Multi-Level Resource Sharing Framework Using Collaborative Fog Environment for Smart Cities

Abstract

Fog computing has proved its importance over legacy cloud architectures for computation, storage, and communication where edge devices are used to facilitate the delay-sensitive applications. The inception of fog nodes has brought computing intelligence close to the end-devices. Many fog computing frameworks have been proposed where edge devices are used for computation. In this paper, we proposed a simulation framework for fog devices that can use end devices to handle the peak computation load to provide better Quality of Services (QoS). The regional fog nodes are deployed at network edge locations which are used as an intelligent agent to handle the computation requests by either scheduling them on local servers, cloud data centers, or at the under-utilized end-user devices. The proposed device-to-device resource sharing model relies on Ant Colony Optimization (ACO) and Earliest Deadline First(EDF) Algorithm to provide a better quality of service using device available at multi-layer design. The concept of using IoT devices as fog nodes has improved the performance of legacy fog based systems. The proposed work is benchmarked in terms of system cost, efficiency, energy, and quality of service. Further, the proposed framework is with xFogSim in terms of task efficiency.

A Fog Computing Framework in IoT Healthcare Environment: Towards A New Method Based on Tasks Significance

“…In comparison, another optimal resource-sharing approach could maximize the corresponding utility [22]. A hybrid system that combines Mobile Edge Computing (MEC) and Software-Defined Systems (SDSys) for constructing ubiquitous MEC where many local controllers are connected by a global controller [23]. Cloud computing is used to mitigate resource management via sharing device resources among users [24].…”

Fog computing: A taxonomy, systematic review, current trends and research challenges

“…Qayyum et al [154] proposed a framework based on the QoS that managed the peak computation load and resource sharing of fog devices. The authors have work on the Earliest Deadline First and Ant Colony Optimization (ACO) algorithms to improve the performance.…”

Applications of Integrated IoT-Fog-Cloud Systems to Smart Cities: A Survey

“…In iIFCs, cloud computing offers scalable and on-demand computing and data storage infrastructures for smart city applications. Furthermore, fog computing enables the establishment of large-scale and scalable IoT networks to support these applications [68,[135][136][137]. A single-level (linear) or multi-level (hierarchical) fog architecture can be employed to provide scalable support for smart city applications.…”

“…Collaboration between fog nodes in close proximity to each other will enable better utilization of their resources and an enhanced ability to achieve real-time performance. With fog node collaboration, better load balancing across these nodes is achieved and situations where some nodes become overloaded while there are other nodes with low loads can be eliminated [137,138]. However, adding features to enable fog nodes collaboration is also a complex task, as several issues must be addressed.…”