Fog computing is a novel, decentralized and heterogeneous computing environment that extends the traditional cloud computing systems by facilitating task processing near end-users on computing resources called fog nodes. These diverse and resource-constrained fog devices process a large volume of tasks generated by various fog applications. These tasks are generated by various applications, some of which may be latency-sensitive, while others may tolerate some degree of delay in their normal functions. Task scheduling determines when a task should be allocated to a computing resource and how long that task can occupy the assigned resource.The majority of task scheduling algorithms focus on prioritizing the latency-sensitive tasks only, which results in the long waiting time for the other type of tasks. Hence, these priority-based schedulers cause task starvation for less important tasks while achieving delay-optimal results for latency-sensitive tasks. As a result, in this paper, we propose MQP, a multi-queue priority-based preemptive task scheduling approach that achieves a balanced task allocation for those applications that can tolerate a certain amount of processing delay and the latency-sensitive fog applications. At run-time, the MQP algorithm categorizes tasks as short and long based on their burst time. MQP algorithm maintains a separate task queue for each task category and dynamically updates the time slot value for preemption. The proposed technique's major purpose is to reduce response time for those data-intensive applications in the fog computing environment, which include both latency-sensitive tasks and tasks which are less latency-sensitive, thereby addressing the starvation problem for less latency-sensitive tasks. A smart traffic management case study is created to model a scenario with both latency-sensitive short and less latency-sensitive long tasks. We implement the MQP algorithm using iFogSim and confirm that it reduces the service latencies for long tasks.Simulation results show that the MQP algorithm allocates tasks to a fog device more efficiently and reduces the service latencies for long tasks. The average value of percentage reduction in the latency across all experimental configurations achieved is 22.68% and 38.45% in comparison to First Come-First Serve and shortest job first algorithms.

Graph colouring problem (GCP) is an NP‐complete optimization problem. It is famous for its applications in scheduling, register allocation, and map colouring. In recent years, biological inspired and especially Swarm intelligence (SI) techniques have gained popularity for solving complex optimization problems. In this article, we have proposed blind naked mole rat‐based colouring (BNMR‐Col) for graphs. BNMR‐Col uses both exploitation and exploration to find the best solution in search space. Exploitation uses both local moves and global moves to find a better solution in the surroundings of an existing solution. On the other hand, exploration generates new solution by combining various solutions from the search space. BNMR‐Col shows better convergence rate and approaches the lowest colour value in 83% of the cases when tested on standard benchmark graph instances.