Abstract—Virtual Network Functions (VNFs) are software
implementation of middleboxes (MBs) (e.g., firewalls and proxy
servers) that provide performance and security guarantees for
virtual machine (VM) cloud applications. In this paper, we study
a new VM flow migration problem for dynamic VNF-enabled
cloud data centers (VDCs). The goal is to migrate the VM flows
in the dynamic VDCs to minimize the total network traffic while
load-balancing VNFs with limited processing capabilities. We
refer to the problem as FMDV: flow migration in dynamic VDCs.
We propose an optimal and efficient minimum cost flow-based
flow migration algorithm and two benefit-based efficient heuristic
algorithms to solve the FMDV. Via extensive simulations, we show
that our algorithms are effective in mitigating dynamic cloud
traffic while achieving load balance among VNFs. In particular,
all our algorithms reduce dynamic network traffic in all cases and
our optimal algorithm always achieves the best traffic-mitigation
effect, reducing the network traffic by up to 28% compared to
the case without flow migration.
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Traffic-Optimal Virtual Network Function Placement and Migration in Dynamic Cloud Data Centers
We propose a new algorithmic framework for
traffic-optimal virtual network function (VNF) placement and
migration for policy-preserving data centers (PPDCs). As dy-
namic virtual machine (VM) traffic must traverse a sequence of
VNFs in PPDCs, it generates more network traffic, consumes
higher bandwidth, and causes additional traffic delays than a
traditional data center. We design optimal, approximation, and
heuristic traffic-aware VNF placement and migration algorithms
to minimize the total network traffic in the PPDC. In particular,
we propose the first traffic-aware constant-factor approximation
algorithm for VNF placement, a Pareto-optimal solution for
VNF migration, and a suite of efficient dynamic-programming
(DP)-based heuristics that further improves the approximation
solution. At the core of our framework are two new graph-
theoretical problems that have not been studied. Using flow
characteristics found in production data centers and realistic
traffic patterns, we show that a) our VNF migration techniques
are effective in mitigating dynamic traffic in PPDCs, reducing the
total traffic cost by up to 73%, b) our VNF placement algorithms
yield traffic costs 56% to 64% smaller than those by existing
techniques, and c) our VNF migration algorithms outperform
the state-of-the-art VM migration algorithms by up to 63% in
reducing dynamic network traffic.
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- Award ID(s):
- 1911191
- NSF-PAR ID:
- 10357756
- Date Published:
- Journal Name:
- 36th IEEE International Parallel & Distributed Processing Symposium (IPDPS 2022)
- Page Range / eLocation ID:
- 919 to 929
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IPDPS53621.2022.00094
