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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Comparative Synthesis: Learning Near-Optimal Network Designs by Query
When managing wide-area networks, network architects must decide how to balance multiple conflicting metrics, and ensure fair allocations to competing traffic while prioritizing critical traffic. The state of practice poses challenges since architects must precisely encode their intent into formal optimization models using abstract notions such as utility functions, and ad-hoc manually tuned knobs. In this paper, we present the first effort to synthesize optimal network designs with indeterminate objectives using an interactive program-synthesis-based approach. We make three contributions. First, we present comparative synthesis, an interactive synthesis framework which produces near-optimal programs (network designs) through two kinds of queries (Validate and Compare), without an objective explicitly given. Second, we develop the first learning algorithm for comparative synthesis in which a voting-guided learner picks the most informative query in each iteration. We present theoretical analysis of the convergence rate of the algorithm. Third, we implemented Net10Q, a system based on our approach, and demonstrate its effectiveness on four real-world network case studies using black-box oracles and simulation experiments, as well as a pilot user study comprising network researchers and practitioners. Both theoretical and experimental results show the promise of our approach.
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- NSF-PAR ID:
- 10398333
- Date Published:
- Journal Name:
- Proceedings of the ACM on Programming Languages
- Volume:
- 7
- Issue:
- POPL
- ISSN:
- 2475-1421
- Page Range / eLocation ID:
- 91 to 120
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3571197
