Optimizing Data Plane Resources for Multipath Flows

Nakibly, Gabi; Cohen, Reuven; Katzir, Liran

doi:10.1109/tnet.2013.2292895

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…The allocation of VDCs is similar to GraphMap, and GraphMap has been applied to the resource allocation in data center network in many other research studies . Some research work also brings the software defined network (SDN) to the resource allocation management …”

Section: Related Workmentioning

confidence: 98%

“…Step 5: Node b receives the message from node c, constructs the greedy partial solution 30 > } with the context ''a = 1,'' and then it sends the partial solution to node a.…”

Section: Example Of Workload Mapping Problemmentioning

confidence: 99%

“…17,22,26,27 Some research work also brings the software defined network (SDN) to the resource allocation management. [28][29][30] The main difference between the proposed algorithms and the existing algorithms is that the former can ensure the minimum cost of mapping the workload to substrate network, while the latter do not consider the issue, they are able to find a feasible solution, without guaranteeing the optimality of the mapping results.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐resource workload mapping with minimum cost in cloud environment

Tan

et al. 2019

Concurrency and Computation

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Summary Workload mapping in cloud environment refers to map multiple workloads provided by the cloud users/tenants to the substrate network provided by the cloud providers, which is a NP‐hard problem. The workload is a service demand made to the cloud, which is modeled as a logical network consists of virtual nodes and virtual links. Substrate network is a physical network consists of physical nodes that are inter‐connected via communication links. Devising heuristic methods has become the mainstream of workload mapping problem, which can obtain a feasible solution, but the quality of the solution is not guaranteed. Pointing to this issue, this paper takes the mapping cost of the workloads as the solving objective, and models the workload mapping as a constraint optimization problem. Based on the constraint optimization model, we devise two algorithms to solve the problem. These algorithms can not only find the feasible solution, but also ensure the solution is optimal. Lastly, we have demonstrated the optimality of the proposed algorithms through theoretical proof and evaluated the performance of them through simulation experiment.

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Section: Related Workmentioning

confidence: 98%

“…Step 5: Node b receives the message from node c, constructs the greedy partial solution 30 > } with the context ''a = 1,'' and then it sends the partial solution to node a.…”

Section: Example Of Workload Mapping Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi‐resource workload mapping with minimum cost in cloud environment

Tan

et al. 2019

Concurrency and Computation

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“…A system called MicroTE is developed to adapt to traffic variations by leveraging the short term and partial predictability of the traffic matrix. Nakibly et al [24] have studied a problem of splitting traffic flow over multiple efficient paths to improve the network bandwidth utilization. However, using multiple paths for a traffic flow will increase the consumption of expensive forwarding resources, such as TCAM entries of switches and wavelengths of optical switches.…”

Section: Multi-path Routing Considering Noncp and Cpmentioning

confidence: 99%

Joint Optimization of Rule Placement and Traffic Engineering for QoS Provisioning in Software Defined Network

Huang

Guo

et al. 2015

IEEE Trans. Comput.

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Software-Defined Network (SDN) is a promising network paradigm that separates the control plane and data plane in the network. It has shown great advantages in simplifying network management such that new functions can be easily supported without physical access to the network switches. However, Ternary Content Addressable Memory (TCAM), as a critical hardware storing rules for high-speed packet processing in SDN-enabled devices, can be supplied to each device with very limited quantity because it is expensive and energy-consuming. To efficiently use TCAM resources, we propose a rule multiplexing scheme, in which the same set of rules deployed on each node apply to the whole flow of a session going through but towards different paths. Based on this scheme, we study the rule placement problem with the objective of minimizing rule space occupation for multiple unicast sessions under QoS constraints. We formulate the optimization problem jointly considering routing engineering and rule placement under both existing and our rule multiplexing schemes. Via an extensive review of the state-of-the-art work, to the best of our knowledge, we are the first to study the non-routing-rule placement problem. Finally, extensive simulations are conducted to show that our proposals significantly outperform existing solutions.

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“…I N many applications, in order to practically use an established network flow, it is required to decompose the flow into a set of paths, each of which carries a certain amount of flow. For example, this problem arises in multiple genome assembly [1], network traffic engineering [2], and multiprotocol label switching [3]. Since each path in the decomposition requires additional resources (e.g., entries in router tables) or represents additional complexity (non-parsimony) of the solution, one frequently seeks a decomposition with minimized number of paths.…”

Section: Introductionmentioning

confidence: 99%

Theory and A Heuristic for the Minimum Path Flow Decomposition Problem

Shao

Kingsford

2019

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Motivated by multiple genome assembly problems and other applications, we study the following minimum path flow decomposition problem: given a directed acyclic graph with source and sink and a flow , compute a set of paths and assign weight for such that , and is minimized. We develop some fundamental theory for this problem, upon which we design an efficient heuristic. Specifically, we prove that the gap between the optimal number of paths and a known upper bound is determined by the nontrivial equations within the flow values. This result gives rise to the framework of our heuristic: to iteratively reduce the gap through identifying such equations. We also define an operation on certain independent substructures of the graph, and prove that this operation does not affect the optimality but can transform the graph into one with desired property that facilitates reducing the gap. We apply and test our algorithm on both simulated random instances and perfect splice graph instances, and also compare it with the existing state-of-art algorithm for flow decomposition. The results illustrate that our algorithm can achieve very high accuracy on these instances, and also that our algorithm significantly improves on the previous algorithms. An implementation of our algorithm is freely available at https://github.com/Kingsford-Group/catfish.

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Optimizing Data Plane Resources for Multipath Flows

Cited by 8 publications

References 31 publications

Multi‐resource workload mapping with minimum cost in cloud environment

Multi‐resource workload mapping with minimum cost in cloud environment

Joint Optimization of Rule Placement and Traffic Engineering for QoS Provisioning in Software Defined Network

Theory and A Heuristic for the Minimum Path Flow Decomposition Problem

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