Mehdi Bahrami

The Role of Cloud Computing Architecture in Big Data

In this data-driven society, we are collecting a massive amount of data from people, actions, sensors, algorithms and the web; handling “Big Data” has become a major challenge. A question still exists regarding when data may be called big data. How large is big data? What is the correlation between big data and business intelligence? What is the optimal solution for storing, editing, retrieving, analyzing, maintaining, and recovering big data? How can cloud computing help in handling big data issues? What is the role of a cloud architecture in handling big data? How important is big data in business intelligence? This chapter attempts to answer these questions. First, we review a definition of big data. Second, we describe the important challenges of storing, analyzing, maintaining, recovering and retrieving a big data. Third, we address the role of Cloud Computing Architecture as a solution for these important issues that deal with big data. We also discuss the definition and major features of cloud computing systems. Then we explain how cloud computing can provide a solution for big data with cloud services and open-source cloud software tools for handling big data issues. Finally, we explain the role of cloud architecture in big data, the role of major cloud service layers in big data, and the role of cloud computing systems in handling big data in business intelligence models.

A Light-Weight Permutation Based Method for Data Privacy in Mobile Cloud Computing

Cloud computing paradigm provides virtual IT infrastructures with a set of resources that are shared with multi-tenant users. Data Privacy is one of the major challenges when users outsource their data to a cloud computing system. Privacy can be violated by the cloud vendor, vendors authorized users, other cloud users, unauthorized users, or external malicious entities. Encryption is one of the solutions to protect and maintain privacy of cloud-stored data. However, encryption methods are complex and expensive for mobile devices. In this paper, we propose a new light-weight method for mobile clients to store data on one or multiple clouds by using pseudo-random permutation based on chaos systems. The proposed method can be used in the client mobile devices to store data in the cloud(s) without using cloud computing resources for encryption to maintain users privacy. We consider JPEG image format as a case study to present and evaluate the proposed method. Our experimental results show that the proposed method achieve superior performance compared to over encryption methods, such as AES and encryption on JPEG encoders while protecting the mobile user data privacy. We review major security attack scenarios against the proposed method that shows the level of security.

A cloud-based web crawler architecture

Web crawlers work on the behalf of applications or services to find interesting and related information on the web. For example, search engines use web crawlers to index the Internet. Web crawlers have several challenges, such as complexity between links and highly intensive computation requirements when a web crawler wants to retrieve complex connected links. Another issue is the storage of a massive amount of indexed links or downloaded unstructured data, such as binary files, videos or images. As the volume of information on the Internet increases rapidly and requests may search data in a variety of formats including unstructured data, no cloud-based architecture exists in the literatures for web crawlers that could effectively address both highly intensive computing and storage issues. The cloud computing paradigm provides support for elastic resources and unstructured data, and provides pay-peruse features that allow individual businesses to run their own web crawlers for crawling the Internet or a limited web hosts. In this paper, we propose a cloud-based web crawler architecture that uses cloud computing features and the MapReduce programming technique. The proposed web crawler allows us to crawl the web by using distributed agents and each agent stores its own finding on a Cloud Azure Table (NoSQL database). The proposed web crawler also could store unstructured and massive amount of data on Azure Blob storage. We analyze the performance and scalability of the proposed web crawler and we describe the advantages of the proposed web crawler over traditional distributed web crawlers.

Cloud Computing for Emerging Mobile Cloud Apps

The tutorial will begin with an explanation of the concepts behind cloud computing systems, cloud software architecture, the need for mobile cloud computing as an aspect of the app industry to deal with new mobile app design, network apps, app designing tools, and the motivation for migrating apps to cloud computing systems. The tutorial will review facts, goals and common architectures of mobile cloud computing systems, as well as introduce general mobile cloud services for app developers and marketers. This tutorial will highlight some of the major challenges and costs, and the role of mobile cloud computing architecture in the field of app design, as well as how the app-design industry has an opportunity to migrate to cloud computing systems with low investment. The tutorial will review privacy and security issues. It will describe major mobile cloud vendor services to illustrate how mobile cloud vendors can improve mobile app businesses. We will consider major cloud vendors, such as Microsoft Windows Azure, Amazon AWS and Google Cloud Platform. Finally, the tutorial will survey some of the cuttingedge practices in the field, and present some opportunities for future development.

Demonstration of a Functional Chaining System Enabled by Named-Data Networking

In this work, we present a functional chaining system enabled by Named-Data Networking (NDN). By using the proposed naming semantics and on-the-fly processing procedures, a functional chaining request, which consists of the name of raw data and an ordered set of functions, can be executed dynamically and seamlessly. Moreover, we demonstrate a functional chaining forwarding strategy and a whole chain security scheme, which can improve the system performance in terms of forwarding efficiency and security, respectively.

Compliance-Aware Provisioning of Containers on Cloud

Deploying applications in containers has several advantages, such as rapid development, portability across different machines, and simplified maintenance. In a cloud computing environment, container scheduling algorithms coordinate with different aspects of physical systems, such as memory allocation for tasks of different users. The scheduled containers on a host may process sensitive data. For instance, containers may process healthcare information. In that case, diverse cloud environments with different components and subsystems may lead to a potential personal health information leakage and violation of data privacy. In this paper, we introduce a novel compliance-aware analysis model for provisioning containers in the cloud, that provides a HIPAA compliance model. The proposed method dynamically analyzes different requirements of HIPAA complaint containers (HIPAA parameters) and their associated risk values. Based on the risk optimization of the compliance parameters for data security and data privacy of the containers, our proposed method determines scheduling of containers that offer the lowest risk to healthcare data and to the compliance posture of the container. The model describes the resources that are associated with highlevel risks and provides real-time resource recommendation for a container scheduler to decrease the risk of HIPAA compliance violation.

API Learning: Applying Machine Learning to Manage the Rise of API Economy

Application Programming Interface (API) exposes data and functions of a software application to third-party users. In digital business, API economy is one of the key component for determining the value of provided services. With the rise in number of publicly available APIs, understanding each API endpoint manually is not only labor intensive but it is also an error prone task for software engineers. Due to the complexity of understanding the sheer number of APIs, it is difficult for software developers to find the best possible API combinations (i.e. API Mashups). In this demonstration, we introduce API Learning platform which employs machine-learning based technologies to efficiently search APIs, validate APIs, and generate API mashups. These technologies enable a machine to automatically generate machine-readable API specification from API documentations, understand variety of APIs, validate extracted information through automated API validation, and finally recommend API mashups for a specific purpose. As of now, API Learning platform collected over 14,000 API documentations and generates a machine readable format for REST APIs with an accuracy of 84%. The proposed demo prototype shows how it enables users to quickly find relevant APIs, automatically verify API availability, and get the best possible API mashup recommendations.

ICN-FC: An Information-Centric Networking based framework for efficient functional chaining

In this paper, we present ICN-FC, which is an Information-Centric Networking (ICN) based framework for efficient functional chaining (FC). The key enabling techniques for ICN-FC includes naming semantics, Interest & Data processing and an efficient FC forwarding strategy. By using the proposed solutions, a functional chaining request, which consists of the name of raw data and an ordered set of functions, can be executed seamlessly, dynamically and flexibly in the network. In addition, the novel FC forwarding strategy can be used to improve the forwarding efficiency for functional chaining requests. The overall feasibility and efficiency of the proposed solutions are validated by using both experimental prototype and network simulation. The results show that the proposed solutions outperform previous works such as named function networking to support functional chaining applications.

Risk-Based Packet Routing for Privacy and Compliance-Preserving SDN

Software Defined Networking (SDN) is increasingly being used in data centers as well as enterprise networks. In an environment that has strict compliance requirements, such as HIPAA compliance, a critical role for an SDN controller is to route all data packets while considering data privacy preservation and compliance-preservation. In this paper, we address this problem by proposing a routing protocol for SDN which is an efficient risk-based swarm routing protocol. The programmable capability of controllers is exploited in order to minimize privacy and compliance risks in data transmission. The proposed routing protocol is based on the Ant Colony Optimization technique and machine learning, while the data for learning is obtained from OVSDB and the OpenvSwitch Database management protocol. We collect a history of packet transfers for training purposes and learn from the training data to efficiently and intelligently route sensitive data packets while it preserves the target compliance. This routing is obtained by intelligent eviction of rules that are downloaded to the switches. We have implemented the proposed schemes based on an RYU controller.

Secure function chaining enabled by Information-Centric Networking

Information-Centric Networking (ICN) has been proposed as a future Internet architecture where data-centric security is one of its most distinguishing features when compared to the channel-based security in IP networks. In ICN, per packet digital signature offers a built-in authentication capability down to the packet level and a fine-grained trust model, yet it is insufficient to address the emerging security challenges in service function chaining, e.g., data may be processed by fake intermediate function nodes. In this paper, we investigate a whole-chain security approach, namely Secure Function Chaining (SFC), which ensures authenticity and data integrity when delivering data content through a chain of function nodes in ICN. In particular, we propose a novel packet signature structure, which consists of a content stack and a signature stack. At the data source and each of intermediate function nodes, we prepend a fixed-length hashed content to the content stack and prepend an unmodified signature (for the hashed content) to the signature stack. The proposed solution enables a Consumer to verify not only the final delivered data content, but also each and every entity in the whole function chaining process - from a Producer to the last function node that delivered the final content. We conduct a comprehensive set of experiments to evaluate the proposed function chaining process and its trust model. The results show the superior performance of the whole-chain security approach over the existing NDN security solution. The results show that the secure proposed scheme is an efficient scheme over the original hop-by-hop NDN signature scheme for a function chaining process and it can be used in substitute of the native scheme.