Towards load-balanced service chaining by Hash-based Traffic Steering on Softswitches

Thai, Minh-Tuan; Lin, Ying-Dar; Lin, Po Ching; Lai, Yuan Cheng

doi:10.1016/j.jnca.2018.02.018

Cited by 11 publications

(5 citation statements)

References 7 publications

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“…A general observation in the HH classification literature is that there is no consensus on what feature or set of features (e.g., size, duration, and rate) should be selected. In consequence, some techniques rely on size 7,8,63,[83][84][85] while other approaches use rate 50,69,86 or duration 50,51 as a threshold. Other features such as the number of packets transmitted in the flow 52 and specific data structures 87 are also used as a threshold.…”

Section: Challenges Of Threshold-based Classificationmentioning

confidence: 99%

Towards threshold‐agnostic heavy‐hitter classification

et al. 2021

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A heavy-hitter (HH) network traffic flow consumes considerably more network resources than other flows combined. The classification of HHs is critical to provide, among others, the required level of Quality of Service and reliability in both conventional and data center networks. HH classification is typically threshold-based. However, there is no consistent and accepted threshold or set of thresholds that would reliably classify flows. Furthermore, existing threshold-driven approaches use counters (e.g., duration, packets, and bytes); thus, their accuracy depends on how complete the flow information is. This paper paves the way to threshold-agnostic HH identification by proposing an approach that performs HH classification based on per-flow packet size distribution (PSD) and template matching (TM). PSD allows capturing the behavior and dynamism of network traffic flows (even from their first few packets). TM enables to classify HHs by measuring the similarity between the PSD of observed flows and a set of master templates representing the flow size behavior of HH classes. We evaluated the PSD-and TM-based approach using flows extracted from real traffic traces. Results show that our approach classifies HHs accurately and timely, corroborating that the threshold-less perspective is feasible for HH identification.

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Section: Challenges Of Threshold-based Classificationmentioning

confidence: 99%

Towards threshold‐agnostic heavy‐hitter classification

et al. 2021

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“…In other words, when two elephant flows have the same hash value, they may share the same link or instance, which will cause the shared link or instance to be heavily loaded, even though the others' loads are light. Although the HATS-Flowcell algorithm, presented in paper [27], was designed to solve this problem of hash collision, this algorithm incurs problems involving packet reordering.…”

Section: A Motivationmentioning

confidence: 99%

“…However, the ECMP approach may cause the hash collision of elephant flows when the hash values of different elephant flows are not considered. To solve the problem of service chaining in a DCN, in the method presented in [27], VNF instances are selected and paths are constructed at soft edge switches by hashing packet headers instead of triggering the centralized controller. In this way, the number of flow entries in the system is reduced while achieving network and VNF load balancing.…”

Section: Sfc Mapping By Utilizing Multiple Pathsmentioning

confidence: 99%

“…In this way, the number of flow entries in the system is reduced while achieving network and VNF load balancing. Specifically, the HATS-Flowcell algorithm proposed in [27] breaks elephant flows into equally sized bursts of packets called flowcells to avoid the hash collision of elephant flows. The work presented in [28] considers the splitting of flows and the replication of VNFs; in this sense, it exhibits some similarities with our article.…”

Section: Sfc Mapping By Utilizing Multiple Pathsmentioning

confidence: 99%

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Online Parallelized Service Function Chain Orchestration in Data Center Networks

Sun

Chen

et al. 2019

IEEE Access

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In recent years, much attention has been focused on deploying service function chains (SFCs), each of which is composed of a set of virtual network functions (VNFs) in a specified order. This is a promising approach for enabling cloud service providers to deploy user service requests more flexibly while saving costs. However, less effort has been directed toward meeting heterogeneous needs, such as high throughput or low latency of user service requests with heterogeneous bandwidth demands, especially in data center networks (DCNs). In this paper, we propose an efficient orchestration algorithm for online SFC requests. It first splits a large flow into a number of subflows and replicates the same number of sub-SFCs. Each subflow is redirected to one of these ''parallelized'' sub-SFCs, which is termed a sub-user request. Then, each sub-user request is deployed based on a worst-fit strategy, and VNFs in the same SFC are instantiated on the same server to the greatest possible extent. Our algorithm is expected to enable network load balancing, reducing the delay experienced by small flows while improving the acceptance ratio for user requests. Finally, the simulation results show that the proposed algorithm outperforms other comparable algorithms.INDEX TERMS Service function chain, splitting, data center network, orchestration, load balancing.

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“…Finally, updates in the monitoring module do not trigger any changes in other components. The proposed standalone monitoring module could be used to feed monitoring information to a service orchestrator, or to a traffic steering mechanism operating over complex infrastructures comprising both network and computing resources (as, for example, presented in [8]). Additionally, our solution may be useful to discover the limitations and real performance of a virtualized infrastructure managed and controlled by a third-party provider.…”

Section: Introductionmentioning

confidence: 99%