Avaliação do impacto computacional e energético do streaming bidirecional via gRPC no offloading multi-linguagem em ambiente de mobile cloud computing

Abstract

The increasing complexity of mobile applications imposes significant challenges on mobile devices, which are limited in computational and energy resources. In this context, computational offloading has been employed to transfer intensive tasks to remote servers, thereby reducing energy consumption and improving the performance of mobile devices. In parallel, recent advances in communication have enabled the emergence of streaming-based models capable of supporting continuous processing almost in real time. However, most solutions still rely on the traditional request–response model, without exploring the potential benefits of bidirectional streaming and multi-language strategies. Despite the potential of gRPC, its practical application in mobile and multi-language environments remains underexplored, particularly regarding its impact on performance and energy efficiency. This work proposed, implemented, and evaluated a multi-language computational offloading solution based on gRPC with bidirectional streaming, integrating an Android client in Java with servers implemented in Go and Java. The methodology involved applying video processing filters and comparing local and remote execution through metrics such as execution time, CPU usage, and energy consumption. The results demonstrated that the effectiveness of offloading directly depends on task complexity: for the simple filter (Grayscale), local execution proved more efficient, whereas for the more complex filter (Pencil), offloading provided significant gains. In the best evaluated scenario, using the Go server and the 5 GHz Wi-Fi network, the Pencil filter at 1080p resolution achieved a reduction of approximately 69% in execution time, accompanied by lower CPU usage and reduced battery consumption. It was concluded that offloading via gRPC with bidirectional streaming is both feasible and effective for computationally intensive mobile applications, provided it is applied to complex tasks under favorable connectivity conditions. The decision between local and remote execution should be adaptive, considering task complexity, content resolution, and network conditions, in order to maximize performance and energy efficiency in Mobile Cloud Computing environments.