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A Performance Modelling Approach for SLA-Aware Resource Recommendation in Cloud Native Network Functions
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013). (DISCO)ORCID iD: 0000-0002-4825-8831
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0001-9194-010X
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0002-6101-4305
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0002-9446-8143
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2020 (English)In: 2020 6th IEEE Conference on Network Softwarization (NetSoft), IEEE, 2020, p. 292-300Conference paper, Published paper (Refereed)
Abstract [en]

Network Function Virtualization (NFV) becomes the primary driver for the evolution of 5G networks, and in recent years, Network Function Cloudification (NFC) proved to be an inevitable part of this evolution. Microservice architecture also becomes the de facto choice for designing a modern Cloud Native Network Function (CNF) due to its ability to decouple components of each CNF into multiple independently manageable microservices. Even though taking advantage of microservice architecture in designing CNFs solves specific problems, this additional granularity makes estimating resource requirements for a Production Environment (PE) a complex task and sometimes leads to an over-provisioned PE. Traditionally, performance engineers dimension each CNF within a Service Function Chain (SFC) in a smaller Performance Testing Environment (PTE) through a series of performance benchmarks. Then, considering the Quality of Service (QoS) constraints of a Service Provider (SP) that are guaranteed in the Service Level Agreement (SLA), they estimate the required resources to set up the PE. In this paper, we used a machine learning approach to model the impact of each microservice's resource configuration (i.e., CPU and memory) on the QoS metrics (i.e. serving throughput and latency) of each SFC in a PTE. Then, considering an SP's Service Level Objectives (SLO), we proposed an algorithm to predict each microservice's resource capacities in a PE. We evaluated the accuracy of our prediction on a prototype of a cloud native 5G Home Subscriber Server (HSS). Our model showed 95%-78% accuracy in a PE that has 2–5 times more computing resources than the PTE.

Place, publisher, year, edition, pages
IEEE, 2020. p. 292-300
Keywords [en]
NFV, SDN, performance modeling, cloud, network, optimization
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-80081DOI: 10.1109/NetSoft48620.2020.9165482ISI: 000623436400048Scopus ID: 2-s2.0-85091994999OAI: oai:DiVA.org:kau-80081DiVA, id: diva2:1464395
Conference
IEEE NetSoft 2020, 29 June-3 July 2020, Ghent, Belgium
Funder
Knowledge Foundation, 5276
Note

Virtual conference

Available from: 2020-09-06 Created: 2020-09-06 Last updated: 2023-11-14Bibliographically approved
In thesis
1. Performance Modelling and Simulation of Service Chains for Telecom Clouds
Open this publication in new window or tab >>Performance Modelling and Simulation of Service Chains for Telecom Clouds
2021 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

New services and ever increasing traffic volumes require the next generation of mobile networks, e.g. 5G, to be much more flexible and scalable. The primary enabler for its flexibility is transforming network functions from proprietary hardware to software using modern virtualization technologies, paving the way of virtual network functions (VNF). Such VNFs can then be flexibly deployed on cloud data centers while traffic is routed along a chain of VNFs through software-defined networks. However, such flexibility comes with a new challenge of allocating efficient computational resources to each VNF and optimally placing them on a cluster.

In this thesis, we argue that, to achieve an autonomous and efficient performance optimization method, a solid understanding of the underlying system, service chains, and upcoming traffic is required. We, therefore, conducted a series of focused studies to address the scalability and performance issues in three stages. We first introduce an automated profiling and benchmarking framework, named NFV-Inspector to measure and collect system KPIs as well as extract various insights from the system. Then, we propose systematic methods and algorithms for performance modelling and resource recommendation of cloud native network functions and evaluate them on a real 5G testbed. Finally, we design and implement a bottom-up performance simulator named PerfSim to approximate the performance of service chains based on the nodes’ performance models and user-defined scenarios.

Place, publisher, year, edition, pages
Karlstad: Karlstads universitet, 2021. p. 23
Series
Karlstad University Studies, ISSN 1403-8099 ; 2021:14
Keywords
Performance Modelling, Performance Optimization, Performance Simulation, Network Function Virtualization, Cloud Native Computing
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-83687 (URN)978-91-7867-199-1 (ISBN)978-91-7867-209-7 (ISBN)
Presentation
2021-06-09, 21A342, Universitetsgatan 2, 651 88 Karlstad, Karlstad, 09:00 (English)
Opponent
Supervisors
Note

Article 5 part of thesis as manuscript, now published.

Available from: 2021-05-18 Created: 2021-04-16 Last updated: 2022-03-04Bibliographically approved
2. Unchaining Microservice Chains: Machine Learning Driven Optimization in Cloud Native Systems
Open this publication in new window or tab >>Unchaining Microservice Chains: Machine Learning Driven Optimization in Cloud Native Systems
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

As the cloud native landscape flourishes, microservices emerge as a central pillar for contemporary software development, enabling agility, resilience, and scalability in modern computing environments. While these modular services promise opportunities, particularly in the transformative ecosystem of 5G and beyond, they also introduce a myriad of complexities. Notably, the migration from hardware-centric to software-defined environments, culminating in Virtual Network Functions (VNF), has facilitated dynamic deployments across cloud data centers. In this transition, VNFs are often deployed within cloud native environments as independent services, mirroring the microservices model. However, the advantage of flexibility in cloud native systems is shadowed by bottlenecks in computational resource allocation, sub-optimal service chain placements, and the perpetual quest for performance enhancement. Addressing these concerns is not just pivotal but indispensable for harnessing the true potential of microservice chains.

In this thesis, the inherent challenges presented by cloud native microservice chains are addressed through the development and application of various tools and methodologies. The NFV-Inspector is introduced as a foundational tool, employing a systematic approach to profile and analyze Virtual Network Functions, subsequently extracting essential system KPIs essential for further modeling. Subsequent research introduced a Machine Learning (ML) based SLA-Aware resource recommendation system for cloud native functions. This system leveraged regression modeling techniques to correlate key performance metrics. Following this, PerfSim is proposed as a performance simulation tool designed specifically for cloud native computing environments, aiming to improve the accuracy of microservice chain simulations. Further research is conducted on Service Function Chain (SFC) Placement, emphasizing the equilibrium between cost-efficiency and latency optimization. The thesis concludes by integrating Deep Learning (DL) techniques for service chain optimization, employing both Graph Attention Networks (GAT) and Deep Q-Learning (DQN), highlighting the intersection of DL techniques and SFC performance optimization.

Abstract [en]

In the dynamic cloud native landscape, microservices stand out as pivotal for modern software development, enhancing agility, resilience, and scalability. These services, crucial in the transformative 5G era, introduce complexities such as resource allocation, service chain placement, and performance optimization challenges. This thesis delves into these challenges, emphasizing the development and application of tools and methodologies specific to microservice chains.

Key contributions include the NFV-Inspector, which, while focusing on Virtual Network Functions, is instrumental in profiling and analyzing microservices, extracting vital KPIs for advanced modeling. Further, a Machine Learning-based SLA-Aware system is introduced for resource recommendation in cloud-native functions, utilizing regression modeling to link performance metrics. PerfSim, another simulation framework, is proposed for simulating microservice chains in cloud environments. The thesis also explores Service Function Chain (SFC) placement, aiming to balance cost-efficiency with latency optimization. The thesis concludes by integrating Deep Learning (DL) for service chain optimization, employing both Graph Attention Networks (GAT) and Deep Q-Learning (DQN), showcasing the potentials of DL in SFC optimization.

Place, publisher, year, edition, pages
Karlstad: Karlstads universitet, 2023. p. 36
Series
Karlstad University Studies, ISSN 1403-8099 ; 2023:35
Keywords
Cloud Native Computing, Service Mesh, Performance Modelling, Performance Optimization, Performance Simulation, Machine Learning, Resource Allocation
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-97377 (URN)978-91-7867-420-6 (ISBN)978-91-7867-421-3 (ISBN)
Public defence
2024-01-17, 1B309, Sjöströmsalen, Universitetsgatan 2, Karlstad, 08:30 (English)
Opponent
Supervisors
Available from: 2023-12-04 Created: 2023-11-14 Last updated: 2024-02-02Bibliographically approved

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Publisher's full textScopushttps://ieeexplore.ieee.org/abstract/document/9165482

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Gokan Khan, MichelTaheri, JavidKhoshkholghi, Mohammad AliKassler, Andreas

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