Efficient load balancing for multicast traffic in data center networks using SDN

Nithin, V; Rathod, Akash B.; Badarla, Venkataramana; Humernbrum, Tim; Gorlatch, Sergei

doi:10.1109/comsnets.2018.8328187

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Moreover, there has been scarce research efforts on multicast load balancing traffic in DCs. For example, in [13], [14], [54], multicast traffic load balancing is done by maintaining bandwidth information for all the paths between all ToR switches. These approaches work well only in small 2-tier, leaf-spine topologies and do not scale well for large 3-tier, Clos topologies, which are widely deployed in production DCs.…”

Section: B Load Balancingmentioning

confidence: 99%

Ernie: Scalable Load-Balanced Multicast Source Routing for Cloud Data Centers

Alqahtani¹,

Hamdaoui

Langar

2021

IEEE Access

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Nowadays, most applications hosted on public cloud data centers (DCs) disseminate data from a single source to a group of receivers for service deployment, data replication, software upgrade, etc. For such one-to-many data communication paradigm, multicast routing is the natural choice as it reduces network traffic and improves application throughput. Unfortunately, recent approaches adopting IP multicast routing suffer from scalability and load balancing issues, and do not scale well with the number of supported multicast groups when used for cloud DC networks. Furthermore, IP multicast does not exploit the topological properties of DCs, such as the presence of multiple parallel paths between end hosts. Despite the recent efforts aimed at addressing these challenges, there is still a need for multicast routing protocol designs that are both scalable and load-balancing aware. This paper proposes Ernie, a scalable load-balanced multicast source routing for large-scale DCs. At its heart, Ernie further exploits DC network structural properties and switch programmability capabilities to encode and organize multicast group information inside packets in a way that minimizes downstream header sizes significantly, thereby reducing overall network traffic. Additionally, Ernie introduces an efficient load balancing strategy, where multicast traffic is adequately distributed at downstream layers. To study the effectiveness of Ernie, we extensively evaluate Ernie's scalability behavior (i.e., switch memory, packet size overheads, and CPU overheads), and load balancing ability through a mix of simulation and analysis of its performances. For example, experiments of large-scale DCs with 27k+ servers show that Ernie requires a downstream header sizes that are 10× smaller than those needed under state-of-the-art schemes while keeping end-host overheads at low levels. Our simulation results also indicate that at highly congested links, Ernie can achieve a better multicast load balancing than other existing schemes.

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Section: B Load Balancingmentioning

confidence: 99%

Ernie: Scalable Load-Balanced Multicast Source Routing for Cloud Data Centers

Alqahtani¹,

Hamdaoui

Langar

2021

IEEE Access

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“…This method achieves dynamic link selection; however, when considering the entire network, it is not the best because it may lead to hyper charged loading of some links. For multicast traffic in high-traffic network topology, Nithin et al 16 present a load balance method applied to multicast traffic. AlZain et al 17 presents a proposed multi-cloud data management model called Byzantine fault tolerance multi-clouds database (BFT-MCDB).…”

Section: Related Workmentioning

confidence: 99%

Research on scheduling method based on traffic matrix for IoT security

Cao

Zhu

et al. 2020

Int J Network Mgmt

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With the increasing number of sensors in the Internet of Things (IoT), network nodes are prone to load imbalance, which seriously affects the reliability and scalability of the IoT. In this paper, a method of load balancing is proposed to reduce the pressure of switches in IoT and improve the efficiency of switch. The method is based on software defined network (SDN) technology using dynamic traffic scheduling of server cluster (DTSSC) model. It obtains the traffic and load matrices for each server and achieves load balancing on each of them. This method has high efficiency and low overhead and is easy to deploy. The validity of this method is verified through experiments; ie, the load ratio of each server is balanced.

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“…However, it has the main shortcoming of high cost with the limitation in storing the flow rules complementing high power consumption. The majority of the commercial OpenFlow switches have the on‐chip TCAM storage that accommodates the maximum of 750–2000 rules (e.g., HP 5406zl is limited to 1500 entries) 10‐13 …”

Section: Introductionmentioning

confidence: 99%

Decision Tree‐Based Entries Reduction scheme using multi‐match attributes to prevent flow table overflow in SDN environment

2020

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Summary The software‐defined networking is used extensively in data centers that provide centralized control for the widely deployed networking resources. The traffic is shaped by rules created by the controller dynamically without modifying the individual switch. The key component that stores rules which are used to process the flows is the flow table which resides in the ternary content addressable memory. The current commercial OpenFlow appliances accommodate limited entries up to 8000 due to its high cost and high power consumption. There are two issues to be considered, where (1) flow table's inability to provide rules during flow table overflow leads to dropping of incoming packets and (2) the significant amount of rule replacement occurs when the traffic in data centers increases which creates massive route requests to controller creating overhead. The proposed scheme prevents flow table overflow using the robust machine learning algorithm called decision tree (Iterative Dichotomiser 3) that allows the flow table to learn its high prioritized fine‐grained entries by means of multiple matching attributes. The entries are classified, and the usual eviction process is replaced by pushing the low important entries into counting bloom filter which acts as a cache to prevent flow entry miss. The simulations were carried out using real‐time network traffic datasets, and the comparisons with the various existing schemes prove that the proposed approach reduces 99.99% of the controller's overhead and the entries are minimized to 99% providing extra space for new flows.

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Efficient load balancing for multicast traffic in data center networks using SDN

Cited by 7 publications

References 11 publications

Ernie: Scalable Load-Balanced Multicast Source Routing for Cloud Data Centers

Ernie: Scalable Load-Balanced Multicast Source Routing for Cloud Data Centers

Research on scheduling method based on traffic matrix for IoT security

Decision Tree‐Based Entries Reduction scheme using multi‐match attributes to prevent flow table overflow in SDN environment

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