2020· International Journal of Data Engineering and Intelligent Computing· Vol 3, pp. 01-10· 0 citations
TL;DR
The key design principles of an EDP, including data distribution, workload optimization, auto-scaling, and cost analytics, and how these can be implemented across multiple cloud providers are discussed.
Abstract
In today’s dynamic business environment, organizations are increasingly relying on multi-cloud strategies to achieve flexibility, cost efficiency, and scalability. However, managing and optimizing IT costs while ensuring optimal performance across multiple cloud environments remains a complex challenge. This paper explores the concept of an Elastic Data Platform (EDP) as a solution for multi-cloud IT cost optimization and performance. By leveraging the inherent elasticity of cloud resources, this architecture provides the ability to scale data infrastructure efficiently while maintaining high performance levels. We discuss the key design principles of an EDP, including data distribution, workload optimization, auto-scaling, and cost analytics, and how these can be implemented across multiple cloud providers. Additionally, we analyze real-world use cases, benefits, and challenges associated with this architecture. This paper aims to provide insights into how businesses can optimize both costs and performance in a multi-cloud environment using an Elastic Data Platform.
Results show that selecting instances based on the second PI achieves at least 97% of the best achievable execution time in most cases, while highlighting cases where additional PIs improve selection accuracy.
J. R. Brunetta, J. F. Borin, E. Borin· Concurrency and Computation· 0 citations
The new EMC+ proposal is an OS‐driven elasticity manager for container‐based environments that continuously estimates idle core cycles left by regular (inelastic) applications, and reallocates idle cores to elastic ones, even during short time intervals, and has minimal impact on the performance and QoS of colocated inelastic applications.
J. C. Saez, Carlos Bilbao, Manuel Prieto-Matías· Concurrency and Computation· 0 citations
This paper explores performance tuning techniques for cloud data workflows, focusing on both batch processing and real-time streaming. It addresses key challenges in scalability, efficiency, and latency reduction to optimize data handling in cloud environments. Various strategies for resource allocation, load balancing, and data partitioning are analyzed to enhance throughput and minimize processing delays. The study evaluates the impact of tuning parameters on system performance through experimental results and case studies. Emphasis is placed on balancing cost-effectiveness with computational demands. Insights into adaptive optimization approaches for dynamic workloads are also provided. The findings demonstrate significant improvements in processing speed and resource utilization. This work contributes practical guidelines for optimizing cloud-based data pipelines in diverse operational contexts.
Subhasis Kundu· International Conference on...· 0 citations
The paper addresses the transformation of enterprise application infrastructure out of on-premise legacy resource setting into service-based cloud environments properly configured to scale horizontally, and presents experimental evaluations of the response time, throughput, service resiliency, and infrastructure utilization in both traditional and cloud-native deployments.
Kanya Mohammed, Naree Thongchai· International Journal of Mod...· 0 citations
The findings show that adaptive algorithm and hybrid algorithm is better in scalability, robustness and the overall performance of the system compared to the traditional centralized algorithms.
Farah Al-Farsi· International Journal of App...· 0 citations
A comprehensive review of Kubernetes scheduling strategies published between January 2023 and January 2026 is presented and a multi-dimensional taxonomy is established that categorizes scheduling approaches based on common objectives, modification methods, optimization methodologies, targeted workloads, evaluation methods, scheduling scopes, and performance metrics.
Mohammed Alhakimi, R. Latip· De Computis· 0 citations
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