PrEstoCloud tries to overcome different challenges associated with real-time Big Data processing systems such as addressing time-sensitive changes in the execution environment and performing the adaptation at run-time, which has been recently recognised by industry. This project creates a novel computing and management solution for efficient deployment and execution of data-intensive applications orchestrated upon edge computing frameworks. The PrEstoCloud solution provides capabilities which allow such systems to benefit from important advantages including (i) development and deployment, (ii) integration and automation, (iii) data protection and privacy, (iv) cost optimisation, and (v) data management.
To this end, the PrEstoCloud project aims to prototype the following technologies which emerge to meet requirements for real-time Big Data environments within edge computing scenarios:
Technologies to improve the execution of data-intensive applications: The PrEstoCloud project provides a new solution based upon edge computing frameworks for different purposes: (i) to securely and reliably collect data from different types of sensors; (ii) to efficiently perform real-time Big Data processing and predictive analytics on IoT events and streams; (iii) to seamlessly upgrade enterprise applications and practices with IoT data; and (iv) to appropriately standardise the integration of sensors/objects/devices with enterprise applications.
Technologies to enhance the development productivity of data-intensive applications: The PrEstoCloud project offers a new way of aiding data-intensive application providers to develop and customise their real-time Big Data processing systems based on edge computing frameworks. In the next decade, more than two billion sensors/objects/devices will get connected to the Internet. Therefore, this huge market obviously requires efficient solutions for deploying and customising such business-oriented real-time data-intensive systems.
Technologies to monitor data-intensive applications deployed upon edge computing frameworks: The performance of data-intensive applications deployed upon edge computing frameworks varies depending on runtime conditions such as the workload density, availability and reliability of virtualised infrastructures, network connection quality between end-users and servers, and so on. Therefore, tracking dynamic changes of execution environments provided by the PrEstoCloud solution on the fly is necessary to identify any deterioration of system health [1].
Technologies to facilitate location-aware and context-driven adaptation recommender systems: As an important advantage of modern cloud-edge solutions such as PrEstoCloud, tracking end-users’ information for example their location, mobility and operational environment can be useful in order to offer fully customised services. In this way, various constraints for proper behaviour of data-intensive applications (e.g. response time, security constraints, etc.) can be expressed during design-time, and further refined while running time, verified in real-time. This fact may appropriately support end-users’ requirements and desires especially for data-intensive applications.
