Brazilian Army, Third BEC, Picos 64606-000, Brazil.
Coordination of Informatics, Federal Institute of Education, Science and Technology of Sergipe, Lagarto 49400-000, Brazil.
Sensors (Basel). 2022 Feb 5;22(3):1221. doi: 10.3390/s22031221.
Cloud computing has been widely adopted over the years by practitioners and companies with a variety of requirements. With a strong economic appeal, cloud computing makes possible the idea of computing as a utility, in which computing resources can be consumed and paid for with the same convenience as electricity. One of the main characteristics of cloud as a service is elasticity supported by auto-scaling capabilities. The auto-scaling cloud mechanism allows adjusting resources to meet multiple demands dynamically. The elasticity service is best represented in critical web trading and transaction systems that must satisfy a certain service level agreement (SLA), such as maximum response time limits for different types of inbound requests. Nevertheless, existing cloud infrastructures maintained by different cloud enterprises often offer different cloud service costs for equivalent SLAs upon several factors. The factors might be contract types, VM types, auto-scaling configuration parameters, and incoming workload demand. Identifying a combination of parameters that results in SLA compliance directly in the system is often sophisticated, while the manual analysis is prone to errors due to the huge number of possibilities. This paper proposes the modeling of auto-scaling mechanisms in a typical cloud infrastructure using a stochastic Petri net (SPN) and the employment of a well-established adaptive search metaheuristic (GRASP) to discover critical trade-offs between performance and cost in cloud services.The proposed SPN models enable cloud designers to estimate the metrics of cloud services in accordance with each required SLA such as the best configuration, cost, system response time, and throughput.The auto-scaling SPN model was extensively validated with 95% confidence against a real test-bed scenario with 18.000 samples. A case-study of cloud services was used to investigate the viability of this method and to evaluate the adoptability of the proposed auto-scaling model in practice. On the other hand, the proposed optimization algorithm enables the identification of economic system configuration and parameterization to satisfy required SLA and budget constraints. The adoption of the metaheuristic GRASP approach and the modeling of auto-scaling mechanisms in this work can help search for the optimized-quality solution and operational management for cloud services in practice.
云计算在过去几年中已被各行业从业者和公司广泛采用，这些公司有各种不同的需求。云计算具有强大的经济吸引力，它使计算成为一种实用工具的想法成为可能，就像使用电力一样方便地使用和支付计算资源。云服务的主要特点之一是具有弹性，这得益于自动扩展功能。自动扩展云机制允许根据需要动态调整资源。弹性服务在必须满足一定服务级别协议（SLA）的关键网络交易和交易系统中得到了最好的体现，例如，对于不同类型的入站请求，最大响应时间限制。然而，不同云企业维护的现有云基础设施通常会根据几个因素为等效 SLA 提供不同的云服务成本。这些因素可能是合同类型、VM 类型、自动扩展配置参数和传入工作负载需求。在系统中直接确定导致 SLA 合规的参数组合通常很复杂，而由于可能性的数量巨大，手动分析容易出错。本文提出了使用随机 Petri 网 (SPN) 对典型云基础架构中的自动扩展机制进行建模，并采用经过良好验证的自适应搜索元启发式算法（GRASP）来发现云服务中性能和成本之间的关键权衡。所提出的 SPN 模型使云设计人员能够根据每个所需的 SLA（例如最佳配置、成本、系统响应时间和吞吐量）估算云服务的指标。自动扩展 SPN 模型经过广泛验证，置信度为 95%，使用 18000 个样本与真实测试床场景进行比较。使用云服务案例研究来调查这种方法的可行性，并评估所提出的自动扩展模型在实践中的适用性。另一方面，所提出的优化算法使能够识别经济系统的配置和参数化，以满足所需的 SLA 和预算约束。在这项工作中采用元启发式 GRASP 方法和自动扩展机制建模可以帮助在实践中寻找优化质量的解决方案和云服务的运营管理。
