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Technical Scheme for Optimizing Urban and Rural Logistics Operations and Improving the Informatization of Rural Logistics
(2024-02-13)
Zheng, Zhong
;
He, Wanxian
;
Wei, Ganglan
In order to optimize the core problems encountered in the operation of urban and rural logistics and improve the informatization degree of rural logistics, the authors of this paper conduct research on the application of big data, cloud computing, and other technologies in urban and rural logistics and informatization. Based on the current problems, the authors adopt the hierarchical design, C/S architecture, and cloud API technology to improve the low level of informatization in urban and rural logistics, as well as optimize the dispersion and repetitive construction of logistics resources for logistics enterprises in urban and rural areas. Overall, a feasible sharing technology solution for urban-rural logistics informatization has been provided, providing a certain reference for optimizing urban-rural logistics operations and improving rural informatization issues.
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Stateless System Performance Prediction and Health Assessment in Cloud Environments: Introducing cSysGuard, an Ensemble Modeling Approach
(2024-01-01)
Chairatana, Nutt
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Chawuthai, Rathachai
Stateless cloud computing presents remarkable scalability and cost-effectiveness by offering dynamically adjustable resources tailored to fluctuating demands, eliminating the constraints of stateful architectures. However, the challenges presented by dynamic workload are substantial in the context of system health monitoring, frequently leading to service interruptions owing to insufficient resources. It underscores the need for the development of more efficient monitoring systems. Our study introduces cSysGuard, a novel framework designed to enhance monitoring capabilities within cloud environments. The methodology employs an ensemble regression model with a stacking strategy to forecast dynamic performance metrics. The algorithm also leverages a classification model to assess the system's health based on forecasted metrics, effectively identifying potential failures in the future. Under the configuration utilized, our evaluations demonstrated increased predictive performance with cSysGuard in forecasting various metrics compared to traditional models. The results showed an improvement of up to a remarkable 2.28-fold increase, varying significantly based on the specific metric under consideration. In addition, the effectiveness of health assessment was achieved through Decision Trees with hyperparameter tuning, resulting in a macro-averaged F1 score of 89.79%. This research contributes to both the theoretical and practical aspects of server monitoring, presenting a solution that assesses system performance metrics and health to tackle dynamic challenges in cloud infrastructure.
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Multi-Objective Task Scheduling Optimization for Load Balancing in Cloud Computing Environment Using Hybrid Artificial Bee Colony Algorithm with Reinforcement Learning
(2022-01-01)
Kruekaew, Boonhatai
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Kimpan, Warangkhana
Workload balancing in cloud computing is still challenging problem, especially in Infrastructure as a Service (IaaS) in the cloud model. A problem that should not occur during cloud access is a host or server being overloaded or underloaded, which may affect the processing time or may result in a system crash. Therefore, to prevent these problems, an appropriate schedule of access should be considered so that the system can distribute tasks across all available resources, which is called load balancing. The load balancing technique should ensure that all Virtual Machines (VMs) are used appropriately. In this paper, an independent task scheduling approach in cloud computing is proposed using a Multi-objective task scheduling optimization based on the Artificial Bee Colony Algorithm (ABC) with a Q-learning algorithm,which is a reinforcement learning technique that helps the ABC algorithm work faster, called the MOABCQ method. The proposed method aims to optimize scheduling and resource utilization, maximize VM throughput, and create load balancing between VMs based on makespan, cost, and resource utilization, which are limitations of concurrent considerations. Performance analysis of the proposed method was compared using CloudSim with the existing load balancing and scheduling algorithms: Max-Min, FCFS, HABC-LJF, Q-learning, MOPSO, and MOCS algorithms in three datasets: Random, Google Cloud Jobs (GoCJ), and Synthetic workload. The experimental results indicated that the algorithms used MOABCQ approach outperformed the other algorithms in terms of reducing makespan, reducing cost, reducing degree of imbalance, increasing throughput and average resource utilization.
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The effect of online mentoring system through professional learning community with information and communication technology via cloud computing for pre-service teachers in Thailand
(2021-01-01)
Karo, Danaisak
;
Petsangsri, Sirirat
The objective of this study was to evaluate the utilization of Online Mentoring system through Professional Learning Community with Information and Communication Technology via Cloud Computing for Pre-Service Teachers in Thailand. The sample was 26 pre-service teachers of computer education program, Faculty of Education, Phuket Rajabhat University of academic year 2018. The instrument was the evaluation form of system effectiveness in 4 components which were the ability of function requirement test; the accuracy of function test; the accessibility and simplicity of usability test; and the security test. The findings indicated that each element was at the highest level and the overall average was at 4.68; the Standard Deviation was 0.36. Therefore, it illustrated that online mentoring through professional learn was effective at the highest level.
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Enhancing of artificial bee colony algorithm for virtual machine scheduling and load balancing problem in cloud computing
(2020-01-01)
Kruekaew, Boonhatai
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Kimpan, Warangkhana
This paper proposes the combination of Swarm Intelligence algorithm of artificial bee colony with heuristic scheduling algorithm, called Heuristic Task Scheduling with Artificial Bee Colony (HABC). This algorithm is applied to improve virtual machines scheduling solution for cloud computing within homogeneous and heterogeneous environments. It was introduced to minimize makespan and balance the loads. The scheduling performance of the cloud computing system with HABC was compared to that supplemented with other swarm intelligence algorithms: Ant Colony Optimization (ACO) with standard heuristic algorithm, Particle Swarm Optimization (PSO) with standard heuristic algorithm and improved PSO (IPSO) with standard heuristic algorithm. In our experiments, CloudSim was used to simulate systems that used different supplementing algorithms for the purpose of comparing their makespan and load balancing capability. The experimental results can be concluded that virtual machine scheduling management with artificial bee colony algorithm and largest job first (HABC_LJF) outperformed those with ACO, PSO, and IPSO.
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Cloud computing classroom acceptance model in Thailand higher education’s institutes: A conceptual framework
(2018-09-22)
Chaveesuk, Singha
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Wutthirong, Phayat
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Chaiyasoonthorn, Wornchanok
In the era of digitization, higher education institutes and universities are strengthening to provide a new way of virtual technologies and electronics for learning and teaching. Cloud computing technology is currently considered to become an integral part of the educational experience and will have a significant impact on the learning environment. This innovative technology has been applied to cloud-computing learning system and has facilitated the development of cloud computing for classrooms. With the development of the cloud computing classrooms, improving students’ attitudes is quite significant to adopt and use cloud computing classroom, however, student behavioral intention using cloud computing remains unclear among higher education institutions in Thailand. Limited studies integrating and analyzing factors influencing user acceptance of cloud computing classroom in higher education are investigated for guiding in students’ adoption decision. Thus, this study review significant factors that could affect user acceptance of cloud computing classroom and develop a conceptual framework of cloud computing classroom acceptance model in higher education’s institutions in Thailand. A conceptual framework developed is theoretically based on Technology Acceptance Model (TAM) and Service and System Quality (SSQ). This framework provides a comprehensive understanding of the potential factors influencing the students’ behavioral intention and the use of cloud computing classrooms.
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A study of integration Internet of Things with health level 7 protocol for real-time healthcare monitoring by using cloud computing
(2017-12-19)
Plathong, Kuntinan
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Surakratanasakul, Boonprasert
Thailand will fully enter aging society in 2025 has a major impact on the rise of patients in each country. There is much organization has tried to develop technology for support aging society. Internet of Things is one of them and it can be connected device to device. Currently, there are many medical devices in Internet of things such as wearable device, digital blood pressure device, blood glucose meter etc. The data from these devices have been used to accurate treatment patients. From the study, related literature. Researcher realizes the importance of accurate medical data transfer and can be supported amount data. This paper present conceptual framework of integration Internet of Things with Health Level 7 protocol for support real-time healthcare monitoring by using Cloud computing. The objective of the conceptual framework is to help elderly or people can check health care with themselves anywhere anytime by using the medical device in Internet of Things. These data real-time storage to Cloud computing with JSON language. So, public health and hospitals can use information for treatment patients or give advice about healthcare through web service with XML language according to Health Level 7 standard.
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Thoth: Automatic resource management with machine learning for container-based cloud platform
(2017-01-01)
Sangpetch, Akkarit
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Sangpetch, Orathai
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Juangmarisakul, Nut
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Warodom, Supakorn
Platform-As-A-Service (PaaS) providers often encounter fluctuation in computing resource usage due to workload changes, resulting in performance degradation. To maintain acceptable service quality, providers may need to manually adjust resource allocation according to workload dynamics. Unfortunately, this approach will not scale well as the number of applications grows. We thus propose Thoth, a dynamic resource management system for PaaS using Docker container technology. Thoth automatically monitors resource usage and dynamically adjusts appropriate amount of resources for each application. To implement the automatic-scaling algorithm, we select three algorithms, namely Neural Network, Q-Learning and our rule-based algorithm, to study and evaluate. The experimental results suggest that Q-Learning can the best adapt to the load changes, followed by a rule-based algorithm and NN. With Q-Learning, Thoth can save computing resources by 28.95% and 21.92%, compared to Neural Network and the rule-based algorithm respectively, without compromising service quality.
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Security context framework for distributed healthcare IoT platform
(2016-01-01)
Sangpetch, Orathai
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Sangpetch, Akkarit
As Internet of Things (IoT) is entering mainstream, data privacy and security in information exchange becomes a major concern and a barrier for potential adopters, especially in healthcare regime. Information from health IoT devices and services is sensitive and confidential. While many existing works have proposed enhancements and security prospects for individual devices and components in IoT ecosystems, they still do not address the underlying challenge which is the lack of sufficient security within systems. Effective security has to be built-in, not patched upon. To efficaciously tackle the challenge in distributed IoT systems, we present a security context framework which applies adaptive security contexts to properly track data of interest. The proposed solution can achieve accountability and track information propagation, involving devices, services and parties who have responsibility and potential legal liability. This could help leverage not just technical but also policy and legal aspects to enable health IoT adoption.
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Virtual machine scheduling management on Cloud computing using Artificial Bee Colony
(2014-01-01)
Kruekaew, B.
;
Kimpan, W.
Resource scheduling management design on Cloud computing is an important problem. Scheduling model, cost, quality of service, time, and conditions of the request for access to services are factors to be focused. A good task scheduler should adapt its scheduling strategy to the changing environment and load balancing Cloud task scheduling policy. Therefore, in this paper, Artificial Bee Colony (ABC) is applied to optimize the scheduling of Virtual Machine (VM) on Cloud computing. The main contribution of work is to analyze the difference of VM load balancing algorithm and to reduce the makespan of data processing time. The scheduling strategy was simulated using CloudSim tools. Experimental results indicated that the combination of the proposed ABC algorithm, scheduling based on the size of tasks, and the Longest Job First (LJF) scheduling algorithm performed a good performance scheduling strategy in changing environment and balancing work load which can reduce the makespan of data processing time.

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