Resource Aware Placement of IoT Application Modules in Fog-Cloud Computing Paradigm

With the evolving IoT scenario, computing has spread to the most minuscule everyday activities, leading to a momentous shift in the way applications are developed and deployed. With the volume of impact increasing exponentially, a coherent approach of deploying these applications is critical for an efficient utilization of the network infrastructure. A typical IoT application consists of various modules running together with active interdependencies; traditionally running on the Cloud hosted in global data centres. In this paper, we present a Module Mapping Algorithm for efficient utilization of resources in the network infrastructure by efficiently deploying Application Modules in Fog-Cloud Infrastructure for IoT based applications. With Fog computing into picture, computation is dynamically distributed across the Fog and Cloud layer, and the modules of an application can thus be deployed closer to the source on devices in the Fog layer. The result of this work can serve as a Micro-benchmark in studies/research related with IoT and Fog Computing, and can be used for Quality of Service (QoS) and Service Level Objective benchmarking for IoT applications. The approach is generic, and applies to a wide range of standardized IoT applications over varied network topologies irrespective of load

Adaptive Energy-aware Scheduling of Dynamic Event Analytics across Edge and Cloud Resources

The growing deployment of sensors as part of Internet of Things (IoT) is generating thousands of event streams. Complex Event Processing (CEP) queries offer a useful paradigm for rapid decision-making over such data sources. While often centralized in the Cloud, the deployment of capable edge devices on the field motivates the need for cooperative event analytics that span Edge and Cloud computing. Here, we identify a novel problem of query placement on edge and Cloud resources for dynamically arriving and departing analytic dataflows. We define this as an optimization problem to minimize the total makespan for all event analytics, while meeting energy and compute constraints of the resources. We propose 4 adaptive heuristics and 3 rebalancing strategies for such dynamic dataflows, and validate them using detailed simulations for 100 - 1000 edge devices and VMs. The results show that our heuristics offer O(seconds) planning time, give a valid and high quality solution in all cases, and reduce the number of query migrations. Furthermore, rebalance strategies when applied in these heuristics have significantly reduced the makespan by around 20 - 25%.Comment: 11 pages, 7 figure