519 research outputs found
Distributed VNF Scaling in Large-scale Datacenters: An ADMM-based Approach
Network Functions Virtualization (NFV) is a promising network architecture
where network functions are virtualized and decoupled from proprietary
hardware. In modern datacenters, user network traffic requires a set of Virtual
Network Functions (VNFs) as a service chain to process traffic demands. Traffic
fluctuations in Large-scale DataCenters (LDCs) could result in overload and
underload phenomena in service chains. In this paper, we propose a distributed
approach based on Alternating Direction Method of Multipliers (ADMM) to jointly
load balance the traffic and horizontally scale up and down VNFs in LDCs with
minimum deployment and forwarding costs. Initially we formulate the targeted
optimization problem as a Mixed Integer Linear Programming (MILP) model, which
is NP-complete. Secondly, we relax it into two Linear Programming (LP) models
to cope with over and underloaded service chains. In the case of small or
medium size datacenters, LP models could be run in a central fashion with a low
time complexity. However, in LDCs, increasing the number of LP variables
results in additional time consumption in the central algorithm. To mitigate
this, our study proposes a distributed approach based on ADMM. The
effectiveness of the proposed mechanism is validated in different scenarios.Comment: IEEE International Conference on Communication Technology (ICCT),
Chengdu, China, 201
Enabling Scalable and Sustainable Softwarized 5G Environments
The fifth generation of telecommunication systems (5G) is foreseen to play a fundamental
role in our socio-economic growth by supporting various and radically new vertical
applications (such as Industry 4.0, eHealth, Smart Cities/Electrical Grids, to name
a few), as a one-fits-all technology that is enabled by emerging softwarization solutions
\u2013 specifically, the Fog, Multi-access Edge Computing (MEC), Network Functions Virtualization
(NFV) and Software-Defined Networking (SDN) paradigms. Notwithstanding
the notable potential of the aforementioned technologies, a number of open issues
still need to be addressed to ensure their complete rollout. This thesis is particularly developed
towards addressing the scalability and sustainability issues in softwarized 5G
environments through contributions in three research axes: a) Infrastructure Modeling
and Analytics, b) Network Slicing and Mobility Management, and c) Network/Services Management
and Control. The main contributions include a model-based analytics approach
for real-time workload profiling and estimation of network key performance indicators
(KPIs) in NFV infrastructures (NFVIs), as well as a SDN-based multi-clustering approach
to scale geo-distributed virtual tenant networks (VTNs) and to support seamless
user/service mobility; building on these, solutions to the problems of resource consolidation,
service migration, and load balancing are also developed in the context of 5G.
All in all, this generally entails the adoption of Stochastic Models, Mathematical Programming,
Queueing Theory, Graph Theory and Team Theory principles, in the context
of Green Networking, NFV and SDN
An Algorithm for Network and Data-aware Placement of Multi-Tier Applications in Cloud Data Centers
Today's Cloud applications are dominated by composite applications comprising
multiple computing and data components with strong communication correlations
among them. Although Cloud providers are deploying large number of computing
and storage devices to address the ever increasing demand for computing and
storage resources, network resource demands are emerging as one of the key
areas of performance bottleneck. This paper addresses network-aware placement
of virtual components (computing and data) of multi-tier applications in data
centers and formally defines the placement as an optimization problem. The
simultaneous placement of Virtual Machines and data blocks aims at reducing the
network overhead of the data center network infrastructure. A greedy heuristic
is proposed for the on-demand application components placement that localizes
network traffic in the data center interconnect. Such optimization helps
reducing communication overhead in upper layer network switches that will
eventually reduce the overall traffic volume across the data center. This, in
turn, will help reducing packet transmission delay, increasing network
performance, and minimizing the energy consumption of network components.
Experimental results demonstrate performance superiority of the proposed
algorithm over other approaches where it outperforms the state-of-the-art
network-aware application placement algorithm across all performance metrics by
reducing the average network cost up to 67% and network usage at core switches
up to 84%, as well as increasing the average number of application deployments
up to 18%.Comment: Submitted for publication consideration for the Journal of Network
and Computer Applications (JNCA). Total page: 28. Number of figures: 15
figure
Temporal Bandwidth-Intensive Virtual Network Allocation Optimization in Data Centers
Title from PDF of title page, viewed on July 15, 2015Thesis advisor: Deep MedhiVitaIncludes bibliographic references (pages 30-31)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2014In this paper, we consider bandwidth-intensive services for customers that want
virtual networks (VN) in a data center environment. In particular, we consider this problem
in a temporal context where bandwidth-intensive requests from each VN may arrive
randomly at a review point, which may last for a certain duration. Thus, at each review
point, the data center network provider must optimally allocate resources for the demand
requests. For this problem, we present a mixed-integer programming (MIP) problem formulation
where any request from a VN customer may be assigned to any virtual machine
so that network resource availability is optimized. We present an overbooking strategy
that may be employed to allow for some demands not met in the first try. For comparison,
we also consider a base case where the allocation is pinned to a specific destination.
Through our study, we show the comparative gains of different schemesIntroduction -- Literature survey -- Model -- Results -- Conclusions -- Appendix A. Node numbering -- Appendix B. NetworkSimulation
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