Semantics of Data Mining Services in Cloud Computing

M. Parra-Royon holds a "Excelencia" scholarship from the Regional Government of Andaluc a (Spain). This work was supported by the Research Projects P12-TIC-2958 and TIN2016-81113-R (Ministry of Economy, Industry and Competitiveness - Government of Spain).In recent years with the rise of Cloud Computing (CC), many companies providing services in the cloud, are empowering a new series of services to their catalogue, such as data mining (DM) and data processing (DP), taking advantage of the vast computing resources available to them. Different service definition proposals have been put forward to address the problem of describing services in CC in a comprehensive way. Bearing in mind that each provider has its own definition of the logic of its services, and specifically of DM services, it should be pointed out that the possibility of describing services in a flexible way between providers is fundamental in order to maintain the usability and portability of this type of CC services. The use of semantic technologies based on the proposal offered by Linked Data (LD) for the definition of services, allows the design and modelling of DM services, achieving a high degree of interoperability. In this article a schema for the definition of DM services on CC is presented considering all key aspects of service in CC, such as prices, interfaces, Software Level Agreement (SLA), instances or DM work ow, among others. The new schema is based on LD, and it reuses other schemata obtaining a better and more complete definition of the services. In order to validate the completeness of the scheme, a series of DM services have been created where a set of algorithms such as Random Forest (RF) or KMeans are modeled as services. In addition, a dataset has been generated including the definition of the services of several actual CC DM providers, conforming the effectiveness of the schema.P12-TIC-2958 and TIN2016-81113-R (Ministry of Economy, Industry and Competitiveness - Government of Spain

Cloud/fog computing resource management and pricing for blockchain networks

The mining process in blockchain requires solving a proof-of-work puzzle, which is resource expensive to implement in mobile devices due to the high computing power and energy needed. In this paper, we, for the first time, consider edge computing as an enabler for mobile blockchain. In particular, we study edge computing resource management and pricing to support mobile blockchain applications in which the mining process of miners can be offloaded to an edge computing service provider. We formulate a two-stage Stackelberg game to jointly maximize the profit of the edge computing service provider and the individual utilities of the miners. In the first stage, the service provider sets the price of edge computing nodes. In the second stage, the miners decide on the service demand to purchase based on the observed prices. We apply the backward induction to analyze the sub-game perfect equilibrium in each stage for both uniform and discriminatory pricing schemes. For the uniform pricing where the same price is applied to all miners, the existence and uniqueness of Stackelberg equilibrium are validated by identifying the best response strategies of the miners. For the discriminatory pricing where the different prices are applied to different miners, the Stackelberg equilibrium is proved to exist and be unique by capitalizing on the Variational Inequality theory. Further, the real experimental results are employed to justify our proposed model.Comment: 16 pages, double-column version, accepted by IEEE Internet of Things Journa

Security and Privacy Issues of Big Data

This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by Big Data applications. One of them is privacy. It is a pertinent aspect to be addressed because users share more and more personal data and content through their devices and computers to social networks and public clouds. So, a secure framework to social networks is a very hot topic research. This last topic is addressed in one of the two sections of the current chapter with case studies. In addition, the traditional mechanisms to support security such as firewalls and demilitarized zones are not suitable to be applied in computing systems to support Big Data. SDN is an emergent management solution that could become a convenient mechanism to implement security in Big Data systems, as we show through a second case study at the end of the chapter. This also discusses current relevant work and identifies open issues.Comment: In book Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, IGI Global, 201