Abstract

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.