The performance of randomized network coding can suffer significantly when malicious nodes corrupt the content of the exchanged blocks. Previous work have introduced error correcting codes by generalizing some well known bounds in coding theory. Such codes are based on introducing redundancy in space domain. Other approaches require the use of homomorphic hashing functions, which are computationally expensive.In this paper, we present a novel and computationally efficient security algorithm, referred to as Null Keys, to detect and contain malicious attacks based on the subspace properties of random linear network coding. The participating nodes verify the integrity of a block by checking if it belongs to the subspace spanned by the source blocks. This is possible when every node has a vector orthogonal to all the combinations of the source blocks. These vectors, referred to as null keys, belong to the null space of the source blocks and go through a random combination when distributed by the source. Unlike previous security approaches, our Null Keys algorithm allows nodes to rapidly detect corrupted blocks without changing the code or imposing redundancy on the exchanged data. We analytically evaluate the pollution produced by jamming attacks, and demonstrate the effectiveness of Null Keys by varying the strength of the malicious nodes. We also show, through extensive simulations, that the Null Keys approach is more effective than cooperative security using homomorphic hashing when it comes to limiting the pollution spread.
Network coding has been leveraged with cooperative diversity to improve performance in single channel wireless networks. However, it is not clear how network coding based cooperative diversity can be exploited effectively in multi-channel networks where overhearing is not readily available. Moreover, the question of how to practically realize the promising gains available, including multiuser diversity, cooperative diversity and network coding in multi-channel networks, also remains unexplored. This work represents the first attempt to unravel these two questions. In this paper, we propose XOR-CD, a novel XOR-assisted cooperative diversity scheme in OFDMA wireless networks. It can greatly improve the relay efficiency by over 100% mostly, thus uplifting the throughput performance by over 30% compared to conventional cooperative diversity scheme. In addition, we formulate a unifying optimization framework that jointly considers relay assignment, relay strategy selection, channel assignment and power allocation to reap different forms of gains. We design efficient polynomial time algorithms to solve the NP-hard problem with provably the best approximation factor, and verify their effectiveness using realistic simulations.
In large-scale peer-to-peer (P2P) live streaming systems with a limited supply of server bandwidth, increasing the amount of upload bandwidth supplied by peers becomes critically important to the "well being" of streaming sessions in live channels. Intuitively, two types of peers are preferred to be kept up in a live session: peers that contribute a higher percentage of their upload capacities, and peers that are stable for a long period of time. The fundamental challenge is to identify, and satisfy the needs of, these types of "superior" peers in a live session, and to achieve this goal with minimum disruption to the traditional pull-based protocols that real-world live streaming protocols use. In this paper, we conduct a comprehensive and in-depth statistical analysis based on more than 130 GB worth of runtime traces from hundreds of streaming channels in a largescale real-world live streaming system, UUSee (among the top three commercial systems in popularity in mainland China). Our objective is to discover critical factors that may influence the longevity and bandwidth contribution ratio of peers, using survival analysis techniques such as the Cox proportional hazards model and the Mantel-Haenszel test. Once these influential factors are found, they can be used to form a superiority index to distill superior peers from the general peer population. The index can be used in any way to favor superior peers, and we simulate the use of a simple ranking mechanism in a natural selection algorithm to show the effectiveness of the index, based on a replay of real-world traces from UUSee.
While it is a well known result that network coding achieves optimal flow rates in multicast sessions, its potential for practical use has remained to be a question, due to its high computational complexity. Our previous work has attempted to design a hardware-accelerated and multi-threaded implementation of network coding to fully utilize multi-core CPUs, as well as SSE2 and AltiVec SIMD vector instructions on x86 and PowerPC processors. This paper represents another step forward, and presents the first attempt in the literature to maximize the performance of network coding by taking advantage of not only multi-core CPUs, but also potentially hundreds of computing cores in commodity off-the-shelf Graphics Processing Units (GPU). With GPU computing gaining momentum as a result of increased hardware capabilities and improved programmability, our work shows how the GPU, with a design involving thousands of lightweight threads, can boost network coding performance significantly. Many-core GPUs can be deployed as an attractive alternative and complementary solution to multi-core servers, by offering a better price/performance advantage. In fact, multicore CPUs and many-core GPUs can be deployed and used to perform network coding simultaneously, potentially useful in media streaming servers where hundreds of peers are served concurrently by these dedicated servers. In this paper, we present Nuclei, the design and implementation of GPU-based network coding. With Nuclei, only one mainstream NVidia 8800 GT GPU outperforms an 8-core Intel Xeon server in most test cases. A combined CPU-GPU encoding scenario achieves coding rates of up to 116 MB/second for a variety of coding settings, which is sufficient to saturate a Gigabit Ethernet interface.
Abstract-On-demand and live multimedia streaming applications (such as Internet TV) are well known to utilize a significant amount of bandwidth from media streaming servers, especially as the number of participating peers in the streaming session scales up. To scale to higher bit rates of media streams and larger numbers of participating peers, overlay tree or mesh topologies are typically constructed, such that peers utilize their available upload capacities to alleviate the excessive bandwidth demands on stream servers. Previous works rely on random selections of upstream peers, without optimizing the topologies towards maximized utilization of peer upload bandwidth, and as a result, minimized bandwidth costs on streaming servers.We propose Outreach, a distributed algorithm to construct overlay topologies among participating peers in streaming sessions. The design objective of Outreach is to optimize the quality of overlay topologies towards scalability, with respect to the number of participating peers in the session. To be scalable, Outreach seeks to maximize the utilization of available upload bandwidth on each participating peer, and consequently minimize the total bandwidth costs on streaming servers. With analysis, we show that Outreach constructs topologies such that peers can fully utilize their upload capacities, and present a practical distributed algorithm. With simulation-based comparison studies, we show that Outreach effectively achieves its goals in a highchurn peer-to-peer network with an assortment of peer uplink capacities and link delays.Index Terms-Peer-to-peer multimedia streaming, overlay topology construction, server bandwidth optimization.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.