MobiCOP: A Scalable and Reliable Mobile Code Offloading Solution

Abstract

Code offloading is a popular technique for extending the natural capabilities of mobile devices by migrating processor-intensive tasks to resource-rich surrogates. Despite multiple platforms for offloading being available in academia, these frameworks have yet to permeate the industry. One of the primary reasons for this is limited experimentation in practical settings and lack of reliability, scalability, and options for distribution. This paper introduces MobiCOP, a new code offloading framework designed from the ground up with these requirements in mind. It features a novel design fully self-contained in a library and offers compatibility with most stock Android devices available today. Compared to local task executions, MobiCOP offers performance improvements of up to 17x and increased battery efficiency of up to 25x, shows minimum performance degradation in environments with unstable networks, and features an autoscaling module that allows its server counterpart to scale to an arbitrary number of offloading requests. It is compatible with the most relevant Android technologies optimized for heavy computation (NDK and Renderscript) and has so far been well received by fellow mobile developers. We hope MobiCOP will help bring mobile code offloading closer to the industry realm.

Enhancing Offloading Systems with Smart Decisions, Adaptive Monitoring, and Mobility Support

“…In the last few years, several studies have been developed in response to the challenges offered by MCC [3][4][5][6][7][8][9][10], and with them different approaches for performing computation offloading of mobile applications were proposed. MpOS [3], MAUI [4], ThinkAir [5], MobiCOP [8], Foreseer [9], and CSOS [10] use methods as offloading units, while CloneCloud [6] and EMCO [7] migrate threads to a remote server. Since our solution extends MpOS, we also work at method granularity.…”

“…This decision may take into account the time needed to transfer and execute the task remotely and the energy cost of the offloading operation, as well as several software, hardware, and network-related metrics. Diverse solutions have been proposed to decide when to offload [3][4][5][6][7][8][9][10], but most of them depend on constant profiling of metrics, which imposes the use of local resources that should be avoided. Besides that, the decision-making processes may also use computationally costly tasks, which should preferably be executed outside the mobile device.…”

Moving to the Edge-Cloud-of-Things: Recent Advances and Future Research Directions

“…Consequently, the cooperating mobile devices could satisfy the requirements of IoT applications (like low latency) better than the traditionally centralized clouds. In this context, several mobile cloud platforms have been proposed in academia to produce the most promising edge results, such as GEMCloud [37], NativeBOINC project [38], Hyrax [39], and MobiCOP [40]. However, energy is one of the challenges faced while utilizing mobile devices as an infrastructure of computing resources, because mobile devices use their energy to offer the services.…”

A User-Centered Mobile Cloud Computing Platform for Improving Knowledge Management in Small-to-Medium Enterprises in the Chilean Construction Industry

“…Although MCC is a promising alternative for the needs of SMEs, there are some challenges that still remain to be solved, among them are: limited hardware resources and energy consumption in mobile devices, computational offloading, connectivity, security, latency, and heterogeneity [92,93]. These challenges are discussed by these authors, including how they are currently being addressed.…”