Service Level Agreement Monitoring as a Service: An Independent Monitoring Service for Service Level Agreements in Clouds.

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Big Data Pub Date : 2023-10-01 Epub Date: 2022-01-24 DOI:10.1089/big.2021.0274
Afzal Badshah, Ateeqa Jalal, Umar Farooq, Ghani-Ur Rehman, Shahab S Band, Celestine Iwendi
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引用次数: 6

Abstract

The cloud network is rapidly growing due to a massive increase in interconnected devices and the emergence of different technologies such as the Internet of things, fog computing, and artificial intelligence. In response, cloud computing needs reliable dealings among the service providers, brokers, and consumers. The existing cloud monitoring frameworks such as Amazon Cloud Watch, Paraleap Azure Watch, and Rack Space Cloud Kick work under the control of service providers. They work fine; however, this may create dissatisfaction among customers over Service Level Agreement (SLA) violations. Customers' dissatisfaction may drastically reduce the businesses of service providers. To cope with the earlier mentioned issue and get in line with cloud philosophy, Monitoring as a Service (MaaS), completely independent in nature, is needed for observing and regulating the cloud businesses. However, the existing MaaS frameworks do not address the comprehensive SLA for customer satisfaction and penalties management. This article proposes a reliable framework for monitoring the provider's services by adopting third-party monitoring services with clearcut SLA and penalties management. Since this framework monitors SLA as a cloud monitoring service, it is named as SLA-MaaS. On violations, it penalizes those who are found in breach of terms and condition enlisted in SLA. Simulation results confirmed that the proposed framework adequately satisfies the customers (as well as service providers). This helps in developing a trustworthy relationship among cloud partners and increases customer attention and retention.

服务级别协议监控即服务:云中服务级别协议的独立监控服务。
由于互联设备的大量增加以及物联网、雾计算和人工智能等不同技术的出现,云网络正在迅速发展。作为回应,云计算需要服务提供商、经纪人和消费者之间的可靠交易。现有的云监控框架,如Amazon cloud Watch、Paraleap Azure Watch和Rack Space cloud Kick,在服务提供商的控制下工作。它们工作良好;然而,这可能会引起客户对违反服务水平协议(SLA)的不满。客户的不满可能会大大减少服务提供商的业务。为了解决前面提到的问题并符合云的理念,监控即服务(MaaS)在本质上是完全独立的,需要用于观察和监管云业务。然而,现有的MaaS框架没有解决客户满意度和处罚管理的全面SLA问题。本文提出了一个可靠的框架,通过采用具有clearcut SLA和惩罚管理的第三方监控服务来监控提供商的服务。由于该框架将SLA作为云监控服务进行监控,因此将其命名为SLA-MaaS。关于违规行为,它惩罚那些被发现违反苏丹解放军招募的条款和条件的人。仿真结果证实,所提出的框架充分满足了客户(以及服务提供商)的要求。这有助于在云合作伙伴之间建立值得信赖的关系,并提高客户的关注度和忠诚度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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