PEFT-Based Hybrid PSO for Scheduling Complex Applications in IoT

Abstract

Internet of Things (IoT) is one of the buzzwords of the recent era and the most attractive field for researchers. It is defined as a system of connected physical objects which are approachable through the Internet and are capable of exchanging data using immerse technologies such as sensors, actuators. With the continuous evolution in IoT, number of issues arises such as confined storage space as well as limited processing capabilities. These issues can be resolved by merging IoT with cloud computing, as cloud has the immeasurable storage space as well as processing ability. This combination has proved as a boon for Internet and this combination can also be used to solve workflow scheduling problem as well. Large complex applications are often represented as workflows. Workflow scheduling is one of the eminent obstacles in both IoT and cloud computing. Several approaches have been proposed for workflow scheduling such as heuristic and meta-heuristic approaches. Commonly meta-heuristics approaches include Genetic Algorithm (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO) and heuristic approaches include Critical Path on Processor (CPOP), Heterogeneous Earliest Finish Time (HEFT), and Predict Earliest Finish Time (PEFT). But, mostly these approaches fail due to increasing of tasks, unable to execute tasks within specified budget, time, cost, and many more reasons. To overcome these above mention issues, this paper presents a hybrid PSO algorithm that uses a combine approach of both heuristic and meta-heuristic techniques namely PEFT and PSO, respectively.