1,200 research outputs found
Event-Driven Network Programming
Software-defined networking (SDN) programs must simultaneously describe
static forwarding behavior and dynamic updates in response to events.
Event-driven updates are critical to get right, but difficult to implement
correctly due to the high degree of concurrency in networks. Existing SDN
platforms offer weak guarantees that can break application invariants, leading
to problems such as dropped packets, degraded performance, security violations,
etc. This paper introduces EVENT-DRIVEN CONSISTENT UPDATES that are guaranteed
to preserve well-defined behaviors when transitioning between configurations in
response to events. We propose NETWORK EVENT STRUCTURES (NESs) to model
constraints on updates, such as which events can be enabled simultaneously and
causal dependencies between events. We define an extension of the NetKAT
language with mutable state, give semantics to stateful programs using NESs,
and discuss provably-correct strategies for implementing NESs in SDNs. Finally,
we evaluate our approach empirically, demonstrating that it gives well-defined
consistency guarantees while avoiding expensive synchronization and packet
buffering
A Fast Compiler for NetKAT
High-level programming languages play a key role in a growing number of
networking platforms, streamlining application development and enabling precise
formal reasoning about network behavior. Unfortunately, current compilers only
handle "local" programs that specify behavior in terms of hop-by-hop forwarding
behavior, or modest extensions such as simple paths. To encode richer "global"
behaviors, programmers must add extra state -- something that is tricky to get
right and makes programs harder to write and maintain. Making matters worse,
existing compilers can take tens of minutes to generate the forwarding state
for the network, even on relatively small inputs. This forces programmers to
waste time working around performance issues or even revert to using
hardware-level APIs.
This paper presents a new compiler for the NetKAT language that handles rich
features including regular paths and virtual networks, and yet is several
orders of magnitude faster than previous compilers. The compiler uses symbolic
automata to calculate the extra state needed to implement "global" programs,
and an intermediate representation based on binary decision diagrams to
dramatically improve performance. We describe the design and implementation of
three essential compiler stages: from virtual programs (which specify behavior
in terms of virtual topologies) to global programs (which specify network-wide
behavior in terms of physical topologies), from global programs to local
programs (which specify behavior in terms of single-switch behavior), and from
local programs to hardware-level forwarding tables. We present results from
experiments on real-world benchmarks that quantify performance in terms of
compilation time and forwarding table size
SDN management layer: design requirements and future direction
Computer networks are becoming more and more complex and difficult to manage. The research community has been expending a lot of efforts to come up with a general management paradigm that is able to hide the details of the physical infrastructure and enable flexible network management. Software Defined Networking (SDN) is such a paradigm that simplifies network management and enables network innovations. In this survey paper, by reviewing existing SDN management layers (platforms), we identify the general common management architecture for SDN networks, and further identify the design requirements of the management layer that is at the core of the architecture. We also point out open issues and weaknesses of existing SDN management layers. We conclude with a promising future direction for improving the SDN management layer.This work is supported in part by the National Science Foundation (NSF grant CNS-0963974)
DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDN
The optimal multicast tree problem in the Software-Defined Networking (SDN)
multicast routing is an NP-hard combinatorial optimization problem. Although
existing SDN intelligent solution methods, which are based on deep
reinforcement learning, can dynamically adapt to complex network link state
changes, these methods are plagued by problems such as redundant branches,
large action space, and slow agent convergence. In this paper, an SDN
intelligent multicast routing algorithm based on deep hierarchical
reinforcement learning is proposed to circumvent the aforementioned problems.
First, the multicast tree construction problem is decomposed into two
sub-problems: the fork node selection problem and the construction of the
optimal path from the fork node to the destination node. Second, based on the
information characteristics of SDN global network perception, the multicast
tree state matrix, link bandwidth matrix, link delay matrix, link packet loss
rate matrix, and sub-goal matrix are designed as the state space of intrinsic
and meta controllers. Then, in order to mitigate the excessive action space,
our approach constructs different action spaces at the upper and lower levels.
The meta-controller generates an action space using network nodes to select the
fork node, and the intrinsic controller uses the adjacent edges of the current
node as its action space, thus implementing four different action selection
strategies in the construction of the multicast tree. To facilitate the
intelligent agent in constructing the optimal multicast tree with greater
speed, we developed alternative reward strategies that distinguish between
single-step node actions and multi-step actions towards multiple destination
nodes
Blockchain-Based Transaction Validation Protocol for a Secure Distributed IoT Network
Funding Agency: 10.13039/501100010418-Institute for Information and Communications Technology Promotion (IITP), Ministry of Science and ICT (MSIT); 10.13039/501100003621-Korea Government;Peer reviewedPublisher PD
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