manage the code buffer to minimize the overhead of code buffer misses. Yet, intelligent buffer management on the tightly constrained client can be too expensive. In this paper, we propose to move code buffer management to the server, where sophisticated schemes can be employed. We describe two schemes that use profiling information to direct the client in caching code partitions. One scheme is designed for workloads with stable run-time behavior, while the other scheme adapts its decisions for workloads with unstable behaviors. We evaluate and compare our schemes and show they perform well, compared to other approaches, with the adaptive scheme having the best performance overall.