The demand for rich multimedia services over mobile networks has been soaring at a tremendous pace over recent years. However, due to the centralized architecture of current cellular networks, the wireless link capacity as well as the bandwidth of the radio access networks and the backhaul network cannot practically cope with the explosive growth in mobile traffic. Recently, we have observed the emergence of promising mobile content caching and delivery techniques, by which popular contents are cached in the intermediate servers (or middleboxes, gateways, or routers) so that demands from users for the same content can be accommodated easily without duplicate transmissions from remote servers; hence, redundant traffic can be significantly eliminated. In this article, we first study techniques related to caching in current mobile networks, and discuss potential techniques for caching in 5G mobile networks, including evolved packet core network caching and radio access network caching. A novel edge caching scheme based on the concept of content-centric networking or information-centric networking is proposed. Using trace-driven simulations, we evaluate the performance of the proposed scheme and validate the various advantages of the utilization of caching content in 5G mobile networks. Furthermore, we conclude the article by exploring new relevant opportunities and challenges.

Cache in the air: exploiting content caching and delivery techniques for 5G systems

Sending data to the cloud for analysis was a prominent trend during the past decades, driving cloud computing as a dominant computing paradigm. However, the dramatically increasing number of devices and data traffic in the Internet-of-Things (IoT) era are posing significant burdens on the capacity-limited Internet and uncontrollable service delay. It becomes difficult to meet the delay-sensitive and context-aware service requirements of IoT applications by using cloud computing alone. Facing these challenges, computing paradigms are shifting from the centralized cloud computing to distributed edge computing. Several new computing paradigms, including Transparent Computing, Mobile Edge Computing, Fog Computing, and Cloudlet, have emerged to leverage the distributed resources at network edge to provide timely and context-aware services. By integrating end devices, edge servers, and cloud, they form a hierarchical IoT architecture, i.e., End-Edge-Cloud orchestrated architecture to improve the performance of IoT systems. This article presents a comprehensive survey of these emerging computing paradigms from the perspective of end-edge-cloud orchestration. Specifically, we first introduce and compare the architectures and characteristics of different computing paradigms. Then, a comprehensive survey is presented to discuss state-of-the-art research in terms of computation offloading, caching, security, and privacy. Finally, some potential research directions are envisioned for fostering continuous research efforts.

A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms: Transparent Computing, Mobile Edge Computing, Fog Computing, and Cloudlet

Recently, a series of innovative information-centric networking (ICN) architectures have been designed to better address the shift from host-centric end-to-end communication to requester-driven content retrieval. With the explosive increase of mobile data traffic, the mobility issue in ICN is a growing concern and a number of approaches have been proposed to deal with the mobility problem in ICN. Despite the potential advantages of ICN in mobile wireless environments, several significant research challenges remain to be addressed before its widespread deployment, including consistent routing, local cached content discovery, energy efficiency, privacy, security and trust, and practical deployment. In this paper, we present a brief survey on some of the works that have already been done to achieve mobile ICN, and discuss some research issues and challenges. We identify several important aspects of mobile ICN: overview, mobility enabling technologies, information-centric wireless mobile networks, and research challenges.

A Survey of Mobile Information-Centric Networking: Research Issues and Challenges

The landscape toward 5G wireless communication is currently unclear, and, despite the efforts of academia and industry in evolving traditional cellular networks, the enabling technology for 5G is still obscure. This paper puts forward a network paradigm toward next-generation cellular networks, targeting to satisfy the explosive demand for mobile data while minimizing energy expenditures. The paradigm builds on two principles; namely caching and multicast . On one hand, caching policies disperse popular content files at the wireless edge, e.g., pico-cells and femto-cells, hence shortening the distance between content and requester. On other hand, due to the broadcast nature of wireless medium, requests for identical files occurring at nearby times are aggregated and served through a common multicast stream. To better exploit the available cache space, caching policies are optimized based on multicast transmissions. We show that the multicast-aware caching problem is NP-hard and develop solutions with performance guarantees using randomized-rounding techniques. Trace-driven numerical results show that in the presence of massive demand for delay tolerant content, combining caching and multicast can indeed reduce energy costs. The gains over existing caching schemes are 19% when users tolerate delay of three minutes, increasing further with the steepness of content access pattern.

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Exploiting Caching and Multicast for 5G Wireless Networks

In one embodiment, a method for mobile network function virtualization (MNFV) includes creating an evolved packet core (EPC) cluster and associating a sub-network with the EPC cluster. The method also includes booting a virtual machine (VM) and attaching the VM to the EPC.

System and method for mobile network function virtualization

With the rapid increase of traffic on the web, content caching reduces user-perceived latency as well as the transmission of redundant traffic on the network. In this study, we analyze the gains of HTTP content caching at the location of SGW in an LTE Wireless network. High cache hit ratio can be achieved if the proxy server caches only those contents that are guaranteed of significant revisits. In this paper, we identify such contents for optimum proxy server performance. We compare the cacheability gains for different content types such as image, video, text etc, and also for popular websites. Our analysis shows that amongst all the contents, `image' type have the highest revisit rate, which means caching them is beneficial. Amongst the popular websites compared, cacheable contents from `Facebook' have the highest probability of revisits. We extend the analysis by varying the interval of caching and studying its effect on the cacheability. Based on these results, we provide guidelines for configuring the proxy server for high cacheability benefits.

Cacheability analysis of HTTP traffic in an operational LTE network

A capability is provided for virtualizing control plane functions of a wireless core packet network (WCPN). A forwarding element (FE) is configured to provide a set of data plane functions of the WCPN. A virtual control element (VCE) is configured to provide a set of control plane functions of the WCPN. The VCE includes a set of virtual machines (VMs) where at least one of the VMs is configured to detect a condition associated with a tunnel, for a wireless terminal (WT), between the FE and a wireless access device (WAD) configured to serve the WT, and propagate, toward the FE, a message comprising a tunnel command configured to instruct the FE to at least one of install, remove, or modify a tunnel rule associated with the tunnel between the FE and the WAD. The tunnel enables the WT to access a packet data network (PDN).

Virtualization of control plane functions of a wireless core packet network

Voice interfaces are fast becoming an important human-machine interfaces, and Wake Word Engines (WWEs) are a critical part of modern voice interfaces. There are recent advancements in applying Deep Learning (DL) or Deep Neural Network architectures for WWE construction. Similar to other applications of DL however, achieving good accuracy strongly depends on training using the right type of dataset – a task that traditionally requires significant time and human effort. In this paper, we present novel techniques for curating WWE datasets that significantly minimizes the need for data collection from humans. More specifically, we investigated two techniques: (1) Using “Found Data”: we have created automated data curation pipelines for locating and processing data from public sources (e.g., YouTube), and (2) Using Synthetic Data: we explored the use of Synthesized Speech (using Text to Speech and Voice Conversion) to synthetically generate WWE datasets. Using our techniques, we are able to train WWEs that demonstrate a level of performance that is on-par with the WWEs trained with data from expensive human collection (e.g., Mechanical Turk). For example, in our experiments using wake words ‘Computer’ and ‘Shannon’, for a given False Alarm rate of 1 per hour, WWEs trained with our novel methods exhibit a False Reject Rate, averaged across four different test environments, of 0.9% and 1.6% respectively compared to the 1.0% and 0.8% for the baseline trained using data from human collection. Cycle time savings of more than an order of magnitude is possible by utilizing these methods.

Eliminating Data Collection Bottleneck for Wake Word Engine Training Using Found and Synthetic Data

The ability to process large amounts of data efficiently is a must in modern Big Data, Data Science and Machine Learning (ML). Easy and succinct workflow expression with highly efficient scaling is key. Existing systems such as Apache Spark and Beam were created for this type of work but have steep learning curve and tremendous operating complexity. Having a simpler tool with less overhead that is well-matched to the typical workflow definition and execution requirements of data science/ML projects will be beneficial.We propose a new framework PB&J that enables succinct definition and data-parallel scale-out execution of workflows with ease. PB&J is easy to learn as it builds on the native Unix shell, with the addition of just a few operators. PB&J supports Maximal Parallelism with its ability to do true pipelining, and fault recovery with Minimal Redo. It is well-matched to the typical data science/ML processing requirements by leveraging the existing command line executables and Python modules. We illustrate the features and strengths of PB&J with real-life Deep Learning data processing workflows. We compare it to existing frameworks such as Apache Spark, Apache Beam, Swift/T and Apache Airflow in terms of ease of authoring, efficiency, scalability and fault recovery.

~PB&J~ - Easy Automation of Data Science/Machine Learning Workflows

One embodiment includes a method for determining a target location for a metro cell base station by calculating, via at least one monitoring device, at least one of a number of user equipments within a range of the at least one monitoring device and a radio frequency (RF) level of at least one macro cell base station. The method further includes determining, via the at least one monitoring device, the target location for the metro cell base station based on at least one of the calculated number of user equipments within the range and the calculated RF level of the at least one macro cell base station.

Buvaneswari Ramanan

Papers

Cacheability analysis of HTTP traffic in an operational LTE network

Virtualization of control plane functions of a wireless core packet network

Eliminating Data Collection Bottleneck for Wake Word Engine Training Using Found and Synthetic Data

~PB&J~ - Easy Automation of Data Science/Machine Learning Workflows

Lte user presence detection for small cell placement