Comprehensive Analysis of Resource Allocation and Service Placement in Fog and Cloud Computing

The voluminous data produced and consumed by digitalization, need resources that offer compute, storage, and communication facility. To withstand such demands, Cloud and Fog computing architectures are the viable solutions, due to their utility kind and accessibility nature. The success of any computing architecture depends on how efficiently its resources are allocated to the service requests. Among the existing survey articles on Cloud and Fog, issues like scalability and time-critical requirements of the Internet of Things (IoT) are rarely focused on. The proliferation of IoT leads to energy crises too. The proposed survey is aimed to build a Resource Allocation and Service Placement (RASP) strategy that addresses these issues. The survey recommends techniques like Reinforcement Learning (RL) and Energy Efficient Computing (EEC) in Fog and Cloud to escalate the efficacy of RASP. While RL meets the time-critical requirements of IoT with high scalability, EEC empowers RASP by saving cost and energy. As most of the early works are carried out using reactive policy, it paves the way to build RASP solutions using alternate policies. The findings of the survey help the researchers, to focus their attention on the research gaps and devise a robust RASP strategy in Fog and Cloud environment.