Task Scheduling Balancing User Experience and Resource Utilization on Cloud

Cloud computing has been gaining undeniable popularity over the last few years. Among many techniques enabling cloud computing, task scheduling plays a critical role in both efficient resource utilization for cloud service providers and providing an excellent user experience to the clients. In this study, we proposed a priority cloud task scheduling approach that considers users input to calculate priority, while at the same time, efficiently utilizes available resources. This approach is designed for the consideration of both user satisfaction and utilization of cloud services. In the proposed approach, clients will be required to input their time and cost preferences to determine the priority of each task. We conducted our experiments in Python and AWS to best simulate a real-world cloud environment and compared the proposed approach to a first-come-first-serve approach. We measured the performance of our approach in terms of average task wait time AWT, average resource idle time aRIT, and the order the tasks were scheduled. The experimental results show that our approach outperforms the first-come-first-serve approach in AWT,aRIT, and the order the tasks were scheduled

A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling

The following interdisciplinary article presents a memetic algorithm with applying deep reinforcement learning (DRL) for solving practically oriented dual resource constrained flexible job shop scheduling problems (DRC-FJSSP). From research projects in industry, we recognize the need to consider flexible machines, flexible human workers, worker capabilities, setup and processing operations, material arrival times, complex job paths with parallel tasks for bill of material (BOM) manufacturing, sequence-dependent setup times and (partially) automated tasks in human-machine-collaboration. In recent years, there has been extensive research on metaheuristics and DRL techniques but focused on simple scheduling environments. However, there are few approaches combining metaheuristics and DRL to generate schedules more reliably and efficiently. In this paper, we first formulate a DRC-FJSSP to map complex industry requirements beyond traditional job shop models. Then we propose a scheduling framework integrating a discrete event simulation (DES) for schedule evaluation, considering parallel computing and multicriteria optimization. Here, a memetic algorithm is enriched with DRL to improve sequencing and assignment decisions. Through numerical experiments with real-world production data, we confirm that the framework generates feasible schedules efficiently and reliably for a balanced optimization of makespan (MS) and total tardiness (TT). Utilizing DRL instead of random metaheuristic operations leads to better results in fewer algorithm iterations and outperforms traditional approaches in such complex environments.Comment: This article has been accepted by IEEE Access on June 30, 202

Hybrid Genetic Algorithms for Scheduling High-Speed Multimedia Systems

It has been observed that most conventional operating systems could not cope with the scheduling of multimedia tasks owing to the large size of these files. For instance, processing of multimedia tasks using the traditional operating systems are fraught with problems such as low quality of service and delay jitters. In order to address these problems, a scheduling algorithm christened hybrid genetic algorithm for multimedia task scheduling (HGAMTS) was developed. It employed heuristic knowledge of the problem domain to model a hybrid genetic algorithm in a multiprocessor environment. The system is made up of the scheduler model and the task model. The scheduler model consist a centralized dynamic scheduling scheme. In this scheme, all tasks arrive at a central processor (scheduler). The model has a minimum of five and maximum of ten processors. Attached to each processor is a dispatch queue

A Framework for Leveraging Artificial Intelligence in Project Management

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThis dissertation aims to support the project manager in their daily tasks. As we use artificial intelligence (AI) and machine learning (ML) in everyday life, it is necessary to include them in business and change traditional ways of working. For the purpose of this study, it is essential to understand challenges and areas of project management and how artificial intelligence can contribute to them. A theoretical overview, applying the knowledge of project management, will show a holistic view of the current situation in the enterprises. The research is about artificial intelligence applications in project management, the common activities in project management, the biggest challenges, and how AI and ML can support it. Understanding project managers help create a framework that will contribute to optimizing their tasks. After designing and developing the framework for applying artificial intelligence to project management, the project managers were asked to evaluate. This study is essential to increase awareness among the stakeholders and enterprises on how automation of the processes can be improved and how AI and ML can decrease the possibility of risk and cost along with improving the happiness and efficiency of the employees

Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing

Grid computing is a distributed system with heterogeneous infrastructures. Resource management system (RMS) is one of the most important components which has great influence on the grid computing performance. The main part of RMS is the scheduler algorithm which has the responsibility to map submitted tasks to available resources. The complexity of scheduling problem is considered as a nondeterministic polynomial complete (NP-complete) problem and therefore, an intelligent algorithm is required to achieve better scheduling solution. One of the prominent intelligent algorithms is ant colony system (ACS) which is implemented widely to solve various types of scheduling problems. However, ACS suffers from stagnation problem in medium and large size grid computing system. ACS is based on exploitation and exploration mechanisms where the exploitation is sufficient but the exploration has a deficiency. The exploration in ACS is based on a random approach without any strategy. This study proposed four hybrid algorithms between ACS, Genetic Algorithm (GA), and Tabu Search (TS) algorithms to enhance the ACS performance. The algorithms are ACS(GA), ACS+GA, ACS(TS), and ACS+TS. These proposed hybrid algorithms will enhance ACS in terms of exploration mechanism and solution refinement by implementing low and high levels hybridization of ACS, GA, and TS algorithms. The proposed algorithms were evaluated against twelve metaheuristic algorithms in static (expected time to compute model) and dynamic (distribution pattern) grid computing environments. A simulator called ExSim was developed to mimic the static and dynamic nature of the grid computing. Experimental results show that the proposed algorithms outperform ACS in terms of best makespan values. Performance of ACS(GA), ACS+GA, ACS(TS), and ACS+TS are better than ACS by 0.35%, 2.03%, 4.65% and 6.99% respectively for static environment. For dynamic environment, performance of ACS(GA), ACS+GA, ACS+TS, and ACS(TS) are better than ACS by 0.01%, 0.56%, 1.16%, and 1.26% respectively. The proposed algorithms can be used to schedule tasks in grid computing with better performance in terms of makespan

Role of Evolutionary Algorithms in Construction Projects Scheduling

Due to the increase in the stakeholders and their objectives the construction projects have significantly been affected by the ongoing demands leading to increase in complexity of scheduling problems, research in the field of Multi-Objective Optimization (MOO) have increased significantly. Through their population-based search methodologies, Evolutionary Algorithms drove attention to their efficiency in addressing scheduling problems involving two or three objectives. Genetic Algorithms (GA) particularly have been used in most of the construction optimization problems due to their ability to provide near-optimal Pareto solutions in a reasonable amount of time for almost all objectives. However, when optimizing more than three objectives, the efficiency of such algorithms degrades and trade-offs among conflicting objectives must be made to obtain an optimal Pareto Frontier. To address that, this paper aims to provide a comparative analysis on four evolutionary algorithms (Genetic algorithms – Memetic algorithms – Particle Swarm – Ant colony) in the field of construction scheduling optimization, gaps are addressed, and recommendations are proposed for future research development

Task Scheduling Optimization in Cloud Computing by Jaya Algorithm

Cloud computing provides resources to its consumers as a service. The cloud computing paradigm offers dynamic services by providing virtualized resources via the internet for enabling applications, and these services are provided by large-scale data centers known as clouds. Cloud computing is entirely reliant on the internet to provide its services to consumers. Cloud computing offers several advantages, including the fact that users only pay for what they use weekly, monthly, or yearly, that anybody with an internet connection may use the cloud, and that there is no need to purchase resources, hardware, or software on their own. This paper proposes an efficient task scheduling algorithm based on the Jaya algorithm for the cloud computing environment. We evaluate the performance of our method by applying it to three instances. The recommended technique produced the optimal solution in makespan, speedup, efficiency, and throughput, according to the findings

An Efficient Firefly Algorithm for Optimizing Task Scheduling in Cloud Computing Systems

As user service demands change constantly, task scheduling becomes an extremely significant study area within the cloud environment. The goal of scheduling is distributing the tasks on available processors in order to achieve the shortest possible makespan while adhering to priority constraints. In heterogeneous cloud computing resources, task scheduling has a large influence on system performances. The various processes in the heuristic-based algorithm of scheduling will result in varied makespans when heterogeneous resources are utilized. As a result, a smart method of scheduling must be capable of establishing precedence efficacy for each task to decrease makespan time. In our study, we develop a novel efficient method of scheduling tasks according to the firefly algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem. We evaluate the performance of our algorithm by putting it through three situations with changing amounts of processors and numbers of tasks. The findings of the experiment reveal that our suggested technique found optimal solutions substantially more frequently in terms of makespan time when compared with other methods

Task Scheduling Based on Grey Wolf Optimizer Algorithm for Smart Meter Embedded Operating System

In recent years, with the rapid development of electric power informatization, smart meters are gradually developing towards intelligent IOT. Smart meters can not only measure user status, but also interconnect and communicate with cell phones, smart homes and other cloud devices, and these core functions are completed by the smart meter embedded operating system. Due to the dynamic heterogeneity of the user program side and the system processing side of the embedded system, resource allocation and task scheduling is a challenging problem for embedded operating systems of smart meters. Smart meters need to achieve fast response and shortest completion time for user program side requests, and also need to take into account the load balancing of each processing node to ensure the reliability of smart meter embedded systems. In this paper, based on the advanced Grey Wolf Optimizer, we study the scheduling principle of the service program nodes in the smart meter operating system, and analyze the problems of the traditional scheduling algorithm to find the optimal solution. Compared with traditional algorithms and classical swarm intelligence algorithms, the algorithm proposed in this paper avoids the dilemma of local optimization, can quickly allocate operating system tasks, effectively shorten the time consumption of task scheduling, ensure the real-time performance of multi task scheduling, and achieve the system tuning balance. Finally, the effectiveness of the algorithm is verified by simulation experiments

Priority-grouping method for parallel multi-scheduling in Grid

With the advent in multicore computers, the scheduling of Grid jobs can be made more effective if scaled to fully utilize the underlying hardware, and parallelized to benefit from the exploitation of multicores. The fact that sequential algorithms do not scale with multicore systems nor benefit from parallelism remains a major obstacle to scheduling in the Grid. As multicore systems become ever more pervasive in our computing lives, over reliance on such systems for passive parallelism does not offer the best option in harnessing the benefits of their multiprocessors for Grid scheduling. An explicit means of exploiting parallelism for Grid scheduling is required. The Group-based Parallel Multi-scheduler, introduced in this paper, is aimed at effectively exploiting the benefits of multicore systems for Grid scheduling by splitting jobs and machines into paired groups and independently scheduling jobs in parallel from those groups. We implemented two job grouping methods, Execution Time Balanced (ETB) and Execution Time Sorted then Balanced (ETSB), and two machine grouping methods, Evenly Distributed (EvenDist) and Similar Together (SimTog). For each method, we varied the number of groups between 2, 4 and 8. We then executed the MinMin Grid scheduling algorithm independently within the groups. We demonstrated that by sharing jobs and machines into groups before scheduling, the computation time for the scheduling process drastically improved by magnitudes of 85% over the ordinary MinMin algorithm when implemented on a HPC system. We also found that our balanced group based approach achieved better results than our previous Priority based grouping approach