AHTM: Achieving efficient flow table utilization in Software Defined Networks

Ullah

et al. 2023

Preprint

Software-Defined Networking (SDN) has emerged as an innovative method of networking that offers effective Nanyang Institute of Technology management and remarkable flexibility. However, current SDN-based solutions address networks' static nature or concentrate primarily on backbone networks, where network dynamics have minimal impact. This research presents a new approach, specifically the mobility-aware adaptive flow entry placement scheme for SDN-based Internet of Things (IoT) environments, to address the mobility aspect of networks. The proposed scheme employs the Q-learning algorithm to predict the next possible location of end devices, while the cost-sensitive AdaBoost algorithm is utilized to select heavy and active flows. Consequently, efficient flow rules for incoming flows can be dynamically implemented without the need for intervention from the controllers. Extensive computer simulations demonstrate that this approach significantly enhances match probability and prediction accuracy, while concurrently reducing the number of table misses and resource expenditure compared to existing schemes.

Section: A Flow Evictionmentioning

confidence: 99%

Predictive Mobility and Cost-Aware Flow Placement in SDN-Based IoT Networks: A Q-Learning Approach

Ullah

et al. 2023

Preprint

“…The authors in [35] and [36] use machine learning to tune timeouts for proactive flow rule eviction. And there are many other algorithms that manipulate timeouts to prevent bottlenecks [37], [38], [39], [40]. The authors in [41] utilize bloom filters stored in SRAM to track the importance of existing flow rules.…”

Section: A Flow Rule Evictionmentioning

confidence: 99%

An Optimization-based Approach for Flow Table Capacity Bottleneck Mitigation in Software-Defined Networks

Bauer,

Zitterbart

2021

Preprint

Flow delegation is a flexible technique to mitigate flow table capacity bottlenecks in Software-defined Networks (SDN). Such bottlenecks occur when SDN switches provide insufficient flow table capacity which leads to performance degradation and network failures. Flow delegation addresses this problem by automatically relocating flow rules from a bottlenecked switch to neighboring switches with spare capacity. This paper introduces a new algorithm to efficiently perform flow delegation based on a novel delegation template abstraction and multi-period multi-objective optimization. Different from existing work, our approach can include estimated knowledge about future network situations and deal with different optimization criteria such as link and control overhead. We discuss the problem decomposition for the new algorithm and introduce an efficient two-step heuristic. Results show, that our approach performs significantly better than the simple greedy algorithm used in earlier work and is capable of handling flow delegation for networks with hundreds of switches.

“…Open flow configures flow table entries with idle and hard timeout across the flows [9]. These timeouts are used to control the life span of flow entries in the switch flow table.…”

Section: Timeout Mechanismmentioning

confidence: 99%

“…An idle timeout removes the correspondent entries after an inactive period with no packet to match that entry, while a hard timeout removes the corresponding flow entry after a deterministic lifetime of the entry, regardless of whether it has been matched or otherwise. In the conventional method, the SDN controller configured flow entries with the fixed idle or hard timeout value across the flows without considering the variabilities exhibited by the flows and usage of the flow table [9]. The work of [7,9] is an example of such a strategy under a specific condition and application, respectively.…”

Section: Timeout Mechanismmentioning

confidence: 99%

“…In the conventional method, the SDN controller configured flow entries with the fixed idle or hard timeout value across the flows without considering the variabilities exhibited by the flows and usage of the flow table [9]. The work of [7,9] is an example of such a strategy under a specific condition and application, respectively. This method could be effective with better performance when the traffic flows are fixed and flow table space is sufficient.…”

Section: Timeout Mechanismmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive and Hybrid Idle–Hard Timeout Allocation and Flow Eviction Mechanism Considering Traffic Characteristics

et al. 2020

Software-defined networking (SDN) enables flexible fine-grained networking policies by allowing the SDN controller to install packet handling rules on distributed switches. The behaviour of SDN depends on the set of forwarding entries installed at the switch flow table. The increasing number of traffics from the proliferation of the Internet of Thing (IoT) devices increase the processing load on the controller and generates an additional number of entries stored in the flow table. However, the switch flow table memory (TCAM) cannot accommodate many entries. Packets from multimedia flows are usually large in size and thus suffer processing delay and require more flow set up requests. The SDN controller may be overloaded and face some scalability problems because it supports a limited number of requests from switches. OpenFlow uses timeout configuration to manage flow setup request. The conventional fixed timeout cannot cope up with the dynamic nature of traffic flows. This paper controls the frequent flow setup requests by proposing an adaptive and hybrid idle–hard timeout allocation (AH-IHTA). The algorithm considers traffic patterns, flow table usage ratio, and returns appropriate the timeout to different flows. The performance evaluations conducted have shown a 28% and 39% reduction in the flow setup request and flow eviction, respectively.