Mohammad A. Salahuddin

Software-Defined Networking for RSU Clouds in Support of the Internet of Vehicles

We propose a novel roadside unit (RSU) cloud, a vehicular cloud, as the operational backbone of the vehicle grid in the Internet of Vehicles (IoV). The architecture of the proposed RSU cloud consists of traditional and specialized RSUs employing software-defined networking (SDN) to dynamically instantiate, replicate, and/or migrate services. We leverage the deep programmability of SDN to dynamically reconfigure the services hosted in the network and their data forwarding information to efficiently serve the underlying demand from the vehicle grid. We then present a detailed reconfiguration overhead analysis to reduce reconfigurations, which are costly for service providers. We use the reconfiguration cost analysis to design and formulate an integer linear programming (ILP) problem to model our novel RSU cloud resource management (CRM). We begin by solving for the Pareto optimal frontier (POF) of nondominated solutions, such that each solution is a configuration that minimizes either the number of service instances or the RSU cloud infrastructure delay, for a given average demand. Then, we design an efficient heuristic to minimize the reconfiguration costs. A fundamental contribution of our heuristic approach is the use of reinforcement learning to select configurations that minimize reconfiguration costs in the network over the long term. We perform reconfiguration cost analysis and compare the results of our CRM formulation and heuristic. We also show the reduction in reconfiguration costs when using reinforcement learning in comparison to a myopic approach. We show significant improvement in the reconfigurations costs and infrastructure delay when compared to purist service installations.

Smart Cities: A Survey on Data Management, Security, and Enabling Technologies

Integrating the various embedded devices and systems in our environment enables an Internet of Things (IoT) for a smart city. The IoT will generate tremendous amount of data that can be leveraged for safety, efficiency, and infotainment applications and services for city residents. The management of this voluminous data through its lifecycle is fundamental to the realization of smart cities. Therefore, in contrast to existing surveys on smart cities we provide a data-centric perspective, describing the fundamental data management techniques employed to ensure consistency, interoperability, granularity, and reusability of the data generated by the underlying IoT for smart cities. Essentially, the data lifecycle in a smart city is dependent on tightly coupled data management with cross-cutting layers of data security and privacy, and supporting infrastructure. Therefore, we further identify techniques employed for data security and privacy, and discuss the networking and computing technologies that enable smart cities. We highlight the achievements in realizing various aspects of smart cities, present the lessons learned, and identify limitations and research challenges.

RSU cloud and its resource management in support of enhanced vehicular applications

We propose Roadside Unit (RSU) Clouds as a novel way to offer non-safety application with QoS for VANETs. The architecture of RSU Clouds is delineated, and consists of traditional RSUs and specialized micro-datacenters and virtual machines (VMs) using Software Defined Networking (SDN). SDN offers the flexibility to migrate or replicate virtual services and reconfigure the data forwarding rules dynamically. However, frequent changes to service hosts and data flows not only result in degradation of services, but are also costly for service providers. In this paper, we use Mininet to analyze and formally quantify the reconfiguration overhead. Our unique RSU Cloud Resource Management (CRM) model jointly minimizes reconfiguration overhead, cost of service deployment and infrastructure routing delay. To the best of our knowledge, we are the first to utilize this approach. We compare the performance of purist approach to our Integer Linear Programming (ILP) model and our innovative heuristic for the CRM technique and discuss the results. We will show the benefits of a holistic approach in Cloud Resource Management with SDN.

A Survey on Replica Server Placement Algorithms for Content Delivery Networks

Content delivery networks (CDNs) have gained immense popularity over the years. Replica server placement is a key design issue in CDNs. It entails placing replica servers at meticulous locations, such that cost is minimized and quality of service of end-users is satisfied. Many replica server placement models have been proposed in the literature of traditional CDN. As the CDN architecture is evolving through the adoption of emerging paradigms, such as, cloud computing and network functions virtualization, new algorithms are being proposed. In this paper, we present a comprehensive survey of replica server placement algorithms in traditional and emerging paradigm-based CDNs. We categorize the algorithms and provide a summary of their characteristics. Besides, we identify requirements for an efficient replica server placement algorithm and perform a comparison in the light of the requirements. Finally, we discuss potential avenues for further research in replica server placement in CDNs.

Social Network Analysis Inspired Content Placement with QoS in Cloud Based Content Delivery Networks

Content Placement (CP) problem in Cloud based Content Delivery Networks (CCDNs) leverage resource elasticity to build cost effective CDNs that guarantee QoS. In this paper, we present our novel CP model, which optimally places content on surrogates in the cloud, to achieve (a) minimum cost of leasing storage and bandwidth resources for data coming into and going out of the cloud zones and regions, (b) guarantee Service Level Agreement (SLA), and (c) minimize degree of QoS violations. The CP problem is NP Hard, hence we design a unique push based heuristic, called Weighted Social Network Analysis (W SNA) for CCDN providers. W-SNA is based on Betweeness Centrality (BC) from SNA and prioritizes surrogates based on their relationship to the other vertices in the network graph. To achieve our unique objectives, we further prioritize surrogates based on weights derived from storage cost and content requests. We compare our heuristic to current state of the art Greedy Site (GS) and purely Social Network Analysis (SNA) heuristics, which are relevant to our work. We show that W-SNA outperforms GS and SNA in minimizing cost and QoS. Moreover, W-SNA guarantees SLA but also minimizes the degree of QoS violations. To the best of our knowledge, this is the first model and heuristic of its kind, which is timely and gives a fundamental pre allocation scheme for future online and dynamic resource provision for CCDNs.

Softwarization of Internet of Things Infrastructure for Secure and Smart Healthcare

The authors propose an agile, softwarized infrastructure for the flexible, cost-effective, secure, and privacy-preserving deployment of Internet of Things systems for smart healthcare applications and services.

Reinforcement learning for resource provisioning in the vehicular cloud

This article presents a concise view of vehicular clouds that incorporates various vehicular cloud models that have been proposed to date. Essentially, they all extend the traditional cloud and its utility computing functionalities across the entities in the vehicular ad hoc network. These entities include fixed roadside units, onboard units embedded in the vehicle, and personal smart devices of drivers and passengers. Cumulatively, these entities yield abundant processing, storage, sensing, and communication resources. However, vehicular clouds require novel resource provisioning techniques that can address the intrinsic challenges of dynamic demands for the resources and stringent QoS requirements. In this article, we show the benefits of reinforcement-learning-based techniques for resource provisioning in the vehicular cloud. The learning techniques can perceive long-term benefits and are ideal for minimizing the overhead of resource provisioning for vehicular clouds.

Exploiting Context Severity to Achieve Opportunistic Service Differentiation in Vehicular Ad hoc Networks

Wireless Access in Vehicular Environment (WAVE) dictates primitives for intelligent transportation system (ITS) applications and services. The medium access control (MAC) layer in WAVE facilitates service differentiation. This offers quality of service (QoS) by prioritizing traffic through different access categories (ACs), which are based on application-requested priorities. However, it is oblivious to network load, to delay requirements for ITS safety applications, and to the “severity” of vehicles. In this paper, we propose a novel opportunistic service differentiation (OSD) scheme as an enhancement to WAVE. We define a fuzzy inference system (FIS) to deduce a context severity metric (CSM) that relates the driving behavior of a vehicle to its environment. Our OSD traffic distribution heuristic prioritizes vehicle traffic, with respect to CSM, and accounts for network load and link layer bounds. Furthermore, the OSD scheme guarantees that ACs do not exceed maximum allowable delay for ITS applications. Our methodology entails defining and designing a linear programming (LP) model for verification and validation of the OSD scheme. We perform a comparative study of OSD-enhanced WAVE and classical WAVE analytically and in a vehicular ad hoc network (VANET) simulator. We show that both simulation and analytical results substantiate our claim of improvement in performance of OSD-enhanced WAVE over classical WAVE.

A Cloud Platform-as-a-Service for multimedia conferencing service provisioning

Multimedia conferencing is the real-time exchange of multimedia content between multiple parties. It is the basis of a wide range of applications (e.g., multimedia multiplayer game). Cloud-based provisioning of the conferencing services on which these applications rely will bring benefits, such as easy service provisioning and elastic scalability. However, it remains a big challenge. This paper proposes a PaaS for conferencing service provisioning. The proposed PaaS is based on a business model from the state of the art. It relies on conferencing IaaSs that, instead of VMs, offer conferencing substrates (e.g., dial-in signaling, video mixer and audio mixer). The PaaS enables composition of new conferences from substrates on the fly. This has been prototyped in this paper and, in order to evaluate it, a conferencing IaaS is also implemented. Performance measurements are also made.

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.