The continuous growth of Internet traffic, especially video content, presents challenges for access providers (APs) who must upgrade their infrastructure to meet increasing demands. Ensuring a high-quality experience (QoE) for end-users and finding ways to monetize network resources are key concerns. Guaranteeing QoE is complex, as it depends not only on link capacity but also on competing traffic flows and shared network data plane buffers. To address these challenges, we proposeDiffPerf, an in-network, online, and dynamic allocation system.DiffPerfoperates at both macroscopic and microscopic levels. At the macroscopic level, it elastically allocates bandwidth to performance-centric service classes defined by APs to accommodate different performance requirements. At the microscopic level,DiffPerfemploys a lightweight data-driven algorithm to statistically differentiate and isolate traffic flows within each class, improving their performance. We implementedDiffPerfprototypes using SDN-based technology, one with OpenDaylight and OpenFlow hardware switches, and the other with programmable Intel Tofino switches. Our evaluation focused on on-demand video streaming. The results demonstrate thatDiffPerfoffers APs a range of allocation choices while ensuring strong performance isolation. Additionally,DiffPerfimproves fairness and enhances overall user-perceived QoE within each class. Notably,DiffPerfconserves bandwidth and delivers a QoE improvement approximately$4.6\times $higher than TCP BBR, the most popular congestion control mechanism on the Internet.

Continuing the current trend, Internet traffic is expected to grow significantly over the coming years, with video traffic consuming the biggest share. Despite numerous optimizations of the transport congestion control, and the switch butter sizing and management algorithms; however, the complex interaction among all of them still leads to uncertain user performance and thus degrades user-perceived quality, under various network and traffic conditions. The culprit is the difficulty to dynamically control the amount of bandwidth allocated to each of the competing flows under bottleneck due to the algorithms lack of visibility of butter content where the flows reside. We address this bandwidth allocation problem by proposing DiffPerf, an in-network system that relies on a lightweight learning algorithm to statistically differentiate and isolate user flows to help them achieve better performance in an online and dynamic manner. We built two SDN-based prototypes of DiffPerf; one on OpenDaylight with OpenFlow Brocade switch and the other with programmable data plane Barefoot Tofino switch. We evaluate it from an application perspective for ABR video streaming as it accounts for a majority of the Internet traffic. Our evaluations demonstrate the practicality and flexibility that DiffPerf assists users in achieving better fairness and improving overall user-perceived quality. On average DiffPerf yields a quality improvement of about $4.6\times$ and $1.2\times$ higher than TCP BBR and TCP CUBIC, respectively.

Data communication in cloud-based distributed stream data analytics often involves a collection of parallel and pipelined TCP flows. As the standard TCP congestion control mechanism and its variants are designed for achieving “fairness” among competing flows and are agnostic to the application layer contexts, the bandwidth allocation among a set of TCP flows traversing bottleneck links often leads to sub-optimal application-layer performance measures, e.g., stream processing throughput or average tuple complete latency. Motivated by this and enabled by the rapid development of the software-defined networking (SDN) techniques, in this paper, we re-investigate the design space of the bandwidth allocation problem and propose a cross-layer framework which utilizes the instantaneous information obtained from the application layer and provides on-the-fly and dynamic bandwidth adjustment algorithms for assisting the stream analytics applications achieving better performance during the runtime. We implement a prototype cross-layer bandwidth allocation framework based on a popular open-source distributed stream processing platform, Apache Storm, together with the OpenDaylight controller, and carry out extensive experiments with real-world analytical workloads on top of a local cluster consisting of ten workstations interconnected by a SDN-enabled fat-tree like testbed. The experiment results clearly validate the effectiveness and efficiency of our proposed framework and algorithms. Finally, we leverage the proposed cross-layer SDN framework and introduce an exemplary mechanism for bandwidth sharing and performance reasoning among multiple active applications and show a case of a point solution on how to approximate application-level fairness.

Data communication in cloud-based distributed stream data analytics often involves a collection of parallel and pipelined TCP flows. As the standard TCP congestion control mechanism is designed for achieving "fairness" among competing flows and is agnostic to the application layer contexts, the bandwidth allocation among a set of TCP flows traversing bottleneck links often leads to sub-optimal application-layer performance measures, e.g., stream processing throughput or average tuple complete latency. Motivated by this and enabled by the rapid development of the Software-Defined Networking (SDN) techniques, in this paper, we re-investigate the design space of the bandwidth allocation problem and propose a cross-layer framework which utilizes the additional information obtained from the application layer and provides on-the-fly and dynamic bandwidth adjustment algorithms for helping the stream analytics applications achieving better performance during the runtime. We implement a prototype cross-layer bandwidth allocation framework based on a popular open-source distributed stream processing platform, Apache Storm, together with the OpenDaylight controller, and carry out extensive experiments with real-world analytical workloads on top of a local cluster consisting of 10 workstations interconnected by a SDN-enabled switch. The experiment results clearly validate the effectiveness and efficiency of our proposed framework and algorithms.

We consider data intensive cloud-based stream analytics where data transmission through the underlying communication network is the cause of the performance bottleneck. Two key inter-related problems are investigated: task placement and bandwidth allocation. We seek to answer the following questions. How does task placement make impact on the application-level throughput? Does a careful bandwidth allocation among data flows traversing a bottleneck link results in better performance? In this paper, we address these questions by conducting measurement-driven analysis in a SDN-enabled computer cluster running stream processing applications on top of Apache Storm. The results reveal (i) how tasks are assigned to computing nodes make large difference in application level performance; (ii) under certain task placement, a proper bandwidth allocation helps further improve the performance as compared to the default TCP mechanism; and (iii) task placement and bandwidth allocation are collaboratively making effects in overall performance.

Collaborative in-network processing has shown to efficiently reduce the per-node processing and transmission requirements in wireless multimedia sensor networks. When the camera nodes are randomly distributed in an area of interest, it is very likely that their fields of view would overlap causing the corresponding visual information to be highly correlated. In applied wireless multimedia sensor networks, heterogeneous camera nodes with different sensing radii and angles of view are usually deployed, which has shown to enhance the overall network performance and lifetime. This paper introduces a new correlation model to exploit the correlation characteristics among heterogeneous camera nodes for wireless multimedia sensor networks. The proposed model, of which a closed-form analytical correlation function was derived, takes into consideration different sensing parameters of the camera nodes; such as the sensing radii and the angles of view. The simulation results demonstrated that the proposed model outperforms the state-of-the-art in terms of the accuracy of estimating the correlation characteristics, the information gain, and the distortion ratio.

The unique characteristics of the elliptic curve cryptography (ECC) such as the small key size, fast computations and bandwidth saving make its use attractive for multimedia encryption. In this study, the ECC is used to perform encryption along with multimedia compression, and two ECC‐based encryption algorithms are introduced and applied before and during compression. The first algorithm performs selective encryption on the transform coefficients during compression, whereas the second algorithm achieves perceptual encryption based on selective bit‐plane encryption before compression. The results of applying ECC to multimedia encryption are presented and analysed with respect to the requirements of multimedia encryption: the encryption efficiency, compression efficiency, codec compliance and security level. It is shown that ECC is highly efficient and does not affect the compression efficiency, and accordingly can meet the multimedia encryption requirements.