Academic literature on the topic 'Multi-Level Queue Scheduling'

With the development of wireless communication technology, SMS , as a kind of flexible communication tools, is widely used in the various units. Aimed at large quantities of SMS processing, this paper proposes a new scheduling algorithm based on multi-level feedback queue. Multi-level feedback queue scheduling algorithm can not only make the high priority jobs response, but also make the short operations (process) done quickly.

Efficient task execution is critical to optimize the usage of computing resources in process scheduling. Various task scheduling algorithms ensure optimized and efficient use of computing resources. This article introduces an innovative dual-layer scheduling algorithm, Multi-Queue Adaptive Priority Scheduling (MQAPS), for task execution. MQAPS features a dual-layer hierarchy with a ready queue (RQ) and a secondary queue (SQ). New tasks enter the RQ, where they are prioritized, while the SQ contains tasks that have already used computing resources at least once, with priorities below a predefined threshold. The algorithm dynamically calculates the time slice based on process priorities to ensure efficient CPU utilization. In the RQ, the task’s priority level defines its prioritization, which ensures that important jobs are completed on time compared to other conventional methods where priority is fixed or no priority parameter is defined, resulting in starvation in low-priority jobs. The simulation results show that MQAPS better utilizes CPU resources and time than traditional round-robin (RR) and multi-level scheduling. The MQAPS showcases a promising scheduling technique ensuring a balanced framework for dynamic adjustment of time quantum and priority. The MQAPS algorithm demonstrated optimization, fairness, and efficiency in job scheduling.

Volunteer computing uses computers volunteered by the general public to do distributed scientific computing. Volunteer computing is being used in high-energy physics, molecular biology, medicine, astrophysics, climate study, and other areas. These projects have attained unprecedented computing power. However, with the development of information technology, the traditional defense system cannot deal with the unknown security problems of volunteer computing . At the same time, Cyber Mimic Defense (CMD) can defend the unknown attack behavior through its three characteristics: dynamic, heterogeneous, and redundant. As an important part of the CMD, the dynamic scheduling algorithm realizes the dynamic change of the service centralized executor, which can enusre the security and reliability of CMD of volunteer computing . Aiming at the problems of passive scheduling and large scheduling granularity existing in the existing scheduling algorithms, this article first proposes a scheduling algorithm based on time threshold and task threshold and realizes the dynamic randomness of mimic defense from two different dimensions; finally, combining time threshold and random threshold, a dynamic scheduling algorithm based on multi-level queue is proposed. The experiment shows that the dynamic scheduling algorithm based on multi-level queue can take both security and reliability into account, has better dynamic heterogeneous redundancy characteristics, and can effectively prevent the transformation rule of heterogeneous executors from being mastered by attackers.

With the Internet of Things (IoT) development, there is an increasing demand for multi-service scheduling for Mobile Edge Computing (MEC). We propose using polling for scheduling in edge computing to accommodate multi-service scheduling methods better. Given the complexity of asymmetric polling systems, we have used an information-theoretic approach to analyse the model. Firstly, we propose an asymmetric two-level scheduling approach with priority based on a polling scheduling approach. Secondly, the mathematical model of the system in the continuous time state is established by using the embedded Markov chain theory and the probability-generating function. By solving for the probability-generating function’s first-order partial and second-order partial derivatives, we calculate the exact expressions of the average queue length, the average polling period, and the average delay with an approximate analysis of periodic query way. Finally, we design a simulation experiment to verify that our derived parameters are correct. Our proposed model can better differentiate priorities in MEC scheduling and meet the needs of IoT multi-service scheduling.