Priority based task scheduling by mapping conflict-free resources and Optimized workload utilization in cloud computing

Effective academic time management and structured study planning remain persistent and well-documented challenges for students enrolled in postgraduate and undergraduate programs. Existing digital productivity solutions impose excessive reliance on cloud infrastructure, continuous internet connectivity, and mandatory user authentication mechanisms, thereby limiting accessibility and raising substantive concerns regarding personal data privacy and institutional data security. This paper presents the design, implementation, and empirical evaluation of an Academic Smart Study Planner and Task Scheduler, a full-stack web application developed using Python Flask for the backend application logic and SQLite as the relational database engine. The proposed system delivers a comprehensive suite of academic productivity features encompassing personalized study timetable generation, priority-based task scheduling, deadline tracking with visual alert mechanisms, a real-time study analytics dashboard, and Pomodoro-based focus session management. The backend architecture implements a weighted multi-criteria scheduling algorithm that dynamically reorders tasks based on deadline urgency, user-assigned priority classifications, and estimated task durations. Experimental evaluation involving thirty postgraduate students over a six-week period demonstrated a thirty-eight percent average improvement in task completion rates, sustained Pomodoro session completion averaging 4.5 cycles per study session, and a ninety-four percent user satisfaction rate for the analytics visualization component

As part of the DRAGON SLAYER project, the adaptive and reliable distributed file system MELODY has emerged for supporting hard real-time applications in unpredictable environments. In MELODY, the time criticality of tasks and their sensitivity with respect to the latest file information are explicitly used for novel, flexible task scheduling algorithms and file replication management policies, featuring dynamic file replication and relocation of file copies as well as offering file versions of varying recency. The achieved adaptability far outweighs the additional overhead in comparison to simpler models, and enhances both reliability and real-time responsiveness for file access. Both the development of the model and the experimental analysis at the various stages were done in an incremental manner. This was necessary in order to cope with the complexity of the problems resulting from conflicting requirements and dynamic trade-offs (e.g. real-time responsiveness vs. reliability). As the next incremental model extension, and as the major contribution of this paper, a series of integration policies are developed for task and resource scheduling, after redefining the role and order of task and resource scheduling: the periodic and dynamic models (and combinations thereof) are used to invoke the task scheduler for a fixed interval of time. All of these policies compare very convincingly against the "classical" model where the task scheduler only schedules tasks after their resources have been allocated. The results are discussed.

A radar resource management module in a cognitive multifunction radar manages the resources by first prioritizing and then scheduling the tasks. Apart from scheduling the tasks, the task scheduler of a cognitive radar requires the scheduling to be adaptable to the changing environment. We formulate a gen-eral model for the distributions of task parameters, specifically, task priorities and delay tolerance, to ensure priority-based task scheduling. We develop the use of transfer learning (TL) within a deep reinforcement learning (DRL) framework to address the challenge of adaptability to a varying environment. Our approach builds on using a Monte Carlo Tree Search (MCTS) aided by a deep neural network (DNN). We show that TL allows accelerated training by transferring the policy learned by training the D NN-based MCTS on initial parameter distribution (environment) to the policy required for a new environment. Our results show that the high priority tasks are least delayed and dropped with the new formulation, whereas TL ensures the respective adaptation to the dynamic environment.

In the cloud computing environment, task scheduling with multiple objectives optimization becomes a highly challenging problem in such a dynamic and bursty environment. Previous studies have mostly emphasized assigning the incoming tasks in a specific scenario, with a weak generalization ability to various objectives automatically. Thus, they suffer the inefficient issue under large-scale and heterogeneous cloud workloads. To address this issue, we propose a deep reinforcement learning (DRL)-based intelligent cloud task scheduler, which makes the optimal scheduling decision only dependent on learning directly from its experience without any prior knowledge. We formulate task scheduling as a dynamical optimization problem with constraints and then adopt the deep deterministic policy gradients (DDPG) network to find the optimal task assignment solution while meeting the performance and cost constraints. We propose a correlation-aware state representation method to capture the inherent characteristics of demands, and a dual reward model is designed to learn the optimal task allocation strategy. Extensive experimental results on Alibaba cloud workloads show that compared with other existing solutions, our proposed DDPG-based task scheduler enjoy superiority and effectiveness in performance and cost optimization.

SAAS parallel task scheduling based on cloud service flow load algorithm

Resource provisioning and scheduling are essential challenges in handling multiple workflow requests within cloud environments, particularly given the constraints imposed by limited resource availability. Although workflow scheduling has been extensively studied, few methods effectively integrate resource provisioning with scheduling, especially under cloud resource limitations and the complexities of multiple workflows. To address this challenge, we propose an innovative two-stage genetic-based optimization approach. In the first stage, candidate cloud resources are selected for the resource pool under the given resource constraints. In the second stage, these resources are provisioned and task scheduling is optimized on the selected resources. A key advantage of our approach is that it reduces the search space in the first stage through a novel encoding scheme that enables a caching strategy, in which intermediate results are stored and reused to enhance optimization efficiency in the second stage. The proposed solution is evaluated through extensive simulation experiments, assessing both resource selection and task scheduling across a diverse range of workflows. The results demonstrate that the proposed approach outperforms existing algorithms, particularly for highly parallel workflows, highlighting its effectiveness in managing complex workflow scheduling under resource-constrained cloud environments.

Hybrid ant genetic algorithm for efficient task scheduling in cloud data centers

Access network infrastructures are being heavily tested by the advent of new multimedia services. Wireless-optical broadband access network (WOBAN) is being developed to address such demands. In a WOBAN, service requests migrate from the wireless front end to the optical backend to reach distant cloud servers, creating wireless network bottlenecks that could degrade performance. Thus, Cloud Integrated WOBAN (CIW) is a popular method for combining cloudlets with WOBAN to provide resource-intensive and delay-sensitive applications. Data packets go through a number of intermediary nodes in CIW from the source node to a wireless frontend cloudlet. However, it is a problem to figure out how to schedule these packets throughout the many cloudlets. The proposed work examines the priority-based scheduling method with the packet deadline constraint. The outcome examines a parametric evaluation of the algorithm in terms of the packet drop for the highest priority applications.

Many parallel algorithms in domains such as graph analytics and simulations rely on priority-based task scheduling. In such environments, the data structure of choice is a concurrent priority queue (PQ). Unfortunately, PQ algorithms exhibit an undesirable tradeoff. On one hand, strict PQs always dequeue the highest-priority task, and thus fail to scale because of contention at the head of the queue. On the other hand, relaxed PQs avoid contention by dequeuing tasks that are sometimes so far from the head that the resulting schedule misses the benefit of priority-based scheduling.

Task scheduling is an essential component of any distributed system because it routes tasks to appropriate resources for execution, such as grids, clouds, and peer-to-peer networks. Common scheduling algorithms include downsides, such as high temporal complexity, non-simultaneous processing of input tasks, and longer program execution times. Exploration-based scheduling algorithms prioritize tasks using a variety of methods, resulting in long execution times on heterogeneous distributed computing systems. As a result, task prioritization becomes a bottleneck in such systems. It is appropriate to prioritize tasks with the shortest execution time using faster algorithms. The genetic algorithm (GA) is one of the evolutionary approaches used to solve complex problems quickly. This paper proposes a parallel GA with a MapReduce architecture for scheduling jobs on cloud computing with various priority queues. The fundamental aim of this study is to employ a MapReduce architecture to minimize the total execution time of the task scheduling process in the cloud computing environment. The proposed method accomplishes task scheduling in two stages: first, the GA was used in conjunction with heuristic techniques to assign tasks to processors, and then the GA was used in conjunction with the MapReduce framework to assign jobs to processors. In our experiments, we consider heterogeneous resources that differ in their ability to execute various tasks, as well as running a job on different resources with varying execution durations. The results show that the proposed method outperforms other algorithms such as particle swarm optimization, whale optimization algorithm, moth-flame optimization, and intelligent water drops.

Cloud computing and big data: Technologies and applications

Objective: To develop a task scheduling algorithm that efficiently approximates solutions for the multi-objective task scheduling problem in a cloud environment, optimizing resource utilization, execution time, cost, and overall system performance. Method: A Modified Sunflower Optimization Algorithm for Task Scheduling (MSOTS) is proposed to improve efficiency in cloud environments. The traditional sunflower optimization algorithm is enhanced with Levy flight, crossover, and mutation operations to achieve a better balance between exploration and exploitation while preventing entrapment in local minima. These enhancements help improve convergence speed and solution quality. CloudSim is utilized for comprehensive performance evaluation, comparing MSOTS with existing algorithms in terms of execution time, cost, and resource utilization. The randomly generated dataset (500 tasks and 50 VMs) is used for analyze the performance of the MSOTS. Findings: The results demonstrate that MSOA significantly reduces makespan and cost while enhancing resource utilization. Specifically, the proposed method reduces makespan by 22.43% compared to PSO and 14.95% compared to SOA. Additionally, cost is reduced by 14.47% and 10.38% compared to PSO and SOA, respectively, while resource utilization increases by 3.89% and 2.51% over these methods. These findings indicate that MSOA effectively improves task scheduling performance in terms of makespan, cost efficiency, and resource utilization, making it a promising approach for cloud computing environments. Novelty: The MSOA is integrated with single-point crossover, swap mutation, and Lévy flight to update the solution, which prevents entrapment in local minima and premature convergence and enhances task scheduling efficiency, optimizes resource allocation, minimizes execution time, and reduces overall costs in cloud computing environments. Keywords: Meta-heuristic, Sunflower optimization, Levy flight, Crossover, Mutation

Cloud computing is a popular computing paradigm which enables delivery of on-demand services over the internet. A cloud environment consists of multiple customers requesting for the available resources. Cloud vendors who offer Infrastructure as a Service should enable efficient management of the available resources. Proper scheduling in cloud enables the selection of best suitable resources for task execution. In the present scenario, allocating the resources efficiently is a challenging job. Service providers need to ensure that their resources are utilized properly. A new scheduling algorithm is proposed to address the challenge of task scheduling in cloud. The user tasks are prioritized. Based on the priority, the tasks are assigned to the Virtual Machines. The task with highest priority is assigned to a Virtual Machine with highest processing power. The proposed model is simulated using the CloudSim toolkit. The experimental results reveal that the proposed model results in lower execution time as the resources are allocated more efficiently.

In cloud computing, task scheduling is the primary consideration when assigning resources dynamically to minimize makespan and increase performance. Due to the fact that data centers house a large number of computers, the rapid growth of cloud computing leads in massive energy consumption and emissions of carbon dioxide. Thus, in order to lower energy usage and carbon emissions, cloud service providers are searching for environmentally friendly solutions. Task scheduling has gained popularity as a result, where minimal energy usage and effective resource utilization are given careful thought. Although there are many applications and domains where cloud computing is widely employed, task and resource scheduling is still something that has to be improved. To lower costs, makespan, and energy consumption, this study presents the Cost and Energy-aware Task Scheduling Algorithm (CETSA). The suggested approach takes into account the load on each virtual machine as well as the trade-off between cost, energy consumption, and makespan in order to prevent virtual machines from overloading. The recommended method is put into practice with the help of the Cloud Sim toolkit and evaluated for various workloads. In addition, the recommended method lowers the rate of work failure, outperforms the prior algorithms in terms of energy consumption, and strikes a reasonable compromise between the makespan and the overall execution cost. Ultimately, the experimental findings demonstrate that the CETSA algorithm outperforms other algorithms in terms of energy usage.

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.