Cloud Computing System Research Articles

Cloud Task Scheduling Using Modified Penguins Search Optimization Algorithm

The cloud computing has emerged as a novel distributed computing system in past few years. It provides computation and resources over the Internet via dynamic provisioning of services. There are quite a few challenges and issues connected with implementation of cloud computing. This paper considers one of its major problems, i.e. task scheduling. The function of an efficient task scheduling algorithm is that it concentrates not only on attaining the requirements of the user but also in enhancing the efficiency of the cloud computing system. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. This paper proposes a modified Penguins Search Optimization Algorithm (MPeSOA) for efficient cloud task scheduling. The main contribution of our work is to schedule all tasks to available virtual machines so that the makespan is minimized, resource utilization is increased and the degree of imbalance is reduced. The proposed scheduling algorithm was simulated using the CloudSim 4.0 simulator. Experimental results showed that the proposed MPeSOA outperformed three existing meta-heuristics, namely Penguins Search Optimization Algorithm (PeSOA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).

Dynamic system allocation and application of cloud computing virtual resources based on system architecture

Abstract Cloud computing is a system development method based on dynamic sharing, which allows a large number of systems to be combined to provide services. The purpose of this work is to study the design and implementation of a dynamic virtual resource allocation system in cloud computing, whose architecture allows load balancing between virtual resource pools and reduces resource wastage. Using the cluster network topology, the resource usage of the dynamic system cluster can be monitored in real time, and the total cluster load can be automatically determined based on the monitoring data. The experiment is divided into two parts. Performance testing and scenario testing. Performance tests examine execution time, processor, and memory performance. In the scenario test, JMeter is used to simulate the occurrence of a large number of concurrent application access requests, the loss rate, and processing time of these requests on the cloud platform, and load balancing tests are performed. The test results show that the system running time is about 22–27 ms, the CPU utilization is about 90–95%, and the RAM is about 3.5 ms. The results show that cloud technology can improve resource scheduling of large tasks and optimize resource load balance.

Scheduling a multi-agent flow shop with two scenarios and release dates

Cloud computing is widely applied in modern industrial areas due to its technological advancement, cost reduction, and applicability. Packets (tasks) belonging to different applications (agents) compete to share the common cloud resource through a series of edge nodes (processors) in pursuit of fast transmission. This paper abstracts the cloud computing system as a multi-agent flow-shop scheduling (MAFS) problem. The objective is to minimise the total completion time of several agents with the restriction that the maximum lateness cannot exceed a given bound. Given the complexity of the considered problem, a branch and bound algorithm combined with several pruning rules and lower bounds is proposed to obtain optimal solutions. Furthermore, the considered problem is generalised to a bi-scenario version, and a bi-population cooperative co-evolutionary (BCCE) algorithm is proposed to solve it. A reinforcement learning-based method is presented to generate the initial population. Several problem-specific intensification strategies are constructed to explore promising solutions. Comprehensive experiments verified the effectiveness of the proposed algorithms. The industrial data from the China Earthquake Network Centre further confirmed the superiority of the BCCE algorithm. Overall, the MAFS model and the proposed algorithms effectively enhance the user experience and reasonably guarantee revenue.

Cost-efficient resource scheduling in cloud for big data processing using metaheuristic search black widow optimization (MS-BWO) algorithm

In a cloud computing system, resources can be accessed at a minimal cost whenever users raise request needs. The primary goal of cloud computing is to provide cost-efficiency of service scheduling to clients fast while using the least number of resources. Cloud Service Provisioning (CSP) can match consumer needs with minimal use of resources. There are several metaheuristic optimization algorithms have been developed in the field of CSP resource minimization and adequate computing resources are required to ensure client satisfaction. However, it performs poorly under a variety of practical constraints, including a vast amount of user data, smart filtering to boost user search, and slow service delivery. In this regard, propose a Black Widow Optimization (BWO) algorithm that reduces cloud service costs while ensuring that all resources are devoted only to end-user needs. It is a nature-inspired metaheuristic algorithm that involved a multi-criterion correlation that is used to identify the relationship between user requirements and available services and thereby, it is defined as an MS-BWO algorithm. Thus finds the most efficient virtual space allocation in a cloud environment. It uses a service provisioning dataset with metrics like energy usage, bandwidth utilization rate, computational cost, and memory consumption. In terms of data performance, the proposed MS-BWO outperforms exceed than other existing state-of-art-algorithms including Work-load aware Autonomic Resource Management Scheme(WARMS), Fuzzy Clustering Load balancer(FCL), Agent-based Automated Service Composition (A2SC) and Load Balancing Resource Clustering (LBRC), and an autonomic approach for resource provisioning (AARP)

Technological View on Smart Waste Management

The sustainable development goal of society, need of healthy environment and due to growing population quantity of solid waste increases rapidly drives the smart waste management. Since it is unhealthy for humans who involve in manual waste management those field works need automation. The Artificial Intelligence provides the way to achieve the automation to collect and process the solid waste with the help of Internet of Things, Cloud Computing and Intelligent Transport System. We explored the technological development towards the implementation of smart waste management to support further development in this domain.

A Cloud-based State of Health Estimation Method for Lithium-ion Batteries Using Sparse Charging Data

Under the current background of the national standard requirements for electric vehicle remote management and the companies’ needs for their digital platform services, automobile factories habitually transmit battery data to a cloud in a sparse way for state of health (SOH) estimation. Compared with the dynamic discharge condition of the vehicle, the charging process has been noticed in recent years due to its relatively stable advantages. In this paper, a cloud-based SOH estimation method for lithium-ion batteries using sparse charging data is proposed. A HI feature that can be extracted from the sparse data is derived and only the constant voltage charging process is considered. By using the experimental data deployed on the cloud computing system, the correlation of battery health with the HI is verified and SOH can be efficiently estimated by taking as this feature an input to three data-driven methods. The results show that all methods can achieve a low test error of 2% under 10s sparsity, which is promising to be applied in cloud sparse charging conditions.

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

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

Intellectual Property Rights of Control Technology to Contribute to SDGs

IoT creates Big Data and tends to create heavy load in communication and in data analysis when being stored directly to the cloud computing system. Therefore, the edge computing becomes important and effective for the IoT system. On the other hand, various issues occur from the viewpoint of business. In this paper, intellectual property of PID control solution is described.

Data Engineering Innovations: Exploring the Intersection with Cloud Computing, Machine Learning, and AI

Data engineering plays a pivotal role in the advancement of modern technologies such as cloud computing, machine learning (ML), and artificial intelligence (AI). This paper delves into the innovative intersections between data engineering and these cutting-edge fields, exploring how data engineering techniques and practices contribute to the development and optimization of cloud computing, ML, and AI systems. By examining current trends, challenges, and emerging methodologies, this study offers insights into the synergistic relationship between data engineering and these transformative technologies.

A battlefield task distribution strategy optimization model based on edge computing

The harsh environment of the battlefield challenges the delay and reliability of the cloud computing system composed of soldier terminals and BeiDou satellites. Based on this, this paper focuses on common problems in computational crowdsourcing under multi-agent and proposes a task distribution strategy optimization model based on battlefield edge computing. The process introduces the concept of flow pressure to solve these issues, load balancing and cascading congestion. Flow pressure means multiple servers can communicate and partially offload tasks that exceed the computational load to other servers. The computational overflow problem can be solved by task offloading based on flow pressure. Several different mainstream task allocation strategies are compared through experiments to demonstrate the model’s performance. The experimental results show that the model has lower latency and failure rate and reasonable computational resource occupation, which has a particular theoretical value and reference significance.

Kullback–Leibler Divergence of an Open-Queuing Network of a Cell-Signal-Transduction Cascade

Queuing networks (QNs) are essential models in operations research, with applications in cloud computing and healthcare systems. However, few studies have analyzed the cell's biological signal transduction using QN theory. This study entailed the modeling of signal transduction as an open Jackson's QN (JQN) to theoretically determine cell signal transduction, under the assumption that the signal mediator queues in the cytoplasm, and the mediator is exchanged from one signaling molecule to another through interactions between the signaling molecules. Each signaling molecule was regarded as a network node in the JQN. The JQN Kullback-Leibler divergence (KLD) was defined using the ratio of the queuing time (λ) to the exchange time (μ), λ/μ. The mitogen-activated protein kinase (MAPK) signal-cascade model was applied, and the KLD rate per signal-transduction-period was shown to be conserved when the KLD was maximized. Our experimental study on MAPK cascade supported this conclusion. This result is similar to the entropy-rate conservation of chemical kinetics and entropy coding reported in our previous studies. Thus, JQN can be used as a novel framework to analyze signal transduction.

Factors Influencing the Adoption of Cloud Computing and Information Management

Purpose: Over the years, advances in science and technology have resulted in several modern educational methods that have improved learners' expertise. These new methods have been distinguished by their reliance on technological innovations to improve educational effectiveness and efficiency. E-learning and distance learning are two of the most significant technological innovations that have emerged in recent years, with the goal of making the learner the focus of the educational process rather than the teacher . The appropriate application of technological innovations such as cloud computing in the educational process will undoubtedly lead to the discovery of novel solutions to educational problems. This study, therefore, sought to identify the factors influencing the adoption of cloud computing and information management systems in academic institutions.
 Methodology: A desktop literature review was used for this purpose in identifying relevant seminal references and journal articles for the study using Google Scholar. The inclusion criteria entailed papers that have already been done since the introduction of cloud computing in businesses and educational facilities.
 Findings: The study found that organizational factors such as top management commitment, staff attitudes, and appropriate skills have a significant influence on the adoption of cloud computing and information management technology within academic institutions. Environmental related factors including industry competition as well as technological factors such as perceived benefits, complexity, and compatibility influenced academic institutions' adoption of cloud computing technology.
 Unique Contribution to Theory, Practice and Policy: To promote cloud computing, service providers should provide successful case studies and statistics to assist companies, particularly academic institutions, in realizing the potential benefits.

Preferences and challenges towards the adoption of the fourth industrial revolution technologies by hotels: a multilevel concurrent mixed approach

PurposeThe aim of this mixed methods study sought to investigate the preferences and challenges towards fourth industrial revolution (FIR) technologies adoption at hotels in Malaysia.Design/methodology/approachThe study adopted partially-mixed concurrent quantitative-dominant mixed methods. The quantitative part of the study examined the preferred FIR technologies for hotels; whiles the qualitative part explored the challenges towards their adoption.FindingsThe results revealed that Internet of things, big data, cloud computing, artificial intelligence and cyber physical systems were the preferred FIR technologies for hotels. The findings revealed significant relationships between respondents' socio-demographic characteristics (age, gender, working experience, etc.) and preferred FIR technologies. Penultimately, challenges that affect the adoption of FIR technologies including investment cost, migration cost, trust, etc., were discussed.Originality/valueIn effect, findings from this study provide valuable information for policy makers (i.e. hotel owners, management, technology providers and future researchers). Most especially, the study unearths specific FIR technologies preferred by the hospitality and tourism industry. Also, the significant interplay of these preferences vis a vis respondents' socio-demographic characteristics, as well as challenges influencing their adoption also have major implications for policy makers. Another valuable contribution of this study lies in the research method utilised, since most studies on technology adoption either use quantitative or qualitative. These contributions for theory and practice have been thoroughly discussed.

A comprehensive survey of whale optimization algorithm: modifications and classification

Whale optimization algorithm (WOA) is an emerging nature-inspired, swarm-intelligence based algorithm to solve optimization problems more efficiently. This algorithm is based on the bubble-net hunting strategy of the humpback whales. It has gained immense popularity among researchers, typically, due to its simple nature, fast convergence, and having minimum parameters. In the recent past, it has been widely adopted in various fields including data mining, machine learning, wireless sensor networks, cloud computing, civil engineering, and power systems due to its optimal performance. The WOA has given competitive results in comparison to the state-of-the-art optimization algorithms. In this study, we aim to present a comprehensive survey of WOA consisting of more than eighty existing variants of WOA. More specifically, we intend to put forward key aspects of WOA variants with reference to modifications and applications. Further, we classify the most dominant variants of WOA in distinct categories based on modification area such as equation modification, parameter tuning or the problem space for which an algorithm has been specifically altered. We believe that this study will be beneficial for the community working on optimization problems and it can serve as a basis for understanding the modification and improvement process of an optimization algorithm.

Implementasi Keamanan di Cloud Storage Menggunakan Algoritma Key Public Berdasarkan Block Cipher

Cloud computing is a new era of the modern world. It is an emerging paradigm and has become one of the hottest research to date, because of its ability to reduce the costs associated with computing. The main issues associated with cloud computing are data privacy, security and authenticity. The aim of the researcher is to find out how the model of cloud data storage and data security of cloud computing systems. In this research, researchers propose an efficient method for providing data storage. Security in cloud computing uses a new public key algorithm based on a linear block cipher. This algorithm has been proven by previous studies such as Data encryption, E-commerce, Digital signature. In research, researchers apply the same algorithm, namely cloud computing. In this algorithm several important security services are included such as key generation, encryption and decryption provided in the cloud computing system. The scope of this research is to solve security problems in cloud provisioning and cloud consumers by using new cryptographic methods.

An enhanced ordinal optimization with lower scheduling overhead based novel approach for task scheduling in cloud computing environment

Efficient utilization of available computing resources in Cloud computing is one of the most challenging problems for cloud providers. This requires the design of an efficient and optimal task-scheduling strategy that can play a vital role in the functioning and overall performance of the cloud computing system. Optimal Schedules are specifically needed for scheduling virtual machines in fluctuating & unpredictable dynamic cloud scenario. Although there exist numerous approaches for enhancing task scheduling in the cloud environment, it is still an open issue. The paper focuses on an improved & enhanced ordinal optimization technique to reduce the large search space for optimal scheduling in the minimum time to achieve the goal of minimum makespan. To meet the current requirement of optimal schedule for minimum makespan, ordinal optimization that uses horse race conditions for selection rules is applied in an enhanced reiterative manner to achieve low overhead by smartly allocating the load to the most promising schedule. This proposed ordinal optimization technique and linear regression generate optimal schedules that help achieve minimum makespan. Furthermore, the proposed mathematical equation, derived using linear regression, predicts any future dynamic workload for a minimum makespan period target.

INCREASING THE SPATIAL ACCURACY OF THE LAND USE MAP USING FUSION OF OPTICAL AND RADAR IMAGES OF SENTINEL AND GOOGLE EARTH ENGINE

Abstract. Nowadays, exact and real-time land use and land cover (LULC) maps are imperative to supply precise data for energetic observing, arranging and arrive administration. With the appearance of cloud computing frameworks, time arrangement includes extraction methods, and machine learning classifiers, unused openings emerge in more precise and larger-scale LULC mapping. In this research, the aim is to obtain a land use map with a spatial resolution of 10 meters by combining Sentinel-1 radar images and Sentinel-2 optical images. The processing was done in Google Earth Engine cloud computing system. Also, in this research, NDVI map and NDBI map were used to increase classification accuracy. Classification was done using the decision tree method and in eleven classes. To evaluate the classification accuracy and the final map, randomly collected points were used in the study area. The results of the comparison of the ground collection points and the final classified map showed that the classification accuracy in this study was around 94%. The results obtained from such as research can be optimally used in urban planning and crisis management.

Cloud Environment Task Scheduling Optimization of Modified Genetic Algorithm

From the availability of resources to the accomplishment of tasks, cloud computing is a development of supercomputing. One of the most trustworthy paradigms in computing technology is built on internet-based parallel and distributed computing models. Optimization algorithms can be used to distribute user workloads to provided logical resources termed 'Virtual Machines' in the cloud computing system, which is a major aspect of resource management (VM). A fundamental challenge in cloud computing is the dynamic heterogeneity of resources and workloads, which necessitates efficient task scheduling and distribution. It is possible that task scheduling in distributed environments will improve our understanding of workflow scheduling, independent task scheduling that takes into account security and execution time for applications, trust between various system entities, and improved system utilisation and energy efficiency, among other things. The goal of this research is to contribute to these advancements in these areas: An independent task scheduling system based on genetics is presented to obtain the best outcomes in terms of time and resource consumption while allocating tasks to resources in accordance with the task's security needs. Various meta-heuristic algorithms, such as Genetic Algorithm, are currently being used to solve task scheduling difficulties.

The Study on a Model for Monitoring and Supervising the Ability to Face and Cope with Obstruction for Students Participating in Work-Based Learning (WBL)

The objectives of this research were 1) to synthesize and create a monitoring system model to develop the ability to face and cope with obstruction of interns in the Work-Based Learning (WBL) system; 2) to assess the suitability of the monitoring system to develop the ability to face and cope with obstruction of interns in the WBL system. The sample is five experts using a selective method of purposive sampling in evaluating a monitoring system model to develop the ability to face and cope with obstruction of the interns in the WBL system. From the results of the research, there was a monitoring system model to develop the ability to face and cope with obstruction of interns in the WBL system named the SP4D model. The results of the synthesized model evaluation showed that the most issue accepted by experts were 1) Component 2, Student Monitoring Process, Step 3 Promotion, and development of the ability to face and cope with obstruction, 2) Component 3, Cloud Computing system in Google Classroom for Adversity Quotient (AQ) development activities, and 3) Component 3, Cloud Computing system in Google Drive for data storage. The least acceptable issue by experts was Component 1, Defining the scope of users and students. The total synthesis was at the most suitable level (mean at 4.51, the standard deviation at 0.53) and experts agree that the synthesized model can be used appropriately.

A Novel Approach for Enhance Fusion Based Healthcare System In Cloud Computing

Individuals start - ups and large corporations in the healthcare sector have new opportunities to outsource data and outsourcing computation offers to cloud computing. Although the cloud computing paradigm presents users with interesting and cost effective opportunities still in its early stage, and using the cloud introduces with new obstacles. A another issue is the security of cloud data, which may be affected the data particularly in the case of healthcare systems that store and process sensitive data and is outsourced to a cloud computing system.Although there has been significant progress in the development of health services there are still issues that need to be settled regarding, integrity, the security, large-scale deployment, service integration, confidentiality of sensitive medical data. To ensure that sensitive medical data is captured, stored and consumed securely, an information sharing policy syntax based on rules, the Data Capture and Auto Identification Reference (DACAR) platform features a Single Point of Contact as well as data buckets that are hosted on a cost-effective cloud infrastructure and scalable.As a result, security, accuracy, and precision are achieved in this analysis and query time is reduced.