Optimal Task Scheduling Research Articles

Joint optimization of task scheduling and computing resource allocation for VR video services in 5G‐advanced networks

AbstractWith the development of 5G networks and related hardware equipment, the demand for various VR video services including panoramic video live broadcast and immersive game is increasing day by day. Due to the explosive growth of data volume and the trend toward lightweight VR equipment, traditional local computing architecture faces problems of insufficient computing power and high cost. As an emerging computing paradigm, the remote computing architecture of cloud‐edge collaboration frees users from cumbersome local restrictions. However, edge computing resources are often limited, and the remote cloud has inconsistent network latency. Therefore, how to adaptively schedule and allocate computing resources is the key to determining VR service quality. Based on an in‐depth analysis of the characteristics of VR video services and network environments, this article first builds a cloud‐edge‐network coorperation computing and transmission architecture for VR video services. This architecture can adaptively offload concurrent computing tasks and allocate computing power to them in VR scenes based on network conditions provided by the 5G‐Advanced networks via network information exposure interface. This paper proposes a general optimization problem to maximize user experience. And a single‐slot delay optimization model was constructed. Combining convex optimization theory and deep learning algorithms, this paper designs a task scheduling and computing power allocation strategy based on deep reinforcement learning. The policy gradually learns optimal task scheduling and resource allocation decisions to maximize delay‐related rewards in the long run. This article further introduces the graph neural network under the attention mechanism to solve the problem of changes in the number of concurrent tasks. Finally, through Cloudsim simulation, this article verifies the superiority of task scheduling and computing power allocation strategies based on deep reinforcement learning compared to other common strategies in terms of resource utilization, client latency, scalability and decision time.

Innovation of Administrative Management System of Universities Based on Cloud Computing

Abstract In the traditional administrative management mode of higher education institutions, different departments work separately, and it is difficult to realize the sharing of information. This paper follows the hierarchical design of J2EE multilayer architecture to build an administrative management platform for colleges and universities. Through cloud computing technology college management information through Hibernate mapping to the object table to achieve data storage and sharing. Simulated annealing algorithms and genetic algorithms are introduced into the system to realize the optimal scheduling of administrative tasks and promote the overall collaborative efficiency of the task process. Finally, the workflow analysis is carried out on the practical cases of administrative management in colleges and universities, and the optimal scheduling of administrative tasks t 1 ~ t 9 in colleges and universities is completed. The results show that the platform 400Vuser under the integrated response time of 2.833 seconds less than the expected 3 seconds, the maximum number of concurrency in the maximum response time constraints can reach 4500 or so, to meet the performance requirements of university administrative management. And task t 1 ~ t 9 completes the optimal scheduling in the experiment, saving a total of 2 resource types. This system is of great practical significance in improving the efficiency of administrative offices in universities, reducing human errors and saving labor costs.

Feasibility, satisfaction, and test‐retest reliability of repeated remote memory assessments with older adults

AbstractBackgroundFrequent, remote cognitive assessments may improve sensitivity to subtle cognitive decline associated with preclinical Alzheimer’s disease (AD). To gain insight on how use of this technology can be optimized, the objective of this study is to determine if feasibility, satisfaction, or test‐retest reliability (TRR) vary depending on task frequency schedules or demographic characteristics.MethodParticipants were recruited from a longitudinal aging cohort to complete three memory tasks via smartphone or tablet using the neotiv application: Object‐In‐Room Recall (ORR), Mnemonic Discrimination for Objects and Scenes (MDT‐OS), and Complex Scene Recognition (CSR). Participants were randomly assigned to complete 24 remote sessions over one year either: (1) every other week or (2) for multiple days within one week every other month. Each session lasted approximately 10 minutes, including one memory task followed by a delayed retrieval session 90 minutes later. Chi‐square tests were used to assess differences in feasibility, acceptability for participants who completed at least 3 sessions, and satisfaction based on task schedule, gender, age, or education. ICC was used to determine TRR of visit 2 to 3 (“V2‐V3”) and an average of visits 1+2 and 3+4 (“V1+2‐V3+4”).Result115 participants (mean age = 68.4, 66% women) enrolled; 49 completed all remote sessions to date (Table 1). Willingness to enroll differed by education (X2 = 4.2, p = .04), such that participants with at least a bachelor’s degree were more likely to enroll. Withdrawal from the study occurred more often for those scheduled to the every other week condition (X2 = 6.2, p = .01) than the every other month condition. Measures of acceptability and satisfaction did not differ by task schedule or demographics. TRR from V2‐V3 and V1+2‐V3+4 were adequate, (CSR: ICC = 0.56 and 0.64, ORR: ICC = 0.58 and 0.66, MDT‐OS: ICC = 0.38 and 0.52).ConclusionParticipants with a bachelor’s degree or higher were more likely to enroll. Reported satisfaction was similar for both task frequency schedules; however, drop‐outs occurred more frequently in those assigned to complete tasks every other week, which could suggest that it is a less optimal task schedule. TRR was moderate for all three tasks. Altogether, results suggest that repeated mobile cognitive testing can be utilized to track cognitive performance remotely.

Asynchronous Deep Reinforcement Learning for Collaborative Task Computing and On-Demand Resource Allocation in Vehicular Edge Computing

Vehicular Edge Computing (VEC) is enjoying a surge in research interest due to the remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the ever-growing service applications in VEC, how to effectively aggregate and flexibly schedule ubiquitous network resources for implementing diverse tasks and meeting differentiated demands from numerous vehicular users remains haunting. Toward this end, we investigate collaborative task computing and on-demand resource allocation. The collaborative computing framework in VEC is provided to support deep collaboration and intelligent management of heterogeneous resources widely distributed in vehicles, edge servers and cloud. Based on this framework, the joint optimization problem of distributed task offloading and multi-resource management is formulated with the aim to maximize the system utility by making the optimal task and resource scheduling policy, the novelty of which lies in the exploration of available vehicle resources and the consideration of service migration. In view of the dynamics, randomness and time-variant of vehicular networks, the asynchronous deep reinforcement algorithm is leveraged to find the optimal solution. Extensive simulation experiments are implemented to demonstrate the superiority of our proposed algorithm in terms of response latency compared with full offloading and random offloading.

A Multi-Swarm PSO Approach to Large-Scale Task Scheduling in a Sustainable Supply Chain Datacenter

Supply chain management is a vital part of ensuring service quality and production efficiency in industrial applications. With the development of cloud computing and data intelligence in modern industries, datacenters have become an important basic support for intelligent applications. However, the increase in the number and complexity of tasks makes datacenters face increasingly heavy task processing demands. Therefore, there are problems of long task completion time and long response time in the task scheduling process of the data center. A multi-swarm particle swarm optimization task scheduling approach based on load balancing is proposed in this paper, aiming to reduce the maximum completion time and response time in task scheduling. The proposed approach improves the fitness evaluation function of particle swarms to facilitate load balancing. The new adaptive inertia weight and initialization method design can improve the search efficiency and convergence speed of particles. Meanwhile, the multi-swarm design can avoid the problem of particles falling into local optimum as much as possible. Finally, the proposed algorithm is verified experimentally using the task dataset released by Alibaba datacenter, and compared with other benchmark algorithms. The results show that the proposed algorithm can improve the task scheduling performance of the datacenter in supply chain management when dealing with different workloads and changes in the number of elastic machines.

A cost-emission based scheme for residential energy hub management considering comfortable lifestyle and responsible demand

ABSTRACT Due to different and frequently competing goals, managing multiple energy carriers in residential buildings in a coordinated manner is one of the key obstacles to the optimal operation of smart cities. This paper offers a novel conceptual cost-based scheme for optimal energy-gas use in a smart home in the context of residential energy hubs in response to this challenge, considering a significant trade-off between cost reduction and environmental conservation. The suggested model takes into account a variety of energy conversion resources, including energy and heat storage systems, rooftop photovoltaic modules, combined heat and power units, as well as responsible electrical and thermal loads. In addition, an effective stochastic scenario-based approach is used to address the significant uncertainty related to photovoltaic production. By incorporating a weighted summation mixed objective function under various system constraints and user preferences, the suggested framework lowers domestic energy consumption and utility costs while also providing the resident with optimal task scheduling and comfort, which can ensure a high quality of life. The suggested scheme is tested on a real-world case study with energy hubs, and as anticipated, it introduces its applicability and effectiveness in the proposed residential energy hub problem’s optimal energy management. The simulation results show that it is possible to reduce energy procurement costs by up to 47.86% and emissions costs by up to 31.29% while still providing the desired level of comfort for the households.

Hybrid metaheuristic model based performance-aware optimization for map reduce scheduling

Because of the rapid rise in the count of corporations using cloud-dependent infrastructure as the foundation for big data storing and analysis. The fundamental difficulty in scheduling big data services in cloud-dependent systems is ensuring the shortest possible makespan while simultaneously reducing the number of resources being used. We have created a new, secure map reduce scheduling method that functions as follows. Initially, the cloud architecture is designed and the tasks are generated. In the pre-processing phase, the huge set of tasks was processed by the map-reduce scheduling framework. Afterward, the optimal task scheduling task is conducted which utilizes a hybrid algorithm named Tunicate Combined Moth Flame Algorithm (TCMFA), which provides better task scheduling via providing optimal makespan, execution time, and security. This proposed TCMFA is the hybridization of both Moth Flame Optimization (MFO) and Tunicate Swarm Algorithm (TSA). The error rate of the TCMFA gets reduced to 320 approximately over other conventional methods such as RSA, ACO, GHO, BTS, OWPSO, BES, PRO, SOA, COOT, TSA & MFO which proves the accuracy of our TCMFA and makes it more efficient and secure for optimal map-reduce scheduling.

HEPGA: A new effective hybrid algorithm for scientific workflow scheduling in cloud computing environment

Cloud data center comprises various physical and virtual machines, alongside storage datacenter services provided by cloud providers. Effectively mapping tasks to optimize resource utilization and load balance is essential for efficient task scheduling. This process, referred to as scheduling constraints, can significantly enhance overall efficiency. However, to harness the benefits of this scheduling, one must address the challenges arising during task execution. The interdependencies between tasks and the diverse resources available in the datacenter pose significant hurdles to efficient resource allocation. To address these challenges, this paper introduces the Hybrid HEFT-PSO-GA algorithm (HEPGA), aiming to efficiently allocate tasks to available resources across the datacenter. The HEPGA algorithm builds upon prior research by integrating the strengths of PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) to optimize task scheduling in cloud computing environments. Through the fusion of PSO, GA, and HEFT-based Initialization, the algorithm strives to efficiently allocate tasks to processors, thereby minimizing the makespan. This approach capitalizes on parallel processing capabilities to further enhance resource utilization in the cloud environment. By varying the weights of the fitness function and considering the resources within the datacenter, we meticulously analyze the algorithm's performance concerning both makespan and Resource Utilization (RU). The results of these tests underscore the algorithm's consistent and robust resource utilization across diverse weight configurations, highlighting its adaptability to varying priorities. Moreover, the observed variations in makespan performance based on different weights emphasize the algorithm's potential for excellence when tailored to specific optimization goals.

Optimizing Task Scheduling in Cloud Computing Environments using Hybrid Swarm Optimization

Cloud computing has revolutionized the Information Technology (IT) landscape by offering on-demand access to a shared pool of computing resources over the internet. Effective task scheduling is pivotal in optimizing resource utilization and enhancing the overall performance of cloud systems. Tasks are allocated to virtual machines (VMs) based on a server's workload capacity, aiming to minimize traffic congestion and waiting times. Although Particle Swarm Optimization (PSO) is currently the most effective algorithm for task scheduling in cloud environments, this study introduces a Hybrid Swarm Optimization (HSO) algorithm that combines the strengths of PSO and Salp Swarm Optimization (SSO). The proposed hybrid algorithm addresses the challenges associated with task scheduling in cloud computing. The performance of the HSO algorithm is evaluated using the CloudSim simulator and compared against traditional scheduling algorithms. Simulation results indicate that the hybrid PSO-SSO algorithm outperforms existing methods regarding makespan time, cloud throughput, and task execution efficiency. Consequently, the hybrid approach significantly improves resource utilization and overall system performance in cloud computing environments.

METSM: Multiobjective energy-efficient task scheduling model for an edge heterogeneous multiprocessor system

Along with the growth of computing requirements, edge devices, such as satellites and unmanned aerial vehicles, are equipped with heterogeneous multiprocessor systems to cope with complicated missions, including attitude control, signal processing, and objective detection. Concerning the extremely limited power on these devices, reducing the energy and time overhead of task execution evolves into a crucial challenge. Therefore, a multiobjective energy-efficient task scheduling technique (METSM) is proposed. First, a mathematical model is established for the energy-efficient task scheduling problem on edge heterogeneous multiprocessor systems. In this model, both makespan and total energy consumption are the optimization objectives. The decision variables include the task execution sequence, processor assignment, and dynamic voltage and frequency scaling level for each processor. Second, a problem-specific algorithm, namely, iterated greedy-based multiobjective optimizer (IMO), is proposed. Specifically, destruction–reconstruction and local search are redesigned for optimizing task scheduling and resource allocation. Considering local optima avoidance, a probabilistic mutation operation is developed. In addition, multiobjective-oriented strategies of optimal solution selection and acceptance criteria are adopted to accelerate the convergence. Finally, our proposed IMO is compared with several of the latest algorithms through multiple performance metrics. The experimental results show that IMO can obtain optimal Pareto fronts among several multiobjective methods. Savings of approximately 10% and 12% in time and power consumption, respectively, can be achieved by IMO. Moreover, in comparison with classic list-based heuristics for solving test cases, while IMO maintains a similar makespan, energy is reduced by nearly 90%.

Fairness-aware task offloading and load balancing with delay constraints for Power Internet of Things

The rapid development of the Power Internet of Things (PIoT) enables power smart sensing devices to offload their computation tasks to nearby edges. However, due to the increasing demand for computing and the unbalanced spatial distribution of devices, it is imperative to seek cooperative computing and task scheduling optimization schemes at the edge for PIoT. In this paper, we develop a two-tier cooperative edge network paradigm in PIoT. Then, we define a novel fairness indicator based on the Theil index to measure the allocation balance of the system. We also formulate a fairness and delay guaranteed (FDG) task offloading and load balancing optimization problem, which aims to minimize the allocation difference of the edge network while satisfying the delay constraints for multiple tasks in PIoT. Moreover, we develop a Lyapunov optimization and whale optimization algorithm (LWOA) to solve the problem. The simulation results demonstrate that for two types of typical tasks in PIoT, compared with the NonB scheme, the proposed FDG scheme decreases the time-averaged allocation difference of the system by 10% and 35%, the time-averaged allocation difference within subsystems by 5% and 6%, the time-averaged delay by approximately 5% and 7%, and the time-averaged queue backlog by approximately 30% and 40%. Research, both theoretical and experimental, has demonstrated that cooperation at the edge can significantly improve the performance of PIoT.

DAG Scheduling in Mobile Edge Computing

In Mobile Edge Computing, edge servers have limited storage and computing resources that can only support a small number of functions. Meanwhile, mobile applications are becoming more complex, consisting of multiple dependent tasks, modeled as a Directed Acyclic Graph (DAG). When a request arrives, typically in an online manner with a deadline specified, we need to configure the servers and assign the dependent tasks for efficient processing. This work jointly considers the problem of dependent task placement and scheduling with on-demand function configuration on edge servers, aiming to meet as many deadlines as possible. For a single request, when the configuration on each edge server is fixed, we derive FixDoc to find the optimal task placement and scheduling. When the on-demand function configuration is allowed, we propose GenDoc , a novel approximation algorithm, and analyze its additive error from the optimal theoretically. For multiple requests, we derive OnDoc , an online algorithm easy to deploy in practice. Our extensive experiments show that GenDoc outperforms state-of-the-art baselines in processing 86.14% of these unique applications, and reduces their average completion time by at least 24%. The number of deadlines that OnDoc can satisfy is at least 1.9× that of the baselines.

Robust and efficient task scheduling for robotics applications with reinforcement learning

Effective task scheduling can significantly impact on performance, productivity, and profitability in many real-world settings, such as production lines, logistics, and transportation systems. Traditional approaches to task scheduling rely on heuristics or simple rule-based methods. However, with the emergence of machine learning and artificial intelligence, there is growing interest in using these methods to optimize task scheduling. In particular, reinforcement learning is a promising task scheduling approach, because it can learn from experience and adapt to changing conditions. One step often missed or neglected is choosing optimal algorithm parameters and different ways the environment could be implemented. The study analyzes the performance possibilities of task scheduling using reinforcement learning. The deep analysis allows to select highly efficient environment models and Q-learning parameters. Moreover, automatic selection based on optimization algorithms has been proposed. Regardless of the selected optimal parameters, the resilience to environmental changes seems poor. The deducted analysis motivated the Authors to develop a novel Hybrid Q-learning approach. It allows to provide superior efficiency regardless of the environmental parameters.

Real-time rolling-horizon energy management of public laundries: A case study in HSB living lab

Energy Management Systems (EMSs) play a vital role in managing energy consumption for both utilities and consumers. By using EMSs, utilities can influence on energy usage and ensure a more reliable and efficient grid operation, while consumers can make informed decisions about their energy consumption, leading to significant cost savings and reduced environmental impact. In this paper, a real-time rolling-horizon model is developed for managing energy consumption in public laundries aiming at minimizing energy costs, peak demand, and CO2 emission under the traditional Energy-Based Tariff (EBT) and the Power-Based Tariff (PBT). The developed model can not only reduce energy costs, peak demand, and CO2 emission by optimal task scheduling for washing machines and tumble dryers but also ensure users' preferences for a comfortable lifestyle. To demonstrate the effectiveness of the proposed EMS, several simulations were performed under different scenarios using real data and by a realistic case study in HSB living lab demonstration site. The simulation results reveal that implementing the proposed EMS can significantly decrease energy costs and peak demand in public laundries by 13.59% and 39.40%, respectively, when using the PBT tariff. However, the reduction in energy costs and peak demand is negligible when using the EBT tariff. Likewise, the results indicate that using the EMS and changing tariffs have a minimal impact on CO2 emissions reduction.

Research on Sparrow Search Optimization Algorithm for multi-objective task scheduling in cloud computing environment

In cloud computing, optimizing task scheduling is crucial for improving overall system performance and resource utilization. To minimize cloud service costs and prevent resource wastage, advanced techniques must be employed to efficiently allocate cloud resources for executing tasks. This research presents a novel multi-objective task scheduling method, BSSA, which combines the Backtracking Search Optimization Algorithm (BSA) and the Sparrow Search Algorithm (SSA). BSA enhances SSA’s convergence accuracy and global optimization ability in later iterations, improving task scheduling results. The proposed BSSA is evaluated and compared against traditional SSA and other algorithms using a set of 8 benchmark test functions. Moreover, BSSA is tested for task scheduling in cloud environments and compared with various metaheuristic scheduling algorithms. Experimental results demonstrate the superiority of the proposed BSSA, validating its effectiveness and efficiency in cloud task scheduling.

Task scheduling optimization in heterogeneous cloud computing environments: A hybrid GA-GWO approach

Cloud computing, a technology providing flexible and scalable computing resources, faces a critical challenge in task scheduling, directly impacting system performance and customer satisfaction. The task scheduling problem's NP-completeness makes finding solutions difficult. To address this, researchers propose a hybrid algorithm combining the Grey Wolf Optimization Algorithm (GWO) and the Genetic Algorithm (GA). The hybrid GWO-GA algorithm targets multi-objective task scheduling in cloud computing, aiming to minimize makespan, energy consumption, and cost. Enhancements to the proposed algorithm involve leveraging the genetic algorithm's crossover and mutation operator. Furthermore, the GA-based GWO algorithm's faster convergence in large scheduling problems presents an advantage. Evaluation using the Cloudsim toolkit demonstrates the proposed algorithm's efficiency compared to existing methods. We have used both synthetic and real world data set. The results are verified using the Analysis of Variance (ANOVA) statistical tool. Experimental results showcase its effectiveness in minimizing makespan, energy consumption, and computational cost. Particularly, the proposed algorithm outperforms traditional GWO, GA, and PSO algorithms in terms of makespan, cost, and energy consumption, achieving reductions of 19%, 21%, and 15%, respectively, when compared to each approach. Additionally, it yields energy savings of 17%, 19%, and 23% compared to GWO, GA, and PSO, respectively, while reducing total scheduling costs by 13%, 17%, and 22%. These findings demonstrate the efficacy of the proposed algorithm in addressing the task scheduling problem in cloud computing environments.

SCAFE: A Service-Centered Cloud-Native Workflow Engine Architecture

With the rapid development of manufacturing and cloud computing, more and more emerged cloud services provide a promising way to perform complex requirements efficiently. Workflow offers an effective way to assemble disparate services and interacts with them to construct business logic for user requests. Meanwhile, workflow engines are responsible for the control of workflow execution. However, existing engines usually support interaction between workflows and services or computing resources by tight binding approaches, which lack flexibility and scalability. Therefore, a flexible and decoupled architecture is essential to support automatic workflow management. To fill this gap, this paper proposes a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</b> ervice- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</b> entered cloud-n <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</b> tive work <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</b> low architectur <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</b> - SCAFE. The introduction of the service layer in SCAFE decouples the upper business services and lower execution resources, facilitating the independent and joint management of three execution objects (request - service - execution instance) involved in the cloud workflow lifecycle. We present a 2-stage scheduling model for the new architecture to support customized service optimization and resource-aware task scheduling. In addition, a fault-tolerant mechanism is integrated into resolving task execution exceptions quickly. We have successfully implemented a prototype tool to verify its flexibility and crucial functions, thus advancing the field workflow engines in cloud-native environments.

Intelligent Task Scheduling Approach for IoT Integrated Healthcare Cyber Physical Systems

Cyber-physical systems (CPS) based on cloud computing provides resources over the Internet and allow a variety of applications to be deployed to provide services for various industries. We proposed IoT-based healthcare cyber-physical system that provides effective resource utilization at fog and cloud levels with minimum execution cost. In addition, we also consider data from social media networking and drug review for the analysis. Furthermore, two different feature extraction approaches were applied based on data collection. Homogeneity score-based K-means clustering is used as a feature extraction and selection method for sensor data features, while text mining and sentiment analysis approach is used for social media networking and drug review data feature extraction. We proposed efficient resource utilization and cost-effective task scheduling at the Fog level and multi-objective heuristic approach Ant colony optimization task scheduling (MOHACO-TS) at cloud level. Both task scheduling algorithms focus on executing maximum task tasks in minimum time with effective resource utilization. We consider five different datasets and existing task scheduling and classification approaches for performance evaluation of the proposed IoT-HCPS framework. From the results, it is evident that the proposed work IoT-HCPS outperformed the exisitng techniques and algorithms.

A Deep Reinforcement Learning-Based Model for Optimal Resource Allocation and Task Scheduling in Cloud Computing

The advent of cloud computing has dramatically altered how information is stored and retrieved. However, the effectiveness and speed of cloud-based applications can be significantly impacted by inefficiencies in the distribution of resources and task scheduling. Such issues have been challenging, but machine and deep learning methods have shown great potential in recent years. This paper suggests a new technique called Deep Q-Networks and Actor-Critic (DQNAC) models that enhance cloud computing efficiency by optimizing resource allocation and task scheduling. We evaluate our approach using a dataset of real-world cloud workload traces and demonstrate that it can significantly improve resource utilization and overall performance compared to traditional approaches. Furthermore, our findings indicate that deep reinforcement learning (DRL)-based methods can be potent and effective for optimizing cloud computing, leading to improved cloud-based application efficiency and flexibility.

Task Scheduling Mechanism Based on Reinforcement Learning in Cloud Computing

The explosive growth of users and applications in IoT environments has promoted the development of cloud computing. In the cloud computing environment, task scheduling plays a crucial role in optimizing resource utilization and improving overall performance. However, effective task scheduling remains a key challenge. Traditional task scheduling algorithms often rely on static heuristics or manual configuration, limiting their adaptability and efficiency. To overcome these limitations, there is increasing interest in applying reinforcement learning techniques for dynamic and intelligent task scheduling in cloud computing. How can reinforcement learning be applied to task scheduling in cloud computing? What are the benefits of using reinforcement learning-based methods compared to traditional scheduling mechanisms? How does reinforcement learning optimize resource allocation and improve overall efficiency? Addressing these questions, in this paper, we propose a Q-learning-based Multi-Task Scheduling Framework (QMTSF). This framework consists of two stages: First, tasks are dynamically allocated to suitable servers in the cloud environment based on the type of servers. Second, an improved Q-learning algorithm called UCB-based Q-Reinforcement Learning (UQRL) is used on each server to assign tasks to a Virtual Machine (VM). The agent makes intelligent decisions based on past experiences and interactions with the environment. In addition, the agent learns from rewards and punishments to formulate the optimal task allocation strategy and schedule tasks on different VMs. The goal is to minimize the total makespan and average processing time of tasks while ensuring task deadlines. We conducted simulation experiments to evaluate the performance of the proposed mechanism compared to traditional scheduling methods such as Particle Swarm Optimization (PSO), random, and Round-Robin (RR). The experimental results demonstrate that the proposed QMTSF scheduling framework outperforms other scheduling mechanisms in terms of the makespan and average task processing time.