Industrial-Scale Stateless Network Functions

Szalay, Márk; Nagy, Máté; Gehberger, Daniel; Kiss, Zoltán; Mátray, Péter; Németh, Felicián; Pongrácz, Gergely; Rétvári, Gábor; Toka, László

doi:10.1109/cloud.2019.00068

Cited by 14 publications

(7 citation statements)

References 9 publications

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“…As for the technical migration overhead, we argue that stateless [26] application components can be migrated with minimal extra resources. The stateless design, of course, must be supported by a distributed cloud database [24,25], which transforms the punctual migration overhead into a continuous synchronization of application states onto multiple database instances running on nodes potentially hosting the stateless application, which leads to an extra consumption in terms of compute, memory and network resources.…”

Section: Complexity Analysis Of Pod Migration Calculationmentioning

confidence: 99%

“…The standard way of achieving these goals is to decouple the life-cycle of important application states from the life-cycle of individual application instances: states, and data in general, are written to and read from cloud databases, deployed close to the application code. Rooted in cloud-native computing, the stateless design outsources the state embedded in computing entities, e.g., virtual machines, containers, Pods, virtual network functions, to a dedicated state storage layer, facilitating elastic scaling and resiliency [26]. In [26] the authors propose a system design that can be adapted to any cloud application without the need for complex coordination among the network control, the stateless application elements, and the state storage backend.…”

Section: Latency-critical Cloud-native Applications and The Edge Cloudmentioning

confidence: 99%

“…Rooted in cloud-native computing, the stateless design outsources the state embedded in computing entities, e.g., virtual machines, containers, Pods, virtual network functions, to a dedicated state storage layer, facilitating elastic scaling and resiliency [26]. In [26] the authors propose a system design that can be adapted to any cloud application without the need for complex coordination among the network control, the stateless application elements, and the state storage backend. They present the first product-phase realization of the stateless paradigm, an operational virtualized IP Multimedia Subsystem that can restore the live call records of thousands of mobile subscribers under a couple of seconds with half the resources required by a traditional "stateful" design.…”

Section: Latency-critical Cloud-native Applications and The Edge Cloudmentioning

confidence: 99%

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Ultra-Reliable and Low-Latency Computing in the Edge with Kubernetes

Toka

2021

J Grid Computing

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Novel applications will require extending traditional cloud computing infrastructure with compute resources deployed close to the end user. Edge and fog computing tightly integrated with carrier networks can fulfill this demand. The emphasis is on integration: the rigorous delay constraints, ensuring reliability on the distributed, remote compute nodes, and the sheer scale of the system altogether call for a powerful resource provisioning platform that offers the applications the best of the underlying infrastructure. We therefore propose Kubernetes-edge-scheduler that provides high reliability for applications in the edge, while provisioning less than 10% of resources for this purpose, and at the same time, it guarantees compliance with the latency requirements that end users expect. We present a novel topology clustering method that considers application latency requirements, and enables scheduling applications even on a worldwide scale of edge clusters. We demonstrate that in a potential use case, a distributed stream analytics application, our orchestration system can reduce the job completion time to 40% of the baseline provided by the default Kubernetes scheduler.

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Section: Complexity Analysis Of Pod Migration Calculationmentioning

confidence: 99%

Section: Latency-critical Cloud-native Applications and The Edge Cloudmentioning

confidence: 99%

Section: Latency-critical Cloud-native Applications and The Edge Cloudmentioning

confidence: 99%

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Ultra-Reliable and Low-Latency Computing in the Edge with Kubernetes

Toka

2021

J Grid Computing

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“…The main goal of our proposed system design is to enable FaaS functionality for users, with both horizontal scaling, and with small function composition and data access overhead, while retaining the flexibility and security of the currently available platforms. Our key observation is that local data access and function invocations within the same server are typically an order of magnitude faster than remote ones [35], [36]. Some illustrative overhead values can be found in Table II.…”

Section: A Motivation For Co-locationmentioning

confidence: 99%

Location, Proximity, Affinity – The key factors in FaaS

Haja¹,

Turányi²,

Toka³

2020

Infocommunications journal

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The Function-as-a-Service paradigm emerged not only as a pricing technique, but also as a programming model promising to simplify developing to the cloud. Interestingly, while placing functions across hosts under the service platform is believed to be flexible, currently the available platforms pay little attention to co-locate connected functions, or data with the respective processing function in order to improve performance. Even though the local function invocation and data access might be an order of magnitude faster than their remote intra-cloud counterparts. In this paper, we therefore propose a Function-asa- Service platform design that reaps the performance benefits of co-location. We build the platform on WebAssembly, a secure and flexible tool for efficient local function invocations, and on a distributed in-memory database, which allows arbitrary data placement. On top we advocate smart placement strategies for function executions and data, decoupled from the functions. Hence we envision good horizontal scaling of functions while keeping the experienced processing latency to that of a single machine case.

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“…With the advent of 5G, there is a strong drive in the industry to make applications more cloud-native [5,6]-that is, transform them so that they are stateless. The advantage of this approach is that the applications become independent of the underlying infrastructure (cloud vendor agnosticism), which paves the way for novel cloud, edge, and mobile management systems and use-cases, like vendor-agnostic resource consolidation [7], resource-aware cloud service admission control [8], and real-time (low-latency) 5G-enabled industrial IoT [9].…”

Section: Introductionmentioning

confidence: 99%

State Management for Cloud-Native Applications

2021

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The stateless cloud-native design improves the elasticity and reliability of applications running in the cloud. The design decouples the life-cycle of application states from that of application instances; states are written to and read from cloud databases, and deployed close to the application code to ensure low latency bounds on state access. However, the scalability of applications brings the well-known limitations of distributed databases, in which the states are stored. In this paper, we propose a full-fledged state layer that supports the stateless cloud application design. In order to minimize the inter-host communication due to state externalization, we propose, on the one hand, a system design jointly with a data placement algorithm that places functions’ states across the hosts of a data center. On the other hand, we design a dynamic replication module that decides the proper number of copies for each state to ensure a sweet spot in short state-access time and low network traffic. We evaluate the proposed methods across realistic scenarios. We show that our solution yields state-access delays close to the optimal, and ensures fast replica placement decisions in large-scale settings.

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Industrial-Scale Stateless Network Functions

Cited by 14 publications

References 9 publications

Ultra-Reliable and Low-Latency Computing in the Edge with Kubernetes

Ultra-Reliable and Low-Latency Computing in the Edge with Kubernetes

Location, Proximity, Affinity – The key factors in FaaS

State Management for Cloud-Native Applications

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