Datacenter Traffic Control: Understanding Techniques and Tradeoffs

Noormohammadpour, Mohammad; Raghavendra, C.S.

doi:10.1109/comst.2017.2782753

Cited by 133 publications

(57 citation statements)

References 168 publications

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“…However, as shown in §7, these techniques are oblivious of the load on each connection and thus result in hot spots and unreliable SLOs. There are also other load balancing and migration techniques [1,19,20,44,46,50,64,72], but they do not take SLO requirements into consideration when handling connections.…”

Section: Related Workmentioning

confidence: 99%

MMLite

Nagendra

Bhattacharya

Gandhi

et al. 2019

Proceedings of the 2019 ACM Symposium on SDN Research

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With increase in cellular-enabled IoT devices having diverse traffic characteristics and service level objectives (SLOs), handling the control traffic in a scalable and resource-efficient manner in the cellular packet core network is critical. The traditional monolithic design of the cellular core adopted by service-providers is inflexible with respect to the diverse requirements and bursty loads of IoT devices, specifically for properties such as elasticity, customizability, and scalability. To address this key challenge, we focus on the most critical control plane component of the cellular packet core network, the Mobility Management Entity (MME). We present MMLite, a functionally decomposed and stateless MME design wherein individual control procedures are implemented as microservices and states are decoupled from their processing, thus enabling elasticity and fault tolerance. For SLO compliance, we develop a multi-level load balancing approach based on skewed consistent hashing to efficiently distribute incoming connections. We evaluate the performance benefits of MMLite over existing approaches with respect to scaling, fault tolerance, SLO compliance and resource efficiency.

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Section: Related Workmentioning

confidence: 99%

MMLite

Nagendra

Bhattacharya

Gandhi

et al. 2019

Proceedings of the 2019 ACM Symposium on SDN Research

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“…In order to guarantee quality and highly available services, these services are generally operated on top of multiple datacenters dispersed in different cities or countries close to regional users [1], [2], [3]. Given such architecture, it is necessary to replicate data, such as updated machine learning models and multimedia, across the datacenters that offer the same services to reduce the response latency to access services, enhance failure recovery, and expedite geo-distributed data analytics [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16]. A representative example is the search service, where the search engines periodically synchronize their search index databases across multiple locations to improve overall search quality (e.g., relevance and precision) and user satisfaction [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Deadline-Aware Fast One-to-Many Bulk Transfers over Inter-Datacenter Networks

Luo

Kong

Noormohammadpour

et al. 2022

IEEE Trans. Cloud Comput.

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An increasing number of cloud services are operated globally, where the service data are frequently replicated across geographically distributed datacenters to improve service quality and reliability. Such replication generates many one-to-many bulk data transfers over inter-datacenter networks from one datacenter to many receiver datacenters. To provide end-users with guaranteed services, these data transfers are usually required to be completed within designated deadlines. Despite the exponential growth in data demand, there has been little work on guaranteeing deadlines for one-to-many transfers, which is the subject of this paper. This paper proposes a centralized admission control coupled with a scheduling algorithm, named deAdline-Guaranteed transfEr (AGE), to guarantee the deadline of admitted data transfers and utilize the network capacity efficiently. The key idea is to flexibly select the source datacenter for receiver datacenters and allow the remaining receivers to obtain a replica from either the original source or the other receivers that have already received a copy. By jointly allocating the source for receivers and the bandwidth and routing paths for every data transfer, AGE maximizes the number of deadline-satisfied transfers. Our simulations show that compared to the state-of-the-art, AGE guarantees the deadline for up to 70% more transfers, achieves at least 2× higher network throughput, and reduces the completion time up to 80%.

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“…The flowlet formation depends on various factors that are related to the applications and the transport layer. As a result, using a flowlet-based reaction to the path conditions does not provide a timely reaction to congestion [10]. In addition, switch modification-based approaches (CONGA and HULA) are based on switching or hardware modification that imposes impracticalities [12,13].…”

mentioning

confidence: 99%

“…Moreover, Hermes uses the unsystematic hashing, round robin, and seeks the best path to route and reroute based on the path conditions as well as flow status. However, stability is a key concern for the condition when flows interact in the network [10]. The bursty network congestion has been well addressed in [15][16][17] at the transport and link layers.…”

mentioning

confidence: 99%

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Novel Multi-Level Dynamic Traffic Load-Balancing Protocol for Data Center

et al. 2019

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Typically, the production data centers function with various risk factors, such as for instance the network dynamicity, topological asymmetry, and switch failures. Hence, the load-balancing schemes should consider the sensing accurate path circumstances as well as the reduction of failures. However, under dynamic traffic, current load-balancing schemes use the fixed parameter setting, resulting in suboptimal performances. Therefore, we propose a multi-level dynamic traffic load-balancing (MDTLB) protocol, which uses an adaptive approach of parameter setting. The simulation results show that the MDTLB outperforms the state-of-the-art schemes in terms of both the flow completion time and throughput in typical data center applications.

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Datacenter Traffic Control: Understanding Techniques and Tradeoffs

Cited by 133 publications

References 168 publications

MMLite

MMLite

Deadline-Aware Fast One-to-Many Bulk Transfers over Inter-Datacenter Networks

Novel Multi-Level Dynamic Traffic Load-Balancing Protocol for Data Center

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