# A Multi-Usable Cloud Service Platform: A Case Study on Improved Development Face and Efficiency
## 2 Related Work
### Multi-Usable Cloud Service Platforms for Big Data Collection/Analysis and IoT  
As the amount of data that various actors want to process increases, traditional data and analytics are beginning to meet their limits; an emerging solution for large-scale data collection and analytics is cloud analytics. [PERSON] and [PERSON] [1] demonstrate a conceptual architecture of service oriented decision-support, which includes data warehouses, online analytic processing, operational systems and end-user components. This is something that many other actors pursue as well, and three main cloud service offerings are distinguishable: Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) [2]. However, there are quite a few more specialized offerings emerging as well, such as Security-as-a-Service and Identity-as-a-Service, complementing the three main ones. Further, [PERSON] et al. [3] categorize a number of different cloud service platforms for IoT as: global cloud, peer-to-peer and local cloud. In addition, often \"fog\" or hybrids of global and internal clouds are positioned in between the global and local cloud paradigms. Regarding the **global cloud**, for instance, some of the largest and most used cloud service/computing platforms, briefly described below, are: Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform and IBM Bluemix--which are:  
* AWS, a subsidiary of Amazon.com, offers a suite of cloud computing services that make up an on-demand computing platform. AWS has more than 70 services, spanning a wide range, including computing, storage, networking, database, analytics, application services, deployment, management, mobile, developer tools and tools for the IoT [4]. * Microsoft Azure is a cloud computing service created by Microsoft for building, deploying, and managing applications and services through a global network of Microsoft-managed data centers. It provides SaaS, PaaS and IaaS, and supports many different programming languages, tools and frameworks, including both Microsoft-specific and third-party software and systems. The offering includes: data analysis, network, storage, databases, IoT, enterprise-integration, development and monitoring [5]. * The Google Cloud Platform is part of a suite of enterprise services from Google Cloud and provides a set of modular cloud-based services with a host of development tools. Their product portfolio includes: computing, storage and databases, networking, big data, machine learning, management tools, developer tools, and identity and security [6]. * IBM Bluemix is a PaaS that supports several services and programming languages including Java, Node.js, Go, PHP, Swift, Python, Ruby Sinatra and Ruby on Rails and can be extended to support other languages such as Scala. It also includes offers such as infrastructure computation, storage, network, mobile, Watson, data analytics and IoT [7]. Thus, there are quite a few multi-usable cloud service platforms. However, many of these do not have an effective and cost-efficient way to integrate and later operate many IoT-devices from a variety of manufacturers on a long-term basis. Concerning **peer-to-peer**, [PERSON] et al. [3] outline, for instance, the platforms and frameworks, etc. from IPSO, Thread, ThingSquare, IzoT, SEP 2.0, AllJoyn and IoTivity, which have approached IoT application development from a device level and support a high level of peer-to-peer operation, a functional solution for home automation as well as device management. Currently, the **local cloud** is targeted mainly by the Arrowhead Framework [8], which addresses many challenges related to IoT-based automation, and is unique in its support for integration of applications between secure localized clouds. The approach is that IoT devices are abstracted as services in order to enable interoperability between almost all IoT devices.