Overall Performance Of Cloud Research Articles

Multi-resource interleaving for task scheduling in cloud-edge system by deep reinforcement learning

Collaborative cloud–edge computing has been systematically developed to balance the efficiency and cost of computing tasks for many emerging technologies. To improve the overall performance of cloud–edge system, existing works have made progress in task scheduling by dynamically distributing the tasks with different latency thresholds to edge and cloud nodes. However, the relationship of multi-resource queueing among different tasks within a node is not well studied, which leaves the merit of optimizing the multi-resource queueing unexplored. To fill this gap and improve the efficiency of cloud–edge system, we propose DeepMIC, a deep reinforcement learning (DRL)-based multi-resource interleaving scheme for task scheduling in cloud–edge system. First, we formulate a multi-resource queueing model aiming at minimizing the weighted-sum delay of the pending tasks. The proposed model jointly considers the requests for computation, caching, and forwarding resources within a node based on the network information collected through Software-Defined Networking (SDN) and the management framework of Mobile Edge Computing (MEC). Then, we customize a DRL algorithm to ensure a timely solution of the model, which caters to the high throughput of tasks. Finally, we demonstrate that through the flexible scheduling of the tasks, DeepMIC reduces the average task response time and achieves better resource utilization.

Cloud Computing: Optimization using Particle Swarm Optimization to Improve Performance of Cloud

INTRODUCTION: In the contemporary world cloud computing is acknowledged as advanced technology to manage and store huge amount of data over the network. To handle the network traffic and effective task scheduling some efficient load balancing algorithm should be implemented. This can reduce the network traffic and overcome the problem of limited bandwidth. The various research articles represents ample amount of optimization techniques to overcome the transfer of data with limited bandwidth. Among all, few solutions has been chosen for current research article such as – optimization of load distribution of various resources provided by cloud.
 OBJECTIVES: In this paper, Comparative analysis of various task scheduling algorithms such as (FCFS, SJF, Round Robin & PSO) have been proposed in current research article to accumulate the outcome and evaluate the overall performance of cloud at different number of processing elements (pesNumber) .
 METHODS: Overall performance of task scheduling is significantly enhanced by PSO Algorithm implemented on cloud in comparison of FCFS, SJF and Round Robin. Outcomes of optimization technique has been implemented and tested over the CloudSim simulator.
 RESULTS: The comparative analysis conducted based on scalability for increasing the number of processing elements over the cloud. The major insight of proposed algorithm has shows that results are still better when number of VMs is increased and it successfully minimizes waiting time and turnaround time and completion time by 43% which is significantly high than outcomes of existing research articles.
 CONCLUSION: To optimize the task scheduling in cloud computing, comparative analysis of various task scheduling algorithms has been proposed, including Particle Swarm Optimization algorithm.

Identifying performance anomalies in fluctuating cloud environments: A robust correlative-GNN-based explainable approach

Cloud computing provides scalable and elastic resources to customers as a low-cost, on-demand utility service. Multivariate time series anomaly detection is crucial to promise the overall performance of cloud computing systems. However, due to the complexity and high dynamics of cloud environments, anomaly detections caused by irregular fluctuations in data and the robustness of models are challenging. To address these issues, we propose a deep learning-based anomaly detection method for multivariate time series for real-world operational clouds: Correlative-GNN with Multi-Head Self-Attention and Auto-Regression Ensemble Method (CGNN-MHSA-AR). Our method utilizes two parallel graph neural networks (GNN) to learn the time and feature inter-dependencies to achieve fewer false positives. Our approach leverages a multi-head self-attention, GRU, and AR model to capture multiple-dimensional information, leading to better detection robustness. CGNN-MHSA-AR can also provide an abnormal explanation based on the prediction error of its constituent univariate series. We compare the detection performance of CGNN-MHSA-AR with seven baseline methods on seven public datasets. The evaluation shows that the proposed CGNN-MHSA-AR outperforms its competitors with an F1-Score of 0.871 on average and is 19.9% better than state-of-the-art baseline methods. In addition, CGNN-MHSA-AR also offers to correctly identify the root cause of detected anomalies with up to 74.1% accuracy.

Application of Cloud Computing Technology in Information System Construction

With the rapid development and improvement of information technology, information system construction has new requirements and goals. Cloud computing technology is an inevitable choice in the construction of information system. By taking cloud computing technology as the basic structure framework, the phenomenon of data information island can be broken. The purpose of this paper is to study the application of cloud computing in the construction of information system. Taking the management mode of university campus as an example, based on the research of cloud computing technology, virtual machine technology and Internet of things technology, this paper discusses the data resource management algorithm of smart campus. Aiming at the scheduling model, strategy and research objectives of cloud computing resources in data center, this paper proposes a method of initial allocation and adaptive dynamic scheduling of virtual machine resources. According to the multi-dimensional vector characteristics of virtual machine, this paper proposes a resource allocation algorithm based on multi-objective evolution, and gives four important strategies. The experimental results show that the four advantages and strategies can effectively reduce the number of virtual machine migration and reduce the adverse impact on the overall performance of cloud computing core network.

Cloud Monitoring System: Basics, Phases and Challenges

Due to many economical benefits cloud computing has quickly become popular and is widely used for delivering services over the Internet. As the number of cloud users increases day by day, data centers are continuing to grow in terms of hardware resources, virtual resources and traffic volume; thus making cloud operation and management more intricate. To manage complex infrastructure of cloud, an efficient and effective Cloud Monitoring System (CMS) is needed to improve the overall performance of cloud. Cloud monitoring is the process of reviewing, controlling and managing the operational workflow and processes within a cloud-based IT asset or infrastructure. This paper deals with detailed study of CMSs based on their architecture, monitoring phases, properties and functions. It describes various phases of cloud monitoring activities and presents the comparative study of the state-of-the-art works of theses phases. Usage of agents for monitoring of cloud activities is also described. Finally, various challenges/issues and possible future directions of cloud monitoring is discussed. This paper helps researchers, engineers and scientists to know the state-of-the-art works for monitoring cloud activities.

Improved Hyper-Heuristic Scheduling with Load-Balancing and RASA for Cloud Computing Systems

Nowadays cloud computing has turned into a key innovation and has become a great solution for indulging a flexible utility oriented , online allocation and storage of computing resources and client’s information in lower expense, oninterest and dynamically scalable framework on pay per use premise. This technology is a new pattern emerging in IT environment with immense necessities of framework and resources. Job Scheduling Problem is an essential issue. For efficient usage and managing resources, administrations, scheduling plays a critical role. This paper apportion the performance enhancement of HyperHeuristic Scheduling Approach to schedule cloudlets and resources, by taking account of both , computation time and transmission cost with two detection operators. Load Balancing and RASA concept is applied for efficient Load Scheduling, resource utilization and thereby enhancing the overall performance of cloud computing environment. The numerical investigations of HHSA were performed on CloudSim. Experimental results generated via simulation shows that enhanced heuristic scheduling approach is much better than individual heuristic approach in terms of minimizing makespan time.