Energy-efficient computing model is a popular choice for high performance as well as throughput oriented computing ecosystems. Mobile (computing) devices are becoming increasingly ubiquitous to our computing domain, but with limited resources (true both for computation as well as for energy). Hence, workload offloading from resource-constrained mobile devices to the Edge and maybe (later) to the cloud become necessary as well as useful. Thanks to the persistent technical breakthroughs in global wireless standards (or in mobile networks) together with the almost limitless amount of resources in public cloud platforms, workload offloading is possible and cheaper. In such scenarios, Mobile Edge Computing (MEC) resources could be provisioned in proximity to the users for supporting latency-sensitive applications. Here, two relevant problems could be: i) How to distribute workload to the resource pools of MEC as well as public (multi-)clouds? ii) How to manage such resource pools effectively? To answer these problems in this paper, we examine the performance of our proposed approach using the Reliable Server Pooling (RSerPool) framework in more detail. We also have outlined the resource pool management policies to effectively use RSerPool for workload offloading from mobile devices into the cloud/MEC ecosystem.

Colonoscopy is a gold standard procedure but is highly operator-dependent. Efforts have been made to automate the detection and segmentation of polyps, a precancerous precursor, to effectively minimize missed rate. Widely used computer-aided polyp segmentation systems actuated by encoder-decoder have achieved high performance in terms of accuracy. However, polyp segmentation datasets collected from varied centers can follow different imaging protocols leading to difference in data distribution. As a result, most methods suffer from performance drop when trained and tested on different distributions and therefore, require re-training for each specific dataset. We address this generalizability issue by proposing a global multi-scale residual fusion network (GMSRF-Net). Our proposed network maintains high-resolution representations by performing multi-scale fusion operations across all resolution scales through dense connections while preserving low-level information. To further leverage scale information, we design cross multi-scale attention (CMSA) module that uses multi-scale features to identify, keep, and propagate informative features. Additionally, we introduce multi-scale feature selection (MSFS) modules to perform channel-wise attention that gates irrelevant features gathered through global multi-scale fusion within the GMSRF-Net. The repeated fusion operations gated by CMSA and MSFS demonstrate improved generalizability of our network. Experiments conducted on two different polyp segmentation datasets show that our proposed GMSRF-Net outperforms the previous top-performing state-of-the-art method by 8.34% and 10.31% on unseen CVC-ClinicDB and on unseen Kvasir-SEG, in terms of dice coefficient. Additionally, when tested on unseen CVC-ColonDB, we surpass the state-of-the-art method by 9.38% and 4.04% in terms of dice coefficient, when source dataset is Kvasir-SEG and CVC-ClinicDB, respectively.