Data-Driven Service Provisioning
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When a customer submits a request to use a mobile network, system initializes and manages the customer’s services, such as establishing control channels (Huang et al., 2016) and making update scheme for service provisioning due to user mobility (Huang and Guo, 2017).
To activate a low-earth-orbit satellite-based service for secure big data storage, the telecom company has to design and construct the infrastructure for customers in advance (Huang et al., 2018).
Data centers or edge nodes launch a Service Function Chaining enabled machine to meet the customer’s subscription, including the desired software and hardware resources, and the access authority in the agreed period (Huang et al., 2017b).
Big data promises to reshape the knowledge, society, and economy. The technologies that are used to generate, collect, process, compute, and communicate big data have made significant progresses in the past few years. As an overwhelming volume of data is generated with ever-growing speed every day from various sources and applications, such as cloud services, Internet of Things (IoT), social network services, and other smart terminals, it becomes significant to design, deploy, and provision services that are more wise to enable the effective acquisition, storage, transformation, management, and utilization of such huge amount of data.
To alleviate the violations of service-level objectives caused by provisioning delays, inefficient reconfigurations, and inaccurate demand predictions, over-provisioning apparently increases operational costs and resource-utilization inefficiencies, particularly in large-scale systems. Via a data-driven service provisioning framework named AidOps, Lugones et al. (2017) argue that the orchestration system enables a higher service availability and lower over-provisioning costs.
As mentioned by Chesbrough and Spohrer (2006), the growing knowledge of information and communication technology (ICT) has created opportunities to inspire the evolution of internet on the service relationships that can yield new value. Specifically, ICT has the following characteristics that can improve effectiveness and efficiency and enable innovativeness in the manners of (1) commoditizing noncore competency such as outsourcing, (2) improving collaboration in business processes, (3) controlling the risks of information security violation, (4) coordinating service systems such as open innovation platforms, (5) improving customer-oriented service quality, etc. Especially, in the big data era, enormous value can be extracted from the service-oriented big data.
To fully explore the value of service-generated big data, developing efficient technologies that can fast process large amount of data is an open issue. On the other hand, easy access of big data and data analysis results are critical to both service providers and service users (Zheng et al., 2013).
In the following, some typical data-driven platforms/architectures for service provisioning are reviewed.
Efficient models and solutions for querying and processing big data.
Big data-based service modeling, delivery, deployment, and evolution.
Analytic services for big data.
Knowledge discovery over massive datasets.
Social sensing and social network services.
Reasoning over uncertain data and unreliable services.
Big data based service ranking and recommendation.
- Huang H, Guo S (2017) Service provisioning update scheme for mobile application users in a cloudlet network. In: IEEE international conference on communications (ICC). IEEE, pp 1–6Google Scholar
- Lugones D, Aroca JA, Jin Y, Sala A, Hilt V (2017) Aidops: a data-driven provisioning of high-availability services in cloud. In: Proceedings of the 2017 symposium on cloud computing. ACM, pp 466–478Google Scholar
- Xu Z, Liang W, Xu W, Jia M, Guo S (2015) Capacitated cloudlet placements in wireless metropolitan area networks. In: IEEE 40th conference on local computer networks (LCN). IEEE, pp 570–578Google Scholar
- Zheng Z, Zhu J, Lyu MR (2013) Service-generated big data and big data-as-a-service: an overview. In: 2013 IEEE international congress on big data (BigData Congress). IEEE, pp 403–410Google Scholar