Profiling and accelerating commodity NFV service chains with SCC

Katsikas, Georgios P.; Maguire, Gerald Q.; Kosti, Dejan

doi:10.1016/j.jss.2017.01.005

Cited by 13 publications

(21 citation statements)

References 4 publications

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“…There it is also described how descriptor files can help to more strictly orchestrate VNF performance to specific hardware. To accurately profile and reproduce the VNF performance, the service operator must be aware of the factors which can [8].…”

Section: Related Workmentioning

confidence: 99%

Profile-Based Resource Allocation for Virtualized Network Functions

Rossem

Tavernier

Colle

et al. 2019

IEEE Trans. Netw. Serv. Manage.

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The virtualization of compute and network resources enables an unseen flexibility for deploying network services. A wide spectrum of emerging technologies allows an ever-growing range of orchestration possibilities in cloud-based environments. But in this context it remains challenging to rhyme dynamic cloud configurations with deterministic performance. The service operator must somehow map the performance specification in the Service Level Agreement (SLA) to an adequate resource allocation in the virtualized infrastructure. We propose the use of a VNF profile to alleviate this process. This is illustrated by profiling the performance of four example network functions (a virtual router, switch, firewall and cache server) under varying workloads and resource configurations. We then compare several methods to derive a model from the profiled datasets. We select the most accurate method to further train a model which predicts the services' performance, in function of incoming workload and allocated resources. Our presented method can offer the service operator a recommended resource allocation for the targeted service, in function of the targeted performance and maximum workload specified in the SLA. This helps to deploy the softwarized service with an optimal amount of resources to meet the SLA requirements, thereby avoiding unnecessary scaling steps.

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Section: Related Workmentioning

confidence: 99%

Profile-Based Resource Allocation for Virtualized Network Functions

Rossem

Tavernier

Colle

et al. 2019

IEEE Trans. Netw. Serv. Manage.

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“…The assignment is solved independently for each NUMA node, unless there is a large difference in the average load between the NUMA nodes, in which case the assignment will be solved on all cores and buckets at once to re-arrange the balance, allowing inter-NUMA transfers. Unfortunately, in recent x86 architectures a NIC is attached to a given socket, hence its traffic will go via this CPU's interconnect resulting in decreased performance when using cores in another NUMA node [24,27]. Further evaluation of NUMA awareness and potential solutions, such as multi-socket NICs [37], are left for future work.…”

Section: Numa and Nuca Awarenessmentioning

confidence: 99%

Rss++

Barbette

Katsikas

Maguire

et al. 2019

Proceedings of the 15th International Conference on Emerging Networking Experiments and Technologies

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While the current literature typically focuses on load-balancing among multiple servers, in this paper, we demonstrate the importance of load-balancing within a single machine (potentially with hundreds of CPU cores). In this context, we propose a new load-balancing technique (RSS++) that dynamically modifies the receive side scaling (RSS) indirection table to spread the load across the CPU cores in a more optimal way. RSS++ incurs up to 14x lower 95 th percentile tail latency and orders of magnitude fewer packet drops compared to RSS under high CPU utilization. RSS++ allows higher CPU utilization and dynamic scaling of the number of allocated CPU cores to accommodate the input load while avoiding the typical 25% over-provisioning. RSS++ has been implemented for both (i) DPDK and (ii) the Linux kernel. Additionally, we implement a new state migration technique which facilitates sharding and reduces contention between CPU cores accessing per-flow data. RSS++ keeps the flowstate by groups that can be migrated at once, leading to a 20% higher efficiency than a state of the art shared flow table.

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“…Current I/O devices are powerful enough to perform some actions to reduce end-to-end latency. For example, many NICs can perform pre-processing of packets (e.g., packet filters, rate-limiting, and routing the packet to a dedicated CPU), which when exploited reduces end-to-end latency in a DC-wide network (e.g., see [95,187,188]).…”

Section: Simulation and Emulation And Prototypementioning

confidence: 99%