Nelson Mimura Gonzalez

A Quantitative Analysis of Current Security Concerns and Solutions for Cloud Computing

The development of cloud computing services is speeding up the rate in which the organizations outsource their computational services or sell their idle computational resources. Even though migrating to the cloud remains a tempting trend from a financial perspective, there are several other aspects that must be taken into account by companies before they decide to do so. One of the most important aspect refers to security: while some cloud computing security issues are inherited from the solutions adopted to create such services, many new security questions that are particular to these solutions also arise, including those related to how the services are organized and which kind of service/data can be placed in the cloud. Aiming to give a better understanding of this complex scenario, in this article we identify and classify the main security concerns and solutions in cloud computing, and propose a taxonomy of security in cloud computing, giving an overview of the current status of security in this emerging technology.

A Framework for Authentication and Authorization Credentials in Cloud Computing

Security is a key concern when adopting cloud technology. Cloud solutions include not only issues inherited from related technologies, such as virtualization and distributed computing, but also new concerns associated to complexity of the cloud ecosystem, composed by the cloud entities and their interactions. One of the concerns is related to authentication and authorization in the cloud in order to provide robust mechanisms to identify entities and establish their permissions and roles in the cloud, controlling resource usage and promoting accounting and isolation. This paper identifies the state of the art in terms of credential management focusing on the cloud ecosystem. It proposes a credential classification and a framework for studying and developing solutions in this context, unifying concepts related to cloud deployment models, service types, entities and lifecycle controls.

Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures

Cloud computing evolved from the concept of utility computing, which is defined as the provision of computational and storage resources as a metered service. Another key characteristic of cloud computing is multitenancy, which enables resource and cost sharing among a large pool of users. Characteristics such as multitenancy and elasticity perfectly fit the requirements of modern data-intensive research and scientific endeavors. In parallel, as science relies on the analysis of very large data sets, data management and processing must be performed in a scalable and automated way. Workflows have emerged as a way to formalize and structure data analysis, thus becoming an increasingly popular paradigm for scientists to handle complex scientific processes. One of the key enablers of this conjunction of cloud computing and scientific workflows is resource management. However, several issues related to data-intensive loads, complex infrastructures such as hybrid and multicloud environments to support large-scale execution of workflows, performance fluctuations, and reliability, pose as challenges to truly position clouds as viable high-performance infrastructures for scientific computing. This paper presents a survey on cloud resource management that provides an extensive study of the field. A taxonomy is proposed to analyze the selected works and the analysis ultimately leads to the definition of gaps and future challenges to be addressed by research and development.

Fog computing: Data analytics and cloud distributed processing on the network edges

The term fog computing was coined in 2012. However, the concept of pushing data and application logic to the network edges is not a novelty. Similar proposals were observed with edge computing, from the early 2000s, and cloudlets, from 2009. In fact, the cloudlet concept is a subset of edge computing applied to mobile networks and the fog concept is a subset of edge computing applied to Internet of Things (IoT). This paper demystifies these concepts and provides a comprehensive survey of references from academia and industry. It analyzes the terminology and dimensions of performance, security, and governance, based on a taxonomy proposed and presented in the paper. In addition we provide a thorough analysis of related topics, identifying the main research areas correlated to edge computing. Finally, we draw conclusions regarding the state of the art and the future of edge computing.

Inclusion of security requirements in SLA lifecycle management for cloud computing

Service Level Agreement (SLA) is an essential tool for managing cloud computing services. The support of the security requirements through SLA is fundamental to achieve the full potential of the cloud computing paradigm. In this paper we present how security requirements are addressed in a cloud computing SLA. Furthermore, a unified SLA lifecycle for cloud computing services is proposed. The relationship between security requirements and the lifecycle proposed is analyzed. Through the analysis, the current cloud computing security requirements scenario for the SLA context is identified in addition to its research opportunities.

The Use of Distributed Processing and Cloud Computing in Agricultural Decision-Making Support Systems

One of the main challenges in agriculture is to sustainably meet the demand for food while preserving natural resources for future productions. Information Technology can assist producers to make better decisions by providing them with data and tools that enhance decision-making process, consequently allowing better management of the natural resources. Cloud-computing platforms and the extraction of data available on public weather related data sets allow the development of web applications that can assist producers with their investing and planning decisions. This paper describes the Big Weather solution, an agricultural decision-making support system that utilizes a cloud-computing platform, distributed processing technologies, and a big data framework. This paper also presents Big Weather architecture and an example of metric calculations (average temperature and humidity) and discusses the performance of the solution when tested in different virtual machine scenario configurations. The novelty is the transparency of the framework, which allows farmers to make better decisions based on data available on the cloud.

A framework to orchestrate security SLA lifecycle in cloud computing

Security issues of cloud computing environments are considered a major challenge for its full adoption. A Service Level Agreement (SLA) corroborates the shared management vision provided by the cloud computing paradigm, which can assist with related security issues. The necessity to address security requirements in cloud computing SLAs is considered important for both providers and consumers, along with the tools and mechanisms necessary to deal with these requirements. These issues are current research challenges; therefore, this paper aims at proposing a framework to orchestrate the management of cloud services based on security requirements defined by the SLA in an automated manner during its entire lifecycle. In addition, mechanisms to support the phases of the SLA lifecycle are proposed as a part of the framework. also the preliminary validation of the proposed framework. Finally, it is presented the integration of the framework with a cloud solution and in what manner the SLA lifecycle is supported by the framework.

Managing the Lifecycle of Security SLA Requirements in Cloud Computing

One of the major barriers for full adoption of cloud computing is the security issue. As the cloud computing paradigm presents a shared management vision, it is important that security requirements are addressed inside the Service Level Agreements (SLAs) established between cloud providers and consumers, along with the tools and mechanisms necessary to deal with these requirements. This work aims at proposing a framework to orchestrate the management of cloud services and security mechanisms based on the security requirements defined by a SLA, in an automated manner, throughout their lifecycles. In addition, the integration of the framework with a cloud computing solution is presented, in order to demonstrate and validate the framework support throughout SLAs lifecycle phases.

Cloud Distributed Processing Using Trade Wind

This paper presents the distributed processing feature for Trade Wind, a cloud deployment and management solution. The main objective of this research is to evaluate performance and feasibility of this feature for big data applications. A prototype has been created and tested in the calculation of monthly average temperature and humidity based on data from a public data set. Preliminary results show great improvement in terms of time reduction, speedup, and efficiency.

Distributed Processing from Large Scale Sensor Network Using Hadoop

Big data refers to large amounts of data, normally in unstructured forms, such as text, sensor data, audio, video, log files, and more. When collecting from a large number of nodes, sensor networks can produce large data sets. Climate and sensor network data are applied to assist the decision making process in agricultural production. In this paper we present the main aspects of a prototype to test Hadoop framework applied to process climate related data sets in a cloud computing infrastructure.