Datacast: A Scalable and Efficient Reliable Group Data Delivery Service for Data Centers

Cao, Jiaxin; Guo, Chuanxiong; Lu, Guohan; Xiong, Yan; Zheng, Yixin; Zhang, Yongguang; Zhu, Yibo; Chen, Chen; Tian, Ye

doi:10.1109/jsac.2013.131205

Cited by 39 publications

(28 citation statements)

References 18 publications

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“…Using SDN, one can build more efficient trees according to network topology and conditions as well as data properties. While assigning trees to data flows, in addition to reducing bandwidth usage, several objectives include reducing completion times [3], [4], minimizing deadline miss rate [5], [6], and balancing link utilization [7]. …”

Section: Sdn Switchesmentioning

confidence: 99%

List of several SDN switches that support Group Table ALL feature

Noormohammadpour¹,

Raghavendra²

2018

Preprint

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Section: Sdn Switchesmentioning

confidence: 99%

List of several SDN switches that support Group Table ALL feature

Noormohammadpour¹,

Raghavendra²

2018

Preprint

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“…Datacast presents another overlay approach based on inswitch packet caching and edge-disjoint Steiner trees [9], which is more efficient than end-host overlays. Blast has higher transmission speed than Datacast, because it uses high-rate optical channels dedicated for multicast traffic.…”

Section: Related Workmentioning

confidence: 99%

Blast: Accelerating high-performance data analytics applications by optical multicast

Xia

Sun

2015

2015 IEEE Conference on Computer Communications (INFOCOM)

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Multicast data dissemination is the performance bottleneck for high-performance data analytics applications in cluster computing, because terabytes of data need to be distributed routinely from a single data source to hundreds of computing servers. The state-of-the-art solutions for delivering these massive data sets all rely on application-layer overlays, which suffer from inherent performance limitations. This paper presents Blast, a system for accelerating data analytics applications by optical multicast. Blast leverages passive optical power splitting to duplicate data at line rate on a physical-layer broadcast medium separate from the packet-switched network core. We implement Blast on a small-scale hardware testbed. Multicast transmission can start 33ms after an application issues the request, resulting in a very small control overhead. We evaluate Blast's performance at the scale of thousands of servers through simulation. Using only a 10Gbps optical uplink per rack, Blast achieves upto 102× better performance than the state-of-the-art solutions even when they are used over a non-blocking core network with a 400Gbps uplink per rack.

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“…Constraint (11) indicates that a multicast tree can only be constructed for the chosen node mapping solution. Constraint (12) ensures that the links in the constructed multicast tree correspond to existing links in the substrate network. Clearly, this model is hard to scale.…”

Section: The Mvne Ilp Modelmentioning

confidence: 99%

“…Each VN request contains a source node, and a set of terminal nodes in the range [3][4][5][6][7][8][9][10]. The CPU demand of the virtual nodes is in the range [3][4][5][6][7][8][9][10][11][12] GB, while the bandwidth demand can vary between 50 and 500 MB/s. The end-delay threshold is a random number between [6][7][8][9][10] hops, while the differential-delay variation constraint can be between [2][3][4][5][6][7][8][9][10] hops.…”

Section: Blocking Ratiomentioning

confidence: 99%

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Multicast Virtual Network Embedding in Cloud Data Centers with Delay Constraints

Ayoubi

Shaban

Assi

2014

2014 IEEE 7th International Conference on Cloud Computing

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Network virtualization enables the multi-tenancy concept and paves the way towards more advancements and innovation in the underlying infrastructure. With network virtualization, allocating resources to Virtual Networks (VNs) that represent tenants' requests emerges as a challenging problem. This problem is commonly known as the Virtual Network Embedding (VNE) problem, and its NP-Hard nature has drawn a lot of attention from the research community. A common feature in the existing work is that the type of communication in the VN requests was never characterized, assuming that they exhibit unicast communication only. In this paper, we motivate the importance of characterizing the type of communication in VN requests. We present a formal definition of the VNE problem for VNs with multicast communication. To the best of our knowledge, the multicast VNE problem has not been addressed in the frame of cloud computing, where the location of all the virtual machines in a given multicast VN is unknown. We propose a novel 3-steps heuristic to solve the multicast VNE problem with end-delay and delay variation constraints. Our numerical results prove the efficiency of our suggested approach over multiple metrics and against numerous embedding heuristics.

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Datacast: A Scalable and Efficient Reliable Group Data Delivery Service for Data Centers

Cited by 39 publications

References 18 publications

List of several SDN switches that support Group Table ALL feature

List of several SDN switches that support Group Table ALL feature

Blast: Accelerating high-performance data analytics applications by optical multicast

Multicast Virtual Network Embedding in Cloud Data Centers with Delay Constraints

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