A data integrity verification service for cloud storage based on building blocks

Cloud storage is a popular solution for organizations and users to store data in ubiquitous and cost-effective manner. However, violations of confidentiality and integrity are still issues associated to this technology. In this context, there is a need for tools that enable organizations/users to verify the integrity of their information stored in cloud services. In this paper, we present the design and implementation of an efficient service based on provable data possession cryptographic model, which enables organizations to verify, on-demand, the data integrity without retrieving files from the cloud. The storage and cryptographic components have been developed in the form of building blocks, which are deployed on the user-side using the Manager/Worker pattern that favors exploiting parallelism when executing data possession challenges. An experimental evaluation in a private cloud revealed the efficacy of launching integrity verification challenges to cloud storage services and the feasibility of applying containerized task parallel scheme that significantly improves the performance of the data possession proof service in real-world scenarios in comparison with the implementation of the original possession data proof scheme.This work has been partially funded by GRANT Fondo Sectorial Mexican Space Agency-CONACYT Num. 262891 and by EU under the COST programme Action IC1305, Network for Sustainable Ultrascale Computing (NESUS)

Kulla, a container-centric construction model for building infrastructure-agnostic distributed and parallel applications

This paper presents the design, development, and implementation of Kulla, a virtual container-centric construction model that mixes loosely coupled structures with a parallel programming model for building infrastructure-agnostic distributed and parallel applications. In Kulla, applications, dependencies and environment settings, are mapped with construction units called Kulla-Blocks. A parallel programming model enables developers to couple those interoperable structures for creating constructive structures named Kulla-Bricks. In these structures, continuous dataflow and parallel patterns can be created without modifying the code of applications. Methods such as Divide&Containerize (data parallelism), Pipe&Blocks (streaming), and Manager/Block (task parallelism) were developed to create Kulla-Bricks. Recursive combinations of Kulla instances can be grouped in deployment structures called Kulla-Boxes, which are encapsulated into VCs to create infrastructure-agnostic parallel and/or distributed applications. Deployment strategies were created for Kulla-Boxes to improve the IT resource profitability. To show the feasibility and flexibility of this model, solutions combining real-world applications were implemented by using Kulla instances to compose parallel and/or distributed system deployed on different IT infrastructures. An experimental evaluation based on use cases solving satellite and medical image processing problems revealed the efficiency of Kulla model in comparison with some traditional state-of-the-art solutions.This work has been partially supported by the EU project "ASPIDE: Exascale Programing Models for Extreme Data Processing" under grant 801091 and the project "CABAHLA-CM: Convergencia Big data-Hpc: de los sensores a las Aplicaciones" S2018/TCS-4423 from Madrid Regional Government

A gearbox model for processing large volumes of data by using pipeline systems encapsulated into virtual containers

Software pipelines enable organizations to chain applications for adding value to contents (e.g., confidentially, reliability, and integrity) before either sharing them with partners or sending them to the cloud. However, the pipeline components add overhead when processing large volumes of data, which can become critical in real-world scenarios. This paper presents a gearbox model for processing large volumes of data by using pipeline systems encapsulated into virtual containers. In this model, the gears represent applications, whereas gearboxes represent software pipelines. This model was implemented as a collaborative system that automatically performs Gear up (by using parallel patterns) and/or Gear down (by using in-memory storage) until all gears produce uniform data processing velocities. This model reduces delays and bottlenecks produced by the heterogeneous performance of applications included in software pipelines. The new container tool has been designed to encapsulate both the collaborative system and the software pipelines into a virtual container and deploy it on IT infrastructures. We conducted case studies to evaluate the performance of when processing medical images and PDF repositories. The incorporation of a capsule to a cloud storage service for pre-processing medical imagery was also studied. The experimental evaluation revealed the feasibility of applying the gearbox model to the deployment of software pipelines in real-world scenarios as it can significantly improve the end-user service experience when pre-processing large-scale data in comparison with state-of-the-art solutions such as Sacbe and Parsl.This work has been partially supported by the “Spanish Ministerio de Economia y Competitividad ” under the project grant TIN2016-79637-P “Towards Unification of HPC and Big Data paradigms”

From the edge to the cloud: A continuous delivery and preparation model for processing big IoT data

This research was partially supported by "Fondo Sectorial de Investigación para la Educación", SEP-CONACyT Mexico, under grantnumbers 281565 and 285276, and by Madrid Regional Government (Spain) under the grant ”Convergencia Big data-Hpc: de los sensores a las Aplicaciones. (CABAHLA-CM)”, ref: S2018/TCS-4423