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A Deployable Data As a Service Architecture for Enterprises

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A Deployable Data As a Service Architecture for Enterprises A Deployable Data as a Service Architecture for Enterprises Adrian´ Toth´ 1 and Mouzhi Ge2 1Faculty of Informatics, Masaryk University, Brno, Czech Republic 2Deggendorf Institute of Technology, Deggendorf, Germany Keywords: Service Computing, Cloud Computing, as a Service, Data as a Service. Abstract: Nowadays, data have been considered as one of the valuable assets in enterprises. Although the cloud com- puting and service-oriented architecture are capable of accommodating the data asset, they are more focused on software or platforms rather than the data per se. Thus, data management in cloud computing is usually not prioritized and not well organized. In recent years, Data as a Service (DaaS) has been emerged as a critical concept for enterprises. It benefits from a variety of aspects such as data agility and data quality manage- ment. However, it is still unknown for enterprises why and how to develop and deploy a DaaS architecture. This paper is therefore to design a deployable DaaS architecture that is based on the as-a-Service principles and especially tackles data management as a service. To validate the architecture, we have implemented the proposed DaaS with a real-world deployment. 1 INTRODUCTION such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), it is unclear that how the as a Service features can be Big Data has received increasing attention in recent applied to data, and compared to other cloud service years, as organizations and cities are dealing with models, what the specific components for DaaS are. tremendous amounts of data with high complexity Thus, DaaS can on the one hand benefit from the gen- and velocity (Ge et al., 2018). These data are fast eral as a Service features, on the other hand, data can moving and can originate from various sources, such be managed as a service to potentially enhance the as social networks, unstructured data from different resilience and reusability for data-driven applications devices or raw feeds from sensors (Ge and Dohnal, (Rao and Nayak, 2019). 2018). Thus, cloud environment has been used to ac- This paper therefore proposes a DaaS architecture commodate the Big Data and significantly facilitate that can guide users to deploy the data centre on the the development of service-oriented computing for cloud. It can enhance the data accessibility through data management (Park et al., 2021). While service- different channels, and eliminate the geographical and oriented computing not only provides a better perfor- scalability limitations (Rajesh et al., 2012). We will mance of service offerings, perception, and quality of provide a blueprint of how as a Service features can service delivery (Mishra and Kumar, 2018), it also be used for DaaS. The architecture is mainly focused offers a foundation for the development, execution, on specific components to construct the DaaS. The composition, and integration of business processes proposed architecture is further validated by the phys- that are distributed across the cloud computing net- ical implementations with real-world deployment. work and accessible via standard interfaces and pro- tocols (Serhani and Dssouli, 2010). Cloud storage and service-oriented computing al- lows enterprises to focus more on effective cross- 2 AS A SERVICE platform application data exchange (Khan et al., 2019). However, when an enterprise intends to mi- The term as a Service has been widely associated with grate or build their data center with service-oriented different service models while it sometimes can be computing, there is lacking of guidelines on how to misleading or confusing to understand what actually design and build a Data as a Service (DaaS). Beyond the as a service is (Duan et al., 2015). With respect to understanding the well-known cloud service models the specific functionality, the as a Service on the cloud 278 Tóth, A. and Ge, M. A Deployable Data as a Service Architecture for Enterprises. DOI: 10.5220/0010470702780285 In Proceedings of the 6th International Conference on Internet of Things, Big Data and Security (IoTBDS 2021), pages 278-285 ISBN: 978-989-758-504-3 Copyright c 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved A Deployable Data as a Service Architecture for Enterprises Table 1: Summary of features in as a service model. IaaS PaaS SaaS DaaS Monitoring computing resources software systems software applications data transactions Metering load of resources system performance application usage transaction attributes Security user’s responsibility combined responsibility provider’s responsibility provider’s responsibility of user and provider to protect application to protect data assets software Tenancy Partitioning - system instances application instances data pools Availability dependent on data cen- dependent on infrastruc- dependent on infrastruc- dependent on infrastruc- ter(s) ture ture or platform ture or platform Scalability on-deman resources allo- on-deman resources allo- on-deman resources allo- on-deman resources allo- cation/deallocation cation/deallocation cation/deallocation cation/deallocation Fault Tolerance task resubmission, stor- system replicas, load application replicas, or- data protection, data reg- age backup, etc. balancing, etc. chestration, etc. ulation, etc. Distribution - enabled enabled enabled and normal services may have the same functionality within the service represents a potential security but can be organized in different forms. For example, risk that might lead to exploitation of a security the difference between software and software as a ser- vulnerability (Mthunzi et al., 2020). The data se- vice does not depend on the functionality but on how curity is critical and it has been moved to the pri- the functionality is provided. Implementing the as a mary security stipulation. as a Service introduced Service method requires to meet a set of requirements dozens of challenging security problems that re- that guarantee the functionality provision (Moorthy sulted in a continuous security incident monitor- and Pabitha, 2019). Given that as a Service is con- ing, reporting, and resolving. sidered as a subset of cloud service model, there are • Tenancy Partitioning (Multitenancy) - It en- features that are inherited, meanwhile there are some sures that every tenant (user) is isolated from each specific features for data management. By reviewing other and prevents violations between them while the common features from IaaS, PaaS and Saas. We each user is using a single instance of the appli- have derived the following features for as a Service. cation (Aljahdali et al., 2014). Each tenant has • Monitoring - it covers the operations and actions its own operational environment and he is able to connected to the provided service that are later to work in the space excluding the ability to use oth- be observed, checked, and processed for meter- ers operational environment. For instance, the ap- ing. Monitoring results are assets, logs, and audit, plication database can be configured to serve just which grant repudiation and accountability char- for a single tenant, which would prevent storing acteristics to the service itself (Bouasker et al., multiple users’ data in the same database (Liu, 2020). These monitoring data are considered as 2010). There may be an exception when the ten- a trusted source of recorded events, operations, ants share spaces consciously with respect to their and transactions that are substantially significant needs such as collaboration. Tenancy partition- for further calculations such as billing for the used ing requires to implement access control and user resources. isolation. From the aspect of SaaS application, • Metering - it regulates the resource usage analy- this feature has been considered as highly impor- sis. This analysis takes into account the resource tant (Aleem et al., 2019). restrictions concerning the rules in the service • Availability - It can be seen as a warranty from level agreement (Narayan et al., 2017). The ser- a cloud perspective. It is usually expected that vice provider may deliver different service qual- the as a Service derivations meet criteria of high ities to the customers e.g. lower price for less availability and reliability (Li et al., 2020). As as resources, higher price for unlimited operations. a Service is adopted on the basis of cloud comput- The customers should be familiar with the terms ing, the availability is guaranteed partially by the of service and these limitations. Metering can be cloud, which means that the cloud hosted service observed as a special type of monitoring that is itself should be also resilient to errors and fault- required for business and marketing purposes. tolerant. • Security - It is a fundamental feature in the cloud • Scalability - This is the ability to increase or de- computing. Each interaction from outside or crease IT resources regarding the actual demand 279 IoTBDS 2021 - 6th International Conference on Internet of Things, Big Data and Security (Bellavista et al., 2017). Scalability can be dif- 3 DATA AS A SERVICE ferentiated into two types - horizontal and vertical ARCHITECTURE scalability. In vertical scaling we change the com- puting resources (such as CPU, storage, RAM, An architecture provides foundation components that etc.) on an existing machine while in horizon- reveals the implementation requirements. Thus, the tal scaling we change the pool of resources by DaaS architecture can be considered as
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