A multi‐queue priority‐based task scheduling algorithm in fog computing environment

Abstract

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.

A Review Study on Energy Consumption in Cloud Computing

“…Another study [34] leverages reinforcement learning to schedule workflow tasks in the cloud, considering deadline and budget constraints. The study [61] addresses fairness concerns in multi-tenant cloud environments by proposing a priority queue-based scheduling approach that guarantees a minimum QoS for all tenants. The paper [67] explores dynamic voltage scaling for scheduling tasks with approximation tolerance in cloud datacenters, achieving energy savings without compromising accuracy.…”

“…In study [61], researchers introduced a time and energy-aware two-phase scheduling algorithm, named Best Heuristic Scheduling (BHS), specifically designed for scheduling Directed Acyclic Graphs (DAG) on processors in cloud data centers. This algorithm operates in two distinct phases: firstly, it sorts, and then it identifies the best performing action.…”

“…The study [58] proposes an ant colony optimization-based approach for heterogeneous cloud environments, prioritizing energy savings. Another study [61] introduces a priority queue-based algorithm that guarantees a minimum QoS for tenants in multi-tenant cloud environments. The study [60] integrates deadline constraints into the allocation process, prioritizing timely execution of critical tasks.…”

Optimizing Task Execution: The Impact of Dynamic Time Quantum and Priorities on Round Robin Scheduling

“…A new task scheduling approach, multi-queue priority (MQP), is proposed to balance task allocation for latency-sensitive and delay-tolerant applications in fog computing environments. Tasks are categorized as short and long, separate queues are maintained, and the preemption time slot is dynamically updated to reduce response time and address the starvation problem [23]. A new Optimized Round Robin (ORR) CPU Scheduling Algorithm is introduced to minimize the average waiting time, average turnaround time, and number of context switch and maximize the system throughput [24].…”

Advancements in heuristic task scheduling for IoT applications in fog-cloud computing: challenges and prospects

“…Fog computing stands as a transformative breakthrough, reshaping the landscape of traditional cloud computing by extending its reach to the network’s edge ( Fahad et al, 2022 ; Madhura, Elizabeth & Uthariaraj, 2021 ). This innovation enables real-time processing, data analytics, and application deployment in close proximity to data sources, diverging significantly from centralized cloud architectures.…”