Rule Placement Research Articles

Flow-based Service Time optimization in software-defined networks using Deep Reinforcement Learning

A Ternary Content-Addressable Memory (TCAM) constitutes a memory variant employed within Software Defined Networking (SDN) node flow tables. These TCAMs deliver swift processing, enabling rapid parallel lookups. Nonetheless, due to their high energy consumption and cost, TCAMs have limited dimensions. This size constraint influences rule capacity, and suboptimal rule management can degrade network quality of service. Although different techniques for flow table management have been proposed during recent years, such as eviction, idle and hard timeout mechanisms, this paper proposes a Deep-Reinforcement Learning (DRL) solution, namely DRL-Idle, that is able to maximize the service time of the flows in an SDN network without considering any assumption about their status throughout time. By means of a continuous learning, DRL-Idle is able to also minimize the number of rule installations that are required to serve the target flows. Based on the idea of dynamically modifying the idle timeout value of the flows according to their needs, DRL-Idle outperforms existing approaches aiming at solving the Rule Placement Problem, averaging a 30% increase in performance.

Discovering Rules for Rule-Based Machine Learning with the Help of Novelty Search

Automated prediction systems based on machine learning (ML) are employed in practical applications with increasing frequency and stakeholders demand explanations of their decisions. ML algorithms that learn accurate sets of rules, such as learning classifier systems (LCSs), produce transparent and human-readable models by design. However, whether such models can be effectively used, both for predictions and analyses, strongly relies on the optimal placement and selection of rules (in ML this task is known as model selection). In this article, we broaden a previous analysis on a variety of techniques to efficiently place good rules within the search space based on their local prediction errors as well as their generality. This investigation is done within a specific pre-existing LCS, named SupRB, where the placement of rules and the selection of good subsets of rules are strictly separated—in contrast to other LCSs where these tasks sometimes blend. We compare two baselines, random search and (1, lambda )-evolution strategy (ES), with six novelty search variants: three novelty-/fitness weighing variants and for each of those two differing approaches on the usage of the archiving mechanism. We find that random search is not sufficient and sensible criteria, i.e., error and generality, are indeed needed. However, we cannot confirm that the more complicated-to-explain novelty search variants would provide better results than (1, lambda )-ES which allows a good balance between low error and low complexity in the resulting models.

A Review on Efficient Energy Consumption in Software-Defined Networking Using Routing Aware Protocols

The concept of Software-Defined Networking (SDN) has been a fascinating and growing interest in the field of research. The programmable network component is allowed by the SDN’s promising characteristics and partitions the control plane together with the forwarding plane. Energy Efficiency (EE) turned out to be a vital design requisite for modern networking mechanisms since the energy costs supply hugely to the entire costs in networks. Nevertheless, as it is necessary to handle the trade-off betwixt EE and Network Performance (NP), designing energy-effective solutions is non-trivial. Thus, by utilizing Energy-Aware Routing (EAR) approaches, this paper reviews the methodologies of Energy Consumption (EC) on SDN. The latest research related to the traffic-aware solution, compacting TCAM solution, end-host aware solutions, rule placement solutions, heuristic approach-centric solution, and EAR routing protocol was highlighted by this review article in terms of optimal EC on SDN. Finally, centered on the EC metrics, the current research methodologies’ performance is assessed in the performance evaluation. By utilizing EAR routing, this type of research is helpful for future research in efficient EC in SDN.

Data Plane Cooperative Caching With Dependencies

Caching is at the core of most modern communication systems, where caches are used to store content and traffic classification rules. While network components can leverage caching in a cooperative manner, one important aspect of such systems concerns possible dependencies among stored items. A major use case of such dependencies appears in rule placement across software-defined networks (SDNs). Despite the tremendous success of SDNs in datacenters, their wide adoption still poses a key challenge: the packet-forwarding rules in switches require fast and power-hungry memories. Rule tables, which serve as caches, are of limited size in cheap and energy-constrained devices, motivating novel solutions to achieve high hit rates. We leverage device connectivity in the fast data plane, where delays are in the order of few milliseconds, and propose multiple switches to work together to avoid accessing the control plane, where delays are orders of magnitude greater. As a low priority rule in a cache entails caching higher priority rules, we pose the problem of cooperative caching with dependencies. We provide models and algorithms accounting for dependencies among rules implied by existing switch memory types, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lay the foundations of cooperative caching with dependencies</i> .

Flow Management Mechanism in Software-Defined Network

Software-defined networking (SDN) is a paradigm shift in modern networking. However, centralised controller architecture in SDN imposed flow setup overhead issue as the control plane handles all flows regardless of size and priority. Existing frameworks strictly reduce control plane overhead and it does not focus on rule placement of the flows itself. Furthermore, existing frameworks do not focus on managing elephant flows like RTSP. Thus, the proposed mechanism will use the flow statistics gathering method such as random packet sampling to determine elephant flow and microflow via a pre-defined threshold. This mechanism will ensure that the control plane works at an optimum workload because the controller only manages elephant flows via reactive routing and rule placement respectively. Reactive routing has reduced link bandwidth usage below the pre-defined threshold. Furthermore, rule placement has increased average throughput and total transfer to 238%. Meanwhile, the data plane switches will be able to forward microflows via multipath wildcard routing without invoking controller in greater responding time by 85 ms faster in two Transmission Control Protocol (TCP) traffic and achieved 11% and 12% higher total transfer size and throughput respectively. Hence, the controller’s workload reduced significantly to 48% in two TCP traffic.

Dynamic Routing and Failure Recovery Approaches for Efficient Resource Utilization in OpenFlow-SDN: A Survey

Software Defined Networks (SDN) is a new network paradigm that emerged to offer better network management through the separation of network control logic and data forwarding element. This separation speed up network innovation without the need to rely on the vendor-proprietary interface for network element configuration to forward packets. However, SDN is flow driven network, for each arrived flow, a feasible path is computed to forward the flow to its destination. Afterward, the SDN control logic process the corresponding routing and instruct the set of data forwarding elements to install them on their Flowtable to guide the routing process. Unfortunately, the network changes more frequently in dynamic large-scale networks and the Flowtable is a constraint with limited space. These challenges require the SDN controller to compute paths more often which may also need a large number of flow routing rules placement. In addition, the frequency of communication link failures increases lately. The successful deployment of SDN heavily depends on how it satisfies the reliability requirement with uninterrupted services. Several studies were conducted to compute the optimal path for data forward to meet their Quality of Service demand. Other studies focus on reducing the frequency of link failure. Some studies were conducted to manage the constraint Flowtable resources. This survey focuses on Routing rules placement, unoptimized routing, link, and switch load balancing, failure detection, and recovery. The paper extensively discusses each issue and analyzes the weakness of the current solutions. Finally, it highlights potential challenges that need future research attention.

DeepPlace: Deep reinforcement learning for adaptive flow rule placement in Software-Defined IoT Networks

In this paper, we propose a novel and adaptive flow rule placement system based on deep reinforcement learning, namely DeepPlace, in Software-Defined Internet of Things (SDIoT) networks. DeepPlace can provide a fine-grained traffic analysis capability while assuring QoS of traffic flows and proactively avoiding the flow-table overflow issue in the data plane. Specifically, we first investigate the traffic forwarding process in an SDIoT network, i.e., routing and flow rule placement tasks. We design a cost function for the routing to set up traffic flow paths in the data plane. Next, we propose an adaptive flow rule placement approach to maximize the number of match-fields in a flow rule at SDN switches. To deal with the dynamics of IoT traffic flows, we model the system operation by using the Markov decision process (MDP) with a continuous action space and formulate its optimization problem. Subsequently, we develop a deep deterministic policy gradient-based algorithm to help the system obtain the optimal policy. The evaluation results demonstrate that DeepPlace can efficiently maintain a significant number of match-fields in a flow rule, i.e., approximately 86% of the maximum level, while minimizing the QoS violation ratio of traffic flows, i.e., 6.7%, in a highly dynamic traffic scenario, which outperforms three other existing solutions, i.e., FlowMan, FlowStat, and DeepMatch.

Deep Q-Network and Traffic Prediction based Routing Optimization in Software Defined Networks

Software Defined Networking (SDN) is gaining momentum not only in research but also in IT industry representing the drivers of 5G networks, due to its capabilities of increasing the flexibility of a network and address a variety of network challenges, by logically centralizing the intelligence in software-based controllers. Thanks to Machine Learning (ML) techniques, the network performances and utilization can be optimized and enhanced. Neural Networks (NN) and Reinforcement Learning (RL), in particular, have demonstrated great success in cooperating with complex problems arising in network operation and management. To this end, we exploit in this paper, an SDN-based rules placement approach that aims to dynamically predict the traffic congestion by using mainly NN and learn optimal paths and reroute traffic to improve network utilization by deploying a Deep Q-Network (DQN) agent. To this end, we first formulate the Quality-of-Service (QoS)-aware routing problem as a Linear Program (LP), whose objective is to minimize the end-to-end (E2E) delay and link utilization. Then, we propose a simple yet efficient heuristic algorithm to solve it. Numerical results through emulation using ONOS controller and Mininet demonstrate that the proposed approach can significantly improve network performances in terms of decreasing the link utilization, the packet loss and the E2E delay.

ORP: An Online Rule Placement Scheme to Optimize the Traffic Overhead for Data Center Networks

In cloud data centers, the requirement for fine-grained policy leads to a large number of server rules. However, the servers cannot efficiently handle all of these rules due to limitations of the central processing unit (CPU) and memory resources. Thus, a rule partition and placement algorithms have been proposed to divide the original ruleset into several rule subsets and migrate some rule subsets to other devices. Correspondingly, the traffic, which is meant to be blocked by the original server's migratory rules, will also be redirected to the new device. The rule partition and placement algorithms should minimize this extra traffic overhead. The state-of-the-art algorithm, vCRIB, is offline, as its solution is calculated based on a static traffic matrix. In reality, when facing dynamic traffic, the vCRIB solution may actually be suboptimal in terms of traffic overhead. To reduce the traffic overhead, we propose the online rule replacement (ORP) algorithm. Specifically, ORP utilizes the difference between the old and new traffic matrices to discover the rule subsets matching the notably changed traffic overhead or blocked traffic. These rule subsets are then preferentially adjusted to their proper placement. In this manner, ORP can promptly make rule subset adjustments according to the newly updated traffic matrix. Additionally, ORP effectively avoids the invalid migrations existing in vCRIB. The simulation results confirm that ORP is capable of reducing traffic overhead by 26.67%–56.9% compared to vCRIB.

Achieving Fine-Grained Flow Management Through Hybrid Rule Placement in SDNs

Fine-grained flow management is useful in many practical applications, e.g., resource allocation, anomaly detection and traffic engineering. However, it is difficult to provide fine-grained management for a large number of flows in SDNs due to switches’ limited flow table capacity. While using wildcard rules can reduce the number of flow entries needed, it cannot fully ensure fine-grained management for all the flows without degrading application performance. In this article, we design and implement hybrid rule placement for fine-grained flow management (to be referred to as HiFi here after). HiFi achieves fine-grained management with a minimal number of flow entries through taking a two-step approach: wildcard entry installment and application-specific exact-match entry installment. How to optimally install wildcard and exact-match flow entries, however, is intractable. Therefore, we design approximation algorithms with bounded factors to solve these problems. We consider how to achieve network-wide load balancing via fine-grained flow management as a case study. Both experiment on a testbed built with open virtual switches and extensive simulation show that HiFi can reduce the number of required flow entries by about 45-69 percent and reduce the control overhead by about 28-50 percent compared with the state-of-the-art approaches for achieving fine-grained flow management.

A Novel Rule Mapping on TCAM for Power Efficient Packet Classification

Packet Classification is the enabling function performed in commodity switches for providing various services such as access control, intrusion detection, load balancing, and so on. Ternary Content Addressable Memories (TCAMs) are the de facto standard for performing packet classification at high speeds. However, TCAMs are highly costlier both in terms of cost and power consumption, forcing the switch vendors towards placing lots of effort for power management. Hence, power-efficient solutions for TCAM-based packet classification are highly relevant even today. In this article, we propose a novel rule placement algorithm based on the unique field values’ presence within the rule databases. We evaluate the total search that is needed to be inspected with respect to the traditional placement approach and the proposed placement approach based on the information content within the fields. Simulation results showed an average reduction of 30.55% in the search space by the proposed placement approach, thereby resulting in an average reduction of 18.85% per search energy over TCAM. With typical TCAM clock speeds ranging between 200--400MHz, this reduction in the per-search energy maps to a huge reduction in the total energy consumed by the TCAM-based network switches. The proposed solution is plug-and-play type requiring only minimal pre-processing within the Network Processing Unit (NPU) of the switches and edge routers.

A survey of energy efficiency in SDN: Software-based methods and optimization models

Software Defined Networking (SDN) paradigm has the benefits of programmable network elements by separating the control and the forwarding planes, efficiency through optimized routing and flexibility in network management. As the energy costs contribute largely to the overall costs in networks, energy efficiency has become a significant design requirement for modern networking mechanisms. However, designing energy efficient solutions is non-trivial since they need to tackle the trade-off between energy efficiency and network performance. In this article, we address the energy efficiency capabilities that can be utilized in the emerging SDN. We provide a comprehensive and novel classification of software-based energy efficient solutions into subcategories of traffic aware, end system aware and rule placement. We propose general optimization models for each subcategory, and present the objective function, the parameters and constraints to be considered in each model. Detailed information on the characteristics of state-of-the-art methods, their advantages, drawbacks are provided. Hardware-based solutions used to enhance the efficiency of switches are also described. Furthermore, we discuss the open issues and future research directions in the area of energy efficiency in SDN.

A Novel Approach to Rule Placement in Software-Defined Networks Based on OPTree

Software-defined networks (SDNs) are a trend of research in networks. Rule placement, a common SDN operation, becomes a challenging problem due to the capacity limitation of devices in which a large number of rules need to be deployed. Prior works mostly consider rule placement in a single device. However, the position relationships between neighbor devices also have influences on rule placement and should be considered. Our basic idea is to classify the devices position relationships into two categories: the serial relationship and the parallel relationship, and we present novel strategies for rule placement based on the two different position relationships. There are two challenges of implementing our strategies: to check whether a rule is contained by a rule set or not and to check whether a rule can be merged with other rules or not. To handle the challenges, we propose a novel data structure called OPTree to represent the rules, which is convenient to check whether a rule is covered by other rules. We design an insertion algorithm and a search algorithm for OPTree. Extensive experiments show that our approach can effectively reduce the number of rules while ensuring placed rules work. On the other hand, the experimental results also demonstrate that it is necessary to consider the position relationships between neighbor devices when placing rules.

Toward Adaptive and Scalable OpenFlow-SDN Flow Control: A Survey

Software-defined networking (SDN) is an emerging network architecture that promises to simplify network management, improve network resource utilization, and boost evolution and innovation in traditional networks. The SDN allows the abstraction and centralized management of the lower-level network functionalities by decoupling the network logic from the data forwarding devices into the logically centralized distributed controllers. However, this separation introduces new scalability and performance challenges in large-scale networks of dynamic traffic and topology conditions. Many research studies have represented that centralization and maintaining the global network visibility over the distributed SDN controller introduce scalability concern. This paper surveys the state-of-the-art proposed techniques toward minimizing the control to data planes communication overhead and controllers' consistency traffic to enhance the OpenFlow-SDN scalability in the context of logically centralized distributed SDN control plane architecture. The survey mainly focuses on four issues, including logically centralized visibility, link-state discovery, flow rules placement, and controllers' load balancing. In addition, this paper discusses each issue and presents an updated and detailed study of existing solutions and limitations in enhancing the OpenFlow-SDN scalability and performance. Moreover, it outlines the potential challenges that need to be addressed further in obtaining adaptive and scalable OpenFlow-SDN flow control.

Rail Flatness Measurement Method Based on Virtual Rules

This paper deals with the problem of measuring the flatness of rails in the final stage of their manufacturing. The flatness must be measured repeatedly along the total length of the rails. Controlling the flatness of manufactured rails is very important; only rails with very good flatness can be used in high-speed railways. Rails with moderate flatness defects can be used in standard railways, but if the flatness defects are greater than specific tolerances, the rails must undergo a straightening process or be discarded. To measure flatness, we propose a method based on emulation of the manual measurement process: the placement of virtual rules along the entire length of rails. The goal of using virtual rules is to measure potential rail defects. The results indicate which of the rails meets the minimum quality conditions required and which need to be re-manufactured or even discarded. The proposed method is based on an official standard: the European Standard EN-13674-1-2011. An extensive dataset of rails has been measured to test the performance of the proposed method. Results indicate the proposed method is robust and accurate.

Rules Placement with Delay Guarantee in Combined SDN Forwarding Element

Rules Placement with Delay Guarantee in Combined SDN Forwarding Element

Rules Placement Problem in OpenFlow Networks: A Survey

Software-defined networking (SDN) abstracts low-level network functionalities to simplify network management and reduce costs. The OpenFlow protocol implements the SDN concept by abstracting network communications as flows to be processed by network elements. In OpenFlow, the high-level policies are translated into network primitives called rules that are distributed over the network. While the abstraction offered by OpenFlow allows to potentially implement any policy, it raises the new question of how to define the rules and where to place them in the network while respecting all technical and administrative requirements. In this paper, we propose a comprehensive study of the so-called OpenFlow rules placement problem with a survey of the various proposals intending to solve it. Our study is multifold. First, we define the problem and its challenges. Second, we overview the large number of solutions proposed, with a clear distinction between solutions focusing on memory management and those proposing to reduce signaling traffic to ensure scalability. Finally, we discuss potential research directions around the OpenFlow rules placement problem.

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

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.

Tracking the composer’s cognition in the course of a creative process: Stefano Gervasoni and the beginning of Gramigna

Studies of composers’ cognition are restricted by the methodological and epistemological problems with which they are associated. In order to capture the cognitive processes involved in the elaboration of an instrumental piece by Stefano Gervasoni entitled Gramigna (2009-), the authors developed different strategies for composition simulations, which were filmed in order to collect rich and structured data. These methods draw upon the precompositional documents established by the composer over the course of his creative activity, by requesting that he re-enact the operations that generated the documents and the final work. The article focuses on the first movement of Gramigna, composed in a single day, several months before this study began. During a “compositional situation simulation interview”, the composer sat before his manuscript in order to induce his recollection, simulation and verbalisation of the completed creative act. The study’s methodological and epistemological principles (including their advantages and limits) are presented first, followed by a detailed reconstruction of the composer’s action/cognition during the writing of the manuscript. This procedure highlights the sequence of Gervasoni’s significant compositional operations and allows us to categorise and formalise the most salient features of his compositional process. These include the creation of locally applicable construction rules, the placement of concurrent rules, the attention paid to irregularities in the formalistic systems employed and tactics that blur notions of referentiality.

Conflict classification and analysis of distributed firewall policies

Firewalls are core elements in network security. However, managing firewall rules, particularly, in multifirewall enterprise networks, has become a complex and error-prone task. Firewall filtering rules have to be written, ordered, and distributed carefully in order to avoid firewall policy anomalies that might cause network vulnerability. Therefore, inserting or modifying filtering rules in any firewall requires thorough intrafirewall and interfirewall analysis to determine the proper rule placement and ordering in the firewalls. In this paper, we identify all anomalies that could exist in a single- or multifirewall environment. We also present a set of techniques and algorithms to automatically discover policy anomalies in centralized and distributed firewalls. These techniques are implemented in a software tool called the "Firewall Policy Advisor" that simplifies the management of filtering rules and maintains the security of next-generation firewalls.