The increasing demands of high resolution and high quality videos aggravate the burden of limited cluster storage and restricted bandwidth resources. Hence, video de-duplication in storage and transmission is becoming an important feature for video cloud storage and Content Delivery Network (CDN) service providers. However, the current video de-duplication schemes mostly rely on the URL-based solution, which is unable to deal with non-cacheable content like video. The same video content with various resolutions and qualities may have completely different URL identification. In this paper, we propose a novel content-based video segmentation identification scheme that is invariant to the underlying codec and operational bit rates. It computes robust features from a triplet loss deep learning network that captures the invariance of the same content under different coding tools and strategy. In addition, a scalable hashing solution is developed based on Fisher Vector aggregation of the convolutional features from the Triplet loss network. Furthermore, we apply binary tree to obtain the triplets to improve the performance of the triplet-loss based VGG network. Our simulation results show significant improvements in terms of large scale video repository de-duplication compared with the state-of-the-art method.

The traditional IP network has its inherent limitations that could cause application runs in a non-optimized manner. The common methods to improve applications' performance requires a great effort from both network administrators and application designers. In this work, we propose a Software- Defined Network (SDN) approach in an Application-Aware Network (AAN) platform. We first present an architecture for our approach and then show how this architecture can be applied to two real-world applications: Hadoop MapReduce (M/R) framework and MPEG-DASH. Our approach provides both underlying network functions and application-level forwarding logic for MapReduce and video streaming. Based on our experiments, we observed that our AAN platform for Hadoop MapReduce job optimization offers a significant improvement compared to a static, traditional IP network environment by reducing job run time by 16% to 300% for various MapReduce benchmark jobs. As for MPEG-DASH based video streaming, we can increase user perceived video bitrate by 100%.

Future Smart Cities require new ways to manage services that benefit the end users. An important issue is how to connect homes in a community and create an alert management system in Smart Cities with coordination among different entities. In this paper, we present an architectural vision of a software defined home alert management system for Smart Cities. This alert management system would make the residents aware of any incidents in the neighborhood such as fire. In this work, we use the features of software defined networking to design a manageable and flexible smart home for a smart community to provide services such as smart alarm systems.

The MapReduce (M/R) framework used in Hadoop has become the de facto standard for big data analytics. However, the lack of network-awareness of the default M/R resource manager in a traditional IP network can cause unbalanced job scheduling and network bottlenecks; such factors can eventually lead to an increase in the Hadoop M/R job completion time. In this paper, we propose a software-defined network (SDN) approach in an application-aware network (AAN) platform that provides both underlying networks functions as well M/R particular forwarding logics. We measure the resources' usage for M/R workloads using the HiBench benchmark suite to identify the traffic pattern. We then demonstrate that by using our AAN-SDN framework, which uses an adaptive traffic engineering mechanism, the job completion time can be noticeably improved.

Despite tremendous software quality assurance efforts made by network vendors, chastising software bugs is a difficult problem especially, for the network systems in operation. Recent trends towards softwarization and opensourcing of network functions, protocols, controls, and applications tend to cause more software bug problems and pose many critical challenges to handle them. Although many traditional redundancy recovery mechanisms are adopted to the softwarized systems, software bugs cannot be resolved with them due to unexpected failure behavior. Furthermore, they are often bounded by common mode failure and common dependencies (CMFD). In this paper, we propose an online software bug detection, debugging, and isolation (BuDDI) middlebox architecture for software-defined network controllers. The BuDDI architecture consists of a shadow-controller based online debugging facility and a CMFD mitigation module in support of a seamless heterogeneous controller failover. Our proof-of-concept implementation of BuDDI is on the top of OpenVirtex by using Ryu and Pox controllers and verifies that the heterogeneous controller switchover does not cause any additional performance overhead.

Instantaneous low delay on-demand video streaming with a very low start up and channel switching delay is highly desirable for users. The dominant over-the-top (OTT) solutions like DASH, which is based on HTTP and/or WebSocket, suffer from a slow start of the underlying TCP transport while the typical coding structure of the DASH content introduces additional delays. In this work, we address these issues with a new low delay transport based on a WebRTC data channel along with a new content side packetization scheme based on a new QoE metric driven DASH sub-representation to facilitate a fast start, with an agile and fine granular rate adaptation. Simulations on both NS-3 and the GENI testbed demonstrate the effectiveness of this approach. This can serve as a basis for a further peer assisted low delay over-the-top (OTT) solution for an instantaneous live video streaming solution.

Hadoop has become the de facto standard for Big Data analytics, especially for workloads that use the MapReduce (M/R) framework. However, the lack of network awareness of the default MapReduce resource manager in Hadoop can cause unbalanced job scheduling, network bottleneck, and eventually increase the Hadoop run time if Hadoop nodes are clustered in several geographically distributed locations. In this paper, we present an application-aware network approach using software-defined networking (SDN) for distributed Hadoop clusters. We develop the SDN applications for this environment that consider network topology discovery, traffic monitoring, and flow rerouting in addition to loop avoidance mechanisms.

MPEG DASH is a client driven, pull based, adaptive streaming technology that allows for quality and bandwidth trade offs at streaming times. This is especially useful for mobile and Over The Top (OTT) video streaming applications, as throughput variations are inevitable. However, when multiple DASH clients are sharing a common wireless bottleneck with under-provisioned resources, e.g., multiple devices sharing the same home cable connection, or multiple UEs sharing the radio resource served by the same base station, simple adaptation within the same traffic session is not enough and may lead to sub-optimal solutions. In this work, we introduce new Quality of Experience (QoE) metrics for DASH sub-representations, and propose a marginal utility maximization-based resource pricing scheme to coordinate multiple video traffic sharing the bottleneck resource. The solution is based on the DASH SAND (Server And Network assisted DASH) messaging framework. Simulation results demonstrate the QoE multiplexing gains from this solution, and the pricing control scheme is adopted in SAND messages.

A campus network provides a number of services to its stakeholders. Due to the complexity of having many services, it is often difficult to pinpoint an issue quickly. The currently available tools for monitoring a campus network are often designed for a specific piece of the network. On the other hand, systems administrators running a campus network with data centers and supporting a number of internal and external applications face several challenges with the current set of tools for monitoring and management. To fill this void, we propose the I-CaN-MaMa (Integrated Campus Network Monitoring and Management) framework. Our approach takes an integrated view that was developed in consultations with issues faced by systems administrators. We present the key components in this framework and through a brief study, we show the utility of our approach.