2,965 research outputs found
Joint Energy Efficient and QoS-aware Path Allocation and VNF Placement for Service Function Chaining
Service Function Chaining (SFC) allows the forwarding of a traffic flow along
a chain of Virtual Network Functions (VNFs, e.g., IDS, firewall, and NAT).
Software Defined Networking (SDN) solutions can be used to support SFC reducing
the management complexity and the operational costs. One of the most critical
issues for the service and network providers is the reduction of energy
consumption, which should be achieved without impact to the quality of
services. In this paper, we propose a novel resource (re)allocation
architecture which enables energy-aware SFC for SDN-based networks. To this
end, we model the problems of VNF placement, allocation of VNFs to flows, and
flow routing as optimization problems. Thereafter, heuristic algorithms are
proposed for the different optimization problems, in order find near-optimal
solutions in acceptable times. The performance of the proposed algorithms are
numerically evaluated over a real-world topology and various network traffic
patterns. The results confirm that the proposed heuristic algorithms provide
near optimal solutions while their execution time is applicable for real-life
networks.Comment: Extended version of submitted paper - v7 - July 201
Specifying and Placing Chains of Virtual Network Functions
Network appliances perform different functions on network flows and
constitute an important part of an operator's network. Normally, a set of
chained network functions process network flows. Following the trend of
virtualization of networks, virtualization of the network functions has also
become a topic of interest. We define a model for formalizing the chaining of
network functions using a context-free language. We process deployment requests
and construct virtual network function graphs that can be mapped to the
network. We describe the mapping as a Mixed Integer Quadratically Constrained
Program (MIQCP) for finding the placement of the network functions and chaining
them together considering the limited network resources and requirements of the
functions. We have performed a Pareto set analysis to investigate the possible
trade-offs between different optimization objectives
Algorithms for advance bandwidth reservation in media production networks
Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results
Impact of Processing-Resource Sharing on the Placement of Chained Virtual Network Functions
Network Function Virtualization (NFV) provides higher flexibility for network
operators and reduces the complexity in network service deployment. Using NFV,
Virtual Network Functions (VNF) can be located in various network nodes and
chained together in a Service Function Chain (SFC) to provide a specific
service. Consolidating multiple VNFs in a smaller number of locations would
allow decreasing capital expenditures. However, excessive consolidation of VNFs
might cause additional latency penalties due to processing-resource sharing,
and this is undesirable, as SFCs are bounded by service-specific latency
requirements. In this paper, we identify two different types of penalties
(referred as "costs") related to the processingresource sharing among multiple
VNFs: the context switching costs and the upscaling costs. Context switching
costs arise when multiple CPU processes (e.g., supporting different VNFs) share
the same CPU and thus repeated loading/saving of their context is required.
Upscaling costs are incurred by VNFs requiring multi-core implementations,
since they suffer a penalty due to the load-balancing needs among CPU cores.
These costs affect how the chained VNFs are placed in the network to meet the
performance requirement of the SFCs. We evaluate their impact while considering
SFCs with different bandwidth and latency requirements in a scenario of VNF
consolidation.Comment: Accepted for publication in IEEE Transactions on Cloud Computin
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
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