5 research outputs found
Design and Analysis of Deadline and Budget Constrained Autoscaling (DBCA) Algorithm for 5G Mobile Networks
In cloud computing paradigm, virtual resource autoscaling approaches have been intensively studied recent years. Those approaches dynamically scale in/out virtual resources to adjust system performance for saving operation cost. However, designing the autoscaling algorithm for desired performance with limited budget, while considering the existing capacity of legacy network equipment, is not a trivial task. In this paper, we propose a Deadline and Budget Constrained Autoscaling (DBCA) algorithm for addressing the budget-performance tradeoff. We develop an analytical model to quantify the tradeoff and cross-validate the model by extensive simulations. The results show that the DBCA can significantly improve system performance given the budget upper-bound. In addition, the model provides a quick way to evaluate the budget-performance tradeoff and system design without wide deployment, saving on cost and time
ASA: Adaptive VNF Scaling Algorithm for 5G Mobile Networks
5G mobile networks introduce Virtualized Network Functions (VNFs) to provide flexible services for incoming huge mobile data traffic. Compared with fixed capacity legacy network equipment, VNFs can be scaled in/out to adjust system capacity. However, hardware-based legacy network equipment is designed dedicatedly for its purpose so that it is more efficient in terms of unit cost. One challenge is to best use VNF resources and to balance the traffic between legacy network equipment and VNFs. To address this challenge, we first formulate the problem as a cost-performance tradeoff, where both VNF resource cost and system performance are quantified. Then, we propose an adaptive VNF scaling algorithm to balance the tradeoff. We derive the suitable VNF instances to handle data traffic with minimizing cost. Through extensive simulations, the adaptive algorithm is proven to provide good performance
Auto-Scaling Network Resources using Machine Learning to Improve QoS and Reduce Cost
Virtualization of network functions (as virtual routers, virtual firewalls,
etc.) enables network owners to efficiently respond to the increasing
dynamicity of network services. Virtual Network Functions (VNFs) are easy to
deploy, update, monitor, and manage. The number of VNF instances, similar to
generic computing resources in cloud, can be easily scaled based on load.
Hence, auto-scaling (of resources without human intervention) has been
receiving attention. Prior studies on auto-scaling use measured network traffic
load to dynamically react to traffic changes. In this study, we propose a
proactive Machine Learning (ML) based approach to perform auto-scaling of VNFs
in response to dynamic traffic changes. Our proposed ML classifier learns from
past VNF scaling decisions and seasonal/spatial behavior of network traffic
load to generate scaling decisions ahead of time. Compared to existing
approaches for ML-based auto-scaling, our study explores how the properties
(e.g., start-up time) of underlying virtualization technology impacts Quality
of Service (QoS) and cost savings. We consider four different virtualization
technologies: Xen and KVM, based on hypervisor virtualization, and Docker and
LXC, based on container virtualization. Our results show promising accuracy of
the ML classifier using real data collected from a private ISP. We report
in-depth analysis of the learning process (learning-curve analysis), feature
ranking (feature selection, Principal Component Analysis (PCA), etc.), impact
of different sets of features, training time, and testing time. Our results
show how the proposed methods improve QoS and reduce operational cost for
network owners. We also demonstrate a practical use-case example
(Software-Defined Wide Area Network (SD-WAN) with VNFs and backbone network) to
show that our ML methods save significant cost for network service leasers
Performance Modeling of Softwarized Network Services Based on Queuing Theory with Experimental Validation
Network Functions Virtualization facilitates the automation of the scaling of softwarized network services (SNSs).
However, the realization of such a scenario requires a way to
determine the needed amount of resources so that the SNSs performance requisites are met for a given workload. This problem is
known as resource dimensioning, and it can be efficiently tackled
by performance modeling. In this vein, this paper describes an
analytical model based on an open queuing network of G/G/m
queues to evaluate the response time of SNSs. We validate our
model experimentally for a virtualized Mobility Management
Entity (vMME) with a three-tiered architecture running on
a testbed that resembles a typical data center virtualization
environment. We detail the description of our experimental
setup and procedures. We solve our resulting queueing network
by using the Queueing Networks Analyzer (QNA), Jackson’s
networks, and Mean Value Analysis methodologies, and compare
them in terms of estimation error. Results show that, for medium
and high workloads, the QNA method achieves less than half of
error compared to the standard techniques. For low workloads,
the three methods produce an error lower than 10%. Finally,
we show the usefulness of the model for performing the dynamic
provisioning of the vMME experimentally.This work has been partially funded by the H2020 research
and innovation project 5G-CLARITY (Grant No. 871428)National research
project 5G-City: TEC2016-76795-C6-4-RSpanish Ministry of
Education, Culture and Sport (FPU Grant 13/04833). We would also like to
thank the reviewers for their valuable feedback to enhance the quality
and contribution of this wor
Towards Autonomous Computer Networks in Support of Critical Systems
L'abstract è presente nell'allegato / the abstract is in the attachmen