Abstract
We present Jay, a software framework for offloading applications in hybrid edge clouds. Jay provides an API, services, and tools that enable mobile application developers to implement, instrument, and evaluate offloading applications using configurable cloud topologies, offloading strategies, and job types. We start by presenting Jay's job model and the concrete architecture of the framework. We then present the programming API with several examples of customization. Then, we turn to the description of the internal implementation of Jay instances and their components. Finally, we describe the Jay Workbench, a tool that allows the setup, execution, and reproduction of experiments with networks of hosts with different resource capabilities organized with specific topologies. The complete source code for the framework and workbench is provided in a GitHub repository.

Abstract

Abstract
We present a model for measuring the impact of offloading soft real-time jobs over multi-tier cloud infrastructures. The jobs originate in mobile devices and offloading strategies may choose to execute them locally, in neighbouring devices, in cloudlets or in infrastructure cloud servers. Within this specification, we put forward several such offloading strategies characterised by their differential use of the cloud tiers with the goal of optimizing execution time and/or energy consumption. We implement an instance of the model using Jay, a software framework for adaptive computation offloading in hybrid edge clouds. The framework is modular and allows the model and the offloading strategies to be seamlessly implemented while providing the tools to make informed runtime offloading decisions based on system feedback, namely through a built-in system profiler that gathers runtime information such as workload, energy consumption and available bandwidth for every participating device or server. The results show that offloading strategies sensitive to runtime conditions can effectively and dynamically adjust their offloading decisions to produce significant gains in terms of their target optimization functions, namely, execution time, energy consumption and fulfilment of job deadlines.

Abstract
We present RAMBLE(1), a framework for georeferenced content-sharing in environments that have limited infrastructural communications, as is the case for rescue operations in the aftermath of natural disasters. RAMBLE makes use of mobile edge-clouds, networks formed by mobile devices in close proximity, and lightweight cloudlets that serve a small geographical area. Using an Android app, users ramble whilst generating geo-referenced content (e.g., text messages, sensor readings, photos, or videos), and disseminate that content opportunistically to nearby devices, cloudlets, or even cloud servers, as allowed by intermittent wireless connections. Each RAMBLE-enabled device can both produce information; consume information for which it expresses interest to neighboors, and; serve as an opportunistic cache for other devices. We describe the architecture of the framework and a case-study application scenario we designed to evaluate its behavior and performance. The results obtained reinforce our view that kits of RAMBLE-enabled mobile devices and modest cloudlets can constitute lightweight and flexible untethered intelligence gathering platforms for first responders in the aftermath of natural disasters, paving the way for the deployment of humanitary assistance and technical staff at large.

Abstract
Edge computing is a hot research topic given the ever-increasing requirements of mobile applications in terms of computation and communication and the emerging Internet-of-Things with billions of devices. While ubiquitous and with considerable computational resources, devices at the edge may not be able to handle processing tasks on their own and thus resort to offloading to cloudlets, when available, or traditional cloud infrastructures. In this paper, we present JAY, a modular and extensible platform for mobile devices, cloudlets, and clouds that can manage computational tasks spawned by devices and make informed decisions about offloading to neighboring devices, cloudlets, or traditional clouds. JAY is parametric on the scheduling strategy and metrics used to make offloading decisions, providing a useful tool to study the impact of distinct offloading strategies. We illustrate the use of JAY with an evaluation of several offloading strategies in distinct cloud configurations using a real-world machine learning application, firing tasks can be dynamically executed on or offloaded to Android devices, cloudlet servers, or Google Cloud servers. The results obtained show that edge-clouds form competent computing platforms on their own and that they can effectively be meshed with cloudlets and traditional clouds when more demanding processing tasks are considered. In particular, edge computing is competitive with infrastructure clouds in scenarios where data is generated at the edge, high bandwidth is required, and a pool of computationally competent devices or an edge-server is available. The results also highlight JAY's ability of exposing the performance compromises in applications when they are deployed over distinct hybrid cloud configurations using distinct offloading strategies.