With the rapid increase in the amount of exchanged traffic over cellular networks, stemming partly from computation-intensive tasks, and the highly mobile nature of the users, mobility management exhibits considerable challenges in next-generation cellular networks. A way to alleviate these problems is by using Software-defined Radio Access Networks (SD-RAN), where a centralized controller with a complete overview of the network topology (distribution of users across base stations and their channel conditions) can make decisions on the user assignment and resource allocation. To that end, in this paper, we formulate an optimization problem with the objective of maximizing the network utility, where computation-intensive tasks are sent from the users to edge clouds, taking into account the communication constraints (uplink and downlink bandwidth) as well as the finite storage and processing capabilities of edge clouds. Moreover, we provide a user rate guarantee to satisfy an additional application for all users. The problem is NP-hard, therefore, we propose to use Deep Reinforcement Learning (DRL) to solve it. Extensive realistic simulations show that our approach is close to the optimal solution, where the latter is obtained using a solver, while outperforming a benchmark by up to 65%.

Dynamic programming languages have been embracing gradual typing, which supports optional type annotations in source code. Type-annotating a complex and long-lasting codebase is indeed a gradual and expensive process, where two issues have troubled developers. First, there is few guidance about how to implement type annotations due to the existence of non-trivial type practices; second, there is few guidance about which portion of a codebase should be type-annotated first. To address these issues, this paper investigates the patterns of non-trivial type-annotation practices and features of type-annotated code files. Our study detected six patterns of type-annotation practices, which involve recovering and expressing design concerns. Moreover, we revealed three complementary features of type-annotated files. Besides, we implemented a tool for studying optional typing practice. We suggest that: 1) design concerns should be considered to improve type annotation implementation by following at least six patterns; 2) files critical to software architecture could be type-annotated in priority. We believe these guidelines would promote a better type annotation practice for dynamic languages.