One for All: Unified Workload Prediction for Dynamic Multi-tenant Edge Cloud Platforms

Workload prediction in multi-tenant edge cloud platforms (MT-ECP) is vital for efficient application deployment and resource provisioning. However, the heterogeneous application patterns, variable infrastructure performance, and frequent deployments in MT-ECP pose significant challenges for accurate and efficient workload prediction. Clustering-based methods for dynamic MT-ECP modeling often incur excessive costs due to the need to maintain numerous data clusters and models, which leads to excessive costs. Existing end-to-end time series prediction methods are challenging to provide consistent prediction performance in dynamic MT-ECP. In this paper, we propose an end-to-end framework with global pooling and static content awareness, DynEformer, to provide a unified workload prediction scheme for dynamic MT-ECP. Meticulously designed global pooling and information merging mechanisms can effectively identify and utilize global application patterns to drive local workload predictions. The integration of static content-aware mechanisms enhances model robustness in real-world scenarios. Through experiments on five real-world datasets, DynEformer achieved state-of-the-art in the dynamic scene of MT-ECP and provided a unified end-to-end prediction scheme for MT-ECP.Comment: 10 pages, 10 figure

Machine Learning Algorithms in Cloud Manufacturing - A Review

Cloud computing has advanced significantly in terms of storage, QoS, online service availability, and integration with conventional business models and procedures. The traditional manufacturing firm becomes Cloud Manufacturing when Cloud Services are integrated into the present production process. The capabilities of Cloud Manufacturing are enhanced by Machine Learning. A lot of machine learning algorithms provide the user with the desired outcomes. The main objectives are to learn more about the architecture and analysis of Cloud Manufacturing frameworks and the role that machine learning algorithms play in cloud computing in general and Cloud Manufacturing specifically. Machine learning techniques like SVM, Genetic Algorithm, Ant Colony Optimisation techniques, and variants are employed in a cloud environment

Towards accurate prediction for high-dimensional and highly-variable cloud workloads with deep learning

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordResource provisioning for cloud computing necessitates the adaptive and accurate prediction of cloud workloads. However, the existing methods cannot effectively predict the high-dimensional and highly-variable cloud workloads. This results in resource wasting and inability to satisfy service level agreements (SLAs). Since recurrent neural network (RNN) is naturally suitable for sequential data analysis, it has been recently used to tackle the problem of workload prediction. However, RNN often performs poorly on learning longterm memory dependencies, and thus cannot make the accurate prediction of workloads. To address these important challenges, we propose a deep Learning based Prediction Algorithm for cloud Workloads (L-PAW). First, a top-sparse auto-encoder (TSA) is designed to effectively extract the essential representations of workloads from the original high-dimensional workload data. Next, we integrate TSA and gated recurrent unit (GRU) block into RNN to achieve the adaptive and accurate prediction for highly-variable workloads. Using realworld workload traces from Google and Alibaba cloud data centers and the DUX-based cluster, extensive experiments are conducted to demonstrate the effectiveness and adaptability of the L-PAW for different types of workloads with various prediction lengths. Moreover, the performance results show that the L-PAW achieves superior prediction accuracy compared to the classic RNN-based and other workload prediction methods for high-dimensional and highly-variable real-world cloud workloads

SGA Model for Prediction in Cloud Environment

With virtual information, cloud computing has made applications available to users everywhere. Efficient asset workload forecasting could help the cloud achieve maximum resource utilisation. The effective utilization of resources and the reduction of datacentres power both depend heavily on load forecasting. The allocation of resources and task scheduling issues in clouds and virtualized systems are significantly impacted by CPU utilisation forecast. A resource manager uses utilisation projection to distribute workload between physical nodes, improving resource consumption effectiveness. When performing a virtual machine distribution job, a good estimation of CPU utilization enables the migration of one or more virtual servers, preventing the overflow of the real machineries. In a cloud system, scalability and flexibility are crucial characteristics. Predicting workload and demands would aid in optimal resource utilisation in a cloud setting. To improve allocation of resources and the effectiveness of the cloud service, workload assessment and future workload forecasting could be performed. The creation of an appropriate statistical method has begun. In this study, a simulation approach and a genetic algorithm were used to forecast workloads. In comparison to the earlier techniques, it is anticipated to produce results that are superior by having a lower error rate and higher forecasting reliability. The suggested method is examined utilizing statistics from the Bit brains datacentres. The study then analyses, summarises, and suggests future study paths in cloud environments

Enabling Distributed Applications Optimization in Cloud Environment

The past few years have seen dramatic growth in the popularity of public clouds, such as Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Container-as-a-Service (CaaS). In both commercial and scientific fields, quick environment setup and application deployment become a mandatory requirement. As a result, more and more organizations choose cloud environments instead of setting up the environment by themselves from scratch. The cloud computing resources such as server engines, orchestration, and the underlying server resources are served to the users as a service from a cloud provider. Most of the applications that run in public clouds are the distributed applications, also called multi-tier applications, which require a set of servers, a service ensemble, that cooperate and communicate to jointly provide a certain service or accomplish a task. Moreover, a few research efforts are conducting in providing an overall solution for distributed applications optimization in the public cloud. In this dissertation, we present three systems that enable distributed applications optimization: (1) the first part introduces DocMan, a toolset for detecting containerized application’s dependencies in CaaS clouds, (2) the second part introduces a system to deal with hot/cold blocks in distributed applications, (3) the third part introduces a system named FP4S, a novel fragment-based parallel state recovery mechanism that can handle many simultaneous failures for a large number of concurrently running stream applications