Geo-distributed Situation Awareness applications are large in scale and are characterized by 24/7 data generation from mobile and stationary sensors (such as cameras and GPS devices); latency-sensitivity for converting sensed data to actionable knowledge; and elastic and bursty needs for computational resources. Fog computing [7] envisions providing computational resources close to the edge of the network, consequently reducing the latency for the sense-process-actuate cycle that exists in these applications. We propose Foglets, a programming infrastructure for the geo-distributed computational continuum represented by fog nodes and the cloud. Foglets provides APIs for a spatio-temporal data abstraction for storing and retrieving application generated data on the local nodes, and primitives for communication among the resources in the computational continuum. Foglets manages the application components on the Fog nodes. Algorithms are presented for launching application components and handling the migration of these components between Fog nodes, based on the mobility pattern of the sensors and the dynamic computational needs of the application. Evaluation results are presented for a Fog network consisting of 16 nodes using a simulated vehicular network as the workload. We show that the discovery and deployment protocol can be executed in 0.93 secs, and joining an already deployed application can be as quick as 65 ms. Also, QoS-sensitive proactive migration can be accomplished in 6 ms.

The diversity of Fog Computing deployment models and the lack of publicly available Fog infrastructure makes the design of an efficient application or resource management policy a challenging task. Such research often requires a test framework that facilitates the experimental evaluation of an application or protocol design in a repeatable and controllable manner. In this paper, we present EmuFog-an extensible emulation framework tailored for Fog Computing scenarios-that enables the from-scratch design of Fog Computing infrastructures and the emulation of real applications and workloads. EmuFog enables researchers to design the network topology according to the use-case, embed Fog Computing nodes in the topology and run Docker-based applications on those nodes connected by an emulated network. Each of the sub-modules of EmuFog are easily extensible, although EmuFog provides a default implementation for each of them. The scalability and efficacy of EmuFog are evaluated both on synthetic and real-world network topologies.

Stateful applications and virtualized network functions (VNFs) can benefit from state externalization to increase their reliability, scalability, and inter-operability. To keep and share the externalized state, distributed data stores (DDSs) are a powerful tool allowing for the management of classical trade-offs in consistency, availability and partitioning tolerance. With the advent of Fog and Edge Computing, stateful applications and VNFs are pushed from the data centers toward the network edge. This poses new challenges on DDSs that are tailored to a deployment in Cloud data centers. In this paper, we propose two novel design goals for DDSs that are tailored to Fog Computing: (1) Fog-aware replica placement, and (2) context-sensitive differential consistency. To realize those design goals on top of existing DDSs, we propose the FogStore system. FogStore manages the needed adaptations in replica placement and consistency management transparently, so that existing DDSs can be plugged into the system. To show the benefits of FogStore, we perform a set of evaluations using the Yahoo Cloud Serving Benchmark.

New generations of cloud applications are increasingly complex and pose lower latency requirements. e la er is forcing the industry to reduce network latency by adding computation nodes near the edge of the network, also known as Fog Computing. To utilize the Fog nodes e ciently, the dynamic placement and migration of application components must be supported. To this end, a Fogaware application programming and deployment framework, called Foglets, has been proposed.is demonstration shows how the Foglets framework can be easily used to deploy applications in the Fog Computing infrastructure: A video streaming application allows a moving user to continue watching a video on the closest available screen.

Over the last 20 years, mobile computing has evolved to encompass a wide array of increasingly data-rich applications. Many of these applications were enabled by the Cloud computing revolution, which commoditized server hardware to support vast numbers of mobile users from a few large, centralized data centers. Today, mobile's next stage of evolution is spurred by interest in emerging technologies such as Augmented and Virtual Reality (AR/VR), the Internet of Things (IoT), and Autonomous Vehicles. New applications relying on these technologies often require very low latency response times, increased bandwidth consumption, and the need to preserve privacy. Meeting all of these requirements from the Cloud alone is challenging for several reasons. First, the amount of data generated by devices can quickly saturate the bandwidth of backhaul links to the Cloud. Second, achieving low-latency responses for making decisions on sensed data becomes increasingly difficult the further mobile devices are from centralized Cloud data centers. And finally, regulatory or privacy restrictions on the data generated by devices may require that such data be kept locally. For these reasons, enabling next-generation technologies requires us to reconsider the current trend of serving applications from the Cloud alone.

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