With the advent of Internet of Things (IoT) applications, smart IoT devices are ubiquitous. Executing these devices on cloud data centers can lead to network congestion and transmission delay that causes failure instances in cloud architecture. Therefore, fog computing is derived to satisfy the low latency, location awareness, and mobility requirements of massive IoT applications near end users. Concerned with the high requirements of compute-intensive business applications, fog computing structure becomes complex due to limited computing capacity, and therefore these applications are offloaded to the cloud. Therefore, to overcome the offloading problem of workflow tasks in this work, a novel method named: Improved Chemical Reaction Optimization for Workflow Scheduling in a Hybrid Environment (ICRO-WSHE) has been proposed. The proposed algorithm aims at minimizing the execution cost of workflow applications under the defined deadline constraints. The algorithm is based on CRO, which has fewer parameters and the fastest convergence speed. However, the existing CRO has drawbacks in terms of getting stuck in the local optimum and having difficulty obtaining real optimal solutions. Therefore, the proposed mechanism modifies the existing CRO algorithm and includes the suitable features of PSO (Particle Swarm Optimization) and FQR (Fitness-based Quasi Reflection) methods to prevent the shortcomings of the CRO method. Further, a double-point synthesis operation is incorporated to increase the exploration rate and improve the working of the proposed algorithm. The result of the simulation experiment based on different types of workflows shows that the proposed method outperforms existing algorithms and proves to be a practical approach.

The burgeoning volume of data from the IoT applications and intelligent devices processed on the cloud data centers can lead to network congestion and transmission delay. Compared to cloud computing, fog computing focuses on ubiquitous connected heterogeneous devices and addresses the transmission latency by placing the fog nodes at the network edge. Concerning the limited resources of fog nodes enable the computationally intensive tasks to offload on the cloud resources. Scheduling of deadline-constrained workflows with minimum execution cost is challenging due to complex and uncertain computation offloading problems. Therefore, an intelligent fuzzy scheduler is designed to offload tasks characterized with uncertain parameters to the appropriate resources. A new salp swarm algorithm has been exploited to learn and optimize fuzzy task-resource allocation rules. In addition to this, to overcome the shortcomings of the salp swarm algorithm, it is employed with one of the best opposition methods named: Fitness-based quasi-reflection method. The inclusion of the opposition method enhances the proposed ISSS-FQR (Intelligent salp swarm scheduler with the fitness-based quasi-reflection method) approach and improves the learning process. Simulation studies on the benchmark workflows are carried out to demonstrate the efficacy of ISSS-FQR. ISSS-FQR has been compared with the classical algorithms, including chemical reaction optimization and ant colony optimization algorithms for workflow scheduling problems (CR-AC), Cost-Makespan aware scheduling (Deadline-based CMaS), and Directional and non-local convergent particle swarm optimization (DNCPSO). From the analyzed result, ISSS-FQR outperforms the rest of the classical algorithms, which proves the effectiveness of ISSS-FQR. Note to Practitioners—This paper provides a novel method called ISSS-FQR for minimizing the cost of execution of IoT applications while satisfying the deadline constraint. An intelligent fuzzy scheduler is designed to offload tasks characterized with uncertain parameters to the appropriate resources. The ISSS-FQR combines the Salp Swarm Algorithm and the OBL method named FQR to learn the task-resource allocation rules. It is compared with three state-of-the-art algorithms called CR-AC, Deadline-based CMaS, and DNCPSO. From the analyzed result, it has been observed that ISSS-FQR outperforms the previous algorithms.

The emergence of the cloud platform with substantial resources to offer on-demand instigated the researchers to migrate the scientific workflows to the cloud environment. The scheduling of workflows with diverse QoS parameters is not a trivial task, but an NP-Complete problem. Several heuristics for QoS constrained workflows have been investigated. However, most of them focus only on time and cost and do not guarantee high resource utilization. The scheduling of the workflow tasks over the minimum cloud resources under the defined time limit is a grave concern. In this article, an algorithm named MFGA (Modified Fuzzy Adaptive Genetic Algorithm) has been formulated to minimize the makespan and improve resource utilization under both deadline and budget constraints. A fuzzy logic controller has also been devised to control the crossover and mutation rates that prevent MFGA from getting stuck in a local optimum. MFGA has a novel crossover technique that adds the fittest solutions in the population. Additionally, a new mutation technique has also been introduced, which minimizes the makespan and increases the reusability of the resources. The simulation experiments with the real workflows show that the proposed MFGA outperforms other state-of-the-art algorithms.