Efficient multicast algorithms for scalable video coding in software-defined networking

Sheu, Jang‐Ping; Chang, Che‐Wei; Chang, Yeh-Cheng

doi:10.1109/pimrc.2015.7343642

Cited by 14 publications

(3 citation statements)

References 13 publications

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“…Solutions have been proposed to leverage SDN techniques to provide efficient and well-managed network-multicast services. Many of these solutions [30][31][32] aim to find optimal trees. These SDN-based multicast advances focused on the control-plane problem of finding the best multicast topologies but not on the data-plane aspects.…”

Section: Recent Related Workmentioning

confidence: 99%

High-performance reliable network-multicast over a trial deployment

et al. 2022

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A continuing trend in many scientific disciplines is the growth in the volume of data collected by scientific instruments and the desire to rapidly and efficiently distribute this data to the scientific community. As both the data volume and number of subscribers grows, a reliable network multicast is a promising approach to alleviate the demand for the bandwidth needed to support efficient data distribution to multiple, geographically-distributed, research communities. In prior work, we identified the need for a reliable network multicast: scientists engaged in atmospheric research subscribing to meteorological file-streams. An application called Local Data Manager (LDM) is used to disseminate meteorological data to hundreds of subscribers. This paper presents a high-performance, reliable network multicast solution, Dynamic Reliable File-Stream Multicast Service (DRFSM), and describes a trial deployment comprising eight university campuses connected via Research-and-Education Networks (RENs) and Internet2 and a DRFSM-enabled LDM (LDM7). Using this deployment, we evaluated the DRFSM architecture, which uses network multicast with a reliable transport protocol, and leverages Layer-2 (L2) multipoint Virtual LAN (VLAN/MPLS). A performance monitoring system was developed to collect the realtime performance of LDM7. The measurements showed that our proof-of-concept prototype worked significantly better than the current production LDM (LDM6) in two ways. First, LDM7 distributes data faster than LDM6. With six subscribers and a 100 Mbps bandwidth limit setting, an almost 22-fold improvement in delivery time was observed with LDM7. Second, LDM7 significantly reduces the bandwidth requirement needed to deliver data to subscribers. LDM7 needed 90% less bandwidth than LDM6 to achieve a 20 Mbps average throughput across four subscribers.

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Section: Recent Related Workmentioning

confidence: 99%

High-performance reliable network-multicast over a trial deployment

et al. 2022

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“…It is worth mentioning that we have studied constrained ST based on tree growth for a multicast routing problem with quality-of-service requirements. [33][34][35][36] To the best of our knowledge, there has been no research on disaster backup with multicast in SDN. 28 Fortunately, the emergence of SDN makes it possible to realize novel multicast protocols, including the heuristic ST. For SDN multicasting, this distributed mechanism can be concentrated in the SDN controller, leading to simpler implementation of multicast.…”

Section: Disaster Backup Among Mutiple Dcsmentioning

confidence: 99%

“…31,32 For multiple bulk data transfers with a multicast mechanism in SDN, some new researches have emerged in recent years, mainly about video streaming multicast. [33][34][35][36] To the best of our knowledge, there has been no research on disaster backup with multicast in SDN. In this paper, we leverage SDN to realize customized bandwidth allocation and flow control for bandwidth-efficient data transmission in disaster backup.…”

Section: Disaster Backup Among Mutiple Dcsmentioning

confidence: 99%

Cost‐efficient disaster backup for multiple data centers using capacity‐constrained multicast

Wang

Shen

et al. 2019

Concurrency and Computation

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To leverage periodic disaster backup in a cloud data center (DC) network, previous studies employ disjoint unicast paths for bulk data transfers among multiple geographically distributed DCs, causing massive unnecessary traffic duplication. This not only adds the overhead but also may result in severe network congestion. With flexible network resource management in software-defined networks and powerful traffic aggregation capability of multicast, we propose capacity-constrained multicast to realize cost-efficient disaster backup. First, considering limited backup storage capacity and essential redundancy guarantee, we construct a capacity-constrained multicasting backup model. Then, we formulate the disaster backup problem as capacity-constrained multiple Steiner tree problem, which is NP-hard. To solve this problem, we design a new multicasting backup ant colony optimization algorithm based on requirement-aware growth. It directly optimizes every disaster-backup multicast tree (DBMT) from its root node to cover enough destination nodes guaranteeing sufficient redundancy and then expands them into the forest under the guidance of a multicast tree shared degree, the ratio of available storage capacity, and backup load distribution offset. We introduce unique edge fitness evaluation and pheromones for every DBMT to reduce mutual influences among multiple trees. Extensive simulations demonstrate that our strategy performs with less bandwidth consumption cost and relatively good backup load distribution fairness simultaneously. KEYWORDScapacity-constrained, cost-efficient, disaster backup, multiple Steiner tree INTRODUCTIONWith more and more frequent occurrence of natural disasters and human-made intentional attacks on application data centers (DCs), 1,2 a large number of data face the risk of failure. Therefore, people need to leverage periodic backup among multiple geographically distributed (geo-distributed) DCs. 3 To improve the fault-tolerant performance and obtain sufficient data redundancy, terabytes to petabytes of data produced during a period are regularly replicated and assigned to three or more other remote DCs, 4 called disaster backup. Due to the huge amount of data transfers, disaster backup activity always consumes huge bandwidth and imposes a heavy load to the links between DCs and may even cause network congestion. Therefore, to support quality-of-service requirements of various real-time applications in network, the traffic engineering problem for disaster backup among multiple DCs is of significant importance.It is noteworthy that in the research area of traffic engineering for multiple disaster-backup transfers, unicast routing has been studied widely, while multicast routing attracts much less attention. Multicast is designed to deliver contents to a large number of destinations and benefits DC group communications in three aspects. By saving network traffic, it can increase the throughput of bandwidth-hungry computations. By releasing the sender from sending multiple copies of packets to diffe...

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