Timeout Value Research Articles

МАТЕМАТИЧЕСКАЯ МОДЕЛЬ ПРОЦЕССА ДОСТАВКИ ТЕЛЕМЕТРИЧЕСКИХ СООБЩЕНИЙ В СИСТЕМЕ МЕДИЦИНСКОГО ИНТЕРНЕТА ВЕЩЕЙ

Currently, research and development are relevant aimed at increasing the efficiency of transmission of telemetry messages in wireless remote monitoring systems used by medical workers to provide timely assistance to patients under observation. The technical basis for transmitting such messages is provided by medical Internet of Things systems. The article is devoted to the development of a mathematical model of the process of delivering telemetric messages in such a system. In the course of the study, analytical expressions were obtained that make it possible to estimate the average delivery time of messages from the patient’s sensory device to the healthcare worker’s wireless device, depending on the response intervals of the retransmission timers installed in the transmitting and receiving elements of the system. The results obtained allow us to theoretically substantiate the choice of retransmission timeout values that ensure minimization of the average message delivery time at the current level of bit error intensity.

Efficient handling of ACL policy change in SDN using reactive and proactive flow rule installation

Software-defined networking (SDN) is a pioneering network paradigm that strategically decouples the control plane from the data and management planes, thereby streamlining network administration. SDN's centralized network management makes configuring access control list (ACL) policies easier, which is important as these policies frequently change due to network application needs and topology modifications. Consequently, this action may trigger modifications at the SDN controller. In response, the controller performs computational tasks to generate updated flow rules in accordance with modified ACL policies and installs flow rules at the data plane. Existing research has investigated reactive flow rules installation that changes in ACL policies result in packet violations and network inefficiencies. Network management becomes difficult due to deleting inconsistent flow rules and computing new flow rules per modified ACL policies. The proposed solution efficiently handles ACL policy change phenomena by automatically detecting ACL policy change and accordingly detecting and deleting inconsistent flow rules along with the caching at the controller and adding new flow rules at the data plane. A comprehensive analysis of both proactive and reactive mechanisms in SDN is carried out to achieve this. To facilitate the evaluation of these mechanisms, the ACL policies are modeled using a 5-tuple structure comprising Source, Destination, Protocol, Ports, and Action. The resulting policies are then translated into a policy implementation file and transmitted to the controller. Subsequently, the controller utilizes the network topology and the ACL policies to calculate the necessary flow rules and caches these flow rules in hash table in addition to installing them at the switches. The proposed solution is simulated in Mininet Emulator using a set of ACL policies, hosts, and switches. The results are presented by varying the ACL policy at different time instances, inter-packet delay and flow timeout value. The simulation results show that the reactive flow rule installation performs better than the proactive mechanism with respect to network throughput, packet violations, successful packet delivery, normalized overhead, policy change detection time and end-to-end delay. The proposed solution, designed to be directly used on SDN controllers that support the Pyretic language, provides a flexible and efficient approach for flow rule installation. The proposed mechanism can be employed to facilitate network administrators in implementing ACL policies. It may also be integrated with network monitoring and debugging tools to analyze the effectiveness of the policy change mechanism.

Adaptive Flow Timeout Management in Software-Defined Optical Networks

Current trends in network traffic management rely on the efficient control of individual flows. Software-defined networking popularized this notion. Per-flow management is perfectly viable in standard IP networks, in which packet processing is in the electric domain. However, optical networks provide more restrictions and constraints making per-flow traffic management difficult. One of the most important challenges is to reduce the concurrent number of flows present in the flow tables to make the switching process quicker. In this paper, we propose a mechanism to manage flow timeout values that uses idle timeout and hard timeout parameters. To calculate the appropriate values of the parameters, the mechanism analyzes the packet inter-arrival times. The algorithm also takes into account the current occupancy of the flow table.

DDT: A Reinforcement Learning Approach to Dynamic Flow Timeout Assignment in Software Defined Networks

OpenFlow-compliant commodity switches face challenges in efficiently managing flow rules due to the limited capacity of expensive high-speed memories used to store them. The accumulation of inactive flows can disrupt ongoing communication, necessitating an optimized approach to flow rule timeouts. This paper proposes Delayed Dynamic Timeout (DDT), a Reinforcement Learning-based approach to dynamically adjust flow rule timeouts and enhance the utilization of a switch’s flow table(s) for improved efficiency. Despite the dynamic nature of network traffic, our DDT algorithm leverages advancements in Reinforcement Learning algorithms to adapt and achieve flow-specific optimization objectives. The evaluation results demonstrate that DDT outperforms static timeout values in terms of both flow rule match rate and flow rule activity. By continuously adapting to changing network conditions, DDT showcases the potential of Reinforcement Learning algorithms to effectively optimize flow rule management. This research contributes to the advancement of flow rule optimization techniques and highlights the feasibility of applying Reinforcement Learning in the context of SDN.

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.

Flow Table Saturation Attack against Dynamic Timeout Mechanisms in SDN

Software-defined networking (SDN) enables dynamic management and flexible network control by employing reactive rule installation. Due to high power consumption and cost, current OpenFlow switches only support a limited number of flow rules, which is a major limitation for deploying massive fine-grained policies. This bottleneck can be exploited by attackers to launch saturation attacks to overflow the flow table. Moreover, flow table overflow can occur in the absence of malicious attackers. To cope with this, researchers have developed many proposals to relieve the load under benign conditions. Among them, the dynamic timeout mechanism is one of the most effective solutions. We notice that when the SDN controller adopts dynamic timeouts, existing flow table saturation attacks can fail, or even expose the attackers, due to inaccurate inferring results. In this paper, we extract the common features of dynamic timeout strategies and propose an advanced flow table saturation attack. We explore the definition of flow rule lifetime and use a timing-based side-channel to infer the timeout of flow rules. Moreover, we leverage the dynamic timeout mechanisms to proactively interfere with the decision of timeout values and perform an attack. We conduct extensive experiments in various settings to demonstrate its effectiveness. We also notice that some replacement strategies work differently when the controller assigns dynamic timeouts. The experiment results show that the attack can incur significant network performance degradation and carry out the attack in a stealthy manner.

Network fingerprinting via timing attacks and defense in software defined networks

Software-Defined Networking (SDN) is becoming a native networking model for next generation networks. However, with its decoupled architecture, SDN is susceptible to reconnaissance through time inference attacks. Attackers can use probing based measurements and gather information such as network type and flow table size. In this paper, an automated attacker tool called RAFA is proposed to infer network type (SDN or traditional) and flow rule timeout values (hard and idle). Moreover, a lightweight defense mechanism to randomize rule timeouts with respect to network status is described. A comprehensive simulation setup with different network conditions shows that the proposed methods achieve superior success rate in diverse settings.

On Packet Reordering in Time-Sensitive Networks

Time-sensitive networks (IEEE TSN or IETF DetNet) may tolerate some packet reordering. Re-sequencing buffers are then used to provide in-order delivery, the parameters of which (timeout, buffer size) may affect worst-case delay and delay jitter. There is so far no precise understanding of per-flow reordering metrics nor of the dimensioning of re-sequencing buffers in order to provide worst-case guarantees, as required in such networks. First, we show that a previously proposed per-flow metric, reordering late time offset (RTO), determines the timeout value. If the network is lossless, another previously defined metric, the reordering byte offset (RBO), determines the required buffer. If packet losses cannot be ignored, the required buffer may be larger than RBO, and depends on jitter, an arrival curve of the flow at its source, and the timeout. Then we develop a calculus to compute the RTO for a flow path; the method uses a novel relation with jitter and arrival curve, together with a decomposition of the path into non order-preserving and order-preserving elements. We also analyse the effect of re-sequencing buffers on worst-case delay, jitter and propagation of arrival curves. We show in particular that, in a lossless (but non order-preserving) network, re-sequencing is “for free”, namely, it does not increase worst-case delay nor jitter, whereas in a lossy network, re-sequencing increases the worst-case delay and jitter. We apply the analysis to evaluate the performance impact of placing re-sequencing buffers at intermediate points and illustrate the results on two industrial test cases.

Cost‐effective and accurate flow statistics collection in OpenFlow‐based SDN

SummaryNetwork resource scheduling and optimization require the acquisition of status information as a basis. High‐cost solutions lead to more resource consumption but only bring negligible benefits. To address this challenge, this paper proposes a novel statistics collection method adapted to OpenFlow‐based SDN, which can reduce the measurement cost while ensuring the statistical accuracy. First, based on the complex network theory, we propose multi‐path weighted closeness centrality (MWCC) to perform importance ranking on network switching nodes, which helps us select top‐k key nodes for statistical collection to reduce the overhead. Second, we propose an adaptive flow rule timeout mechanism AFRT. AFRT continuously optimizes the rule timeout values based on statistical results, further balancing flow table overhead and statistical accuracy. A series of simulation results on real network topologies verify the superiority of the proposed method in terms of communication cost, statistical accuracy, and time consumption, compared with the existing representative methods.

Flex: A flowlet-level load balancing based on load-adaptive timeout in DCN

Load balancing mechanisms spread traffic across multiple paths to achieve high throughput and low latency in data center networks. Flowlet switching has been proven to be a powerful technique for fine-grained load balancing. Existing flowlet-level schemes detect flowlets based on a fixed timeout. However, the fixed timeout is configured empirically: setting it to an excessively large value leads to fewer flowlet opportunities and low link utilization, while setting it to an insufficient value will risk severe problematic disorder. To cope with this problem, we propose Flex, a flowlet-level load balancing scheme based on load-adaptive flowlet timeout. Flex maintains a separate flowlet timeout for each flow, and dynamically updates the timeout value during the transmission of the flow to adapt to changes in path condition. The flow is split into flowlets at the host based on the adaptive timeout. To pass the flowlet results to the switch, Flex marks the adjacent flowlets of the same flow via one bit of the reserved field in TCP header. Experimental results demonstrate that Flex can achieve performance close to that of LetFlow in the baseline topology, and Flex performs particularly well when handling asymmetry and bursty traffic.

Edge Workloads Monitoring and Failover: a StarlingX-Based Testbed Implementation and Measurement Study

With the ever-growing amount of time-critical, compute-intensive, and private IoT applications, the need for High Availability (HA) Edge Clouds becomes indispensable. Realizing HA Edge Clouds is inherently challenging due to the geographically-dispersed hierarchy of the Distributed Cloud Infrastructure (DCI). For example, frequent isolation between the central Cloud and Edge Clouds due to networking instability necessitates some autonomous operations at the Edge Clouds. Furthermore, because Edge Clouds have fewer resources than central Clouds, configuring the Edge functions (i.e., control, compute, and storage) in HA clusters will undoubtedly reduce downtime. However, it will limit the Edge scalability. To that end, StarlingX is developing an HA-protected and scalable DCI virtualization platform based on the open-source ecosystem, focusing on low-touch management of Edge Clouds. StarlingX provides a fault management service that realizes DCI-wide alarming and logging capabilities, allowing for rapid response to virtualized infrastructure events. Recently, the IETF Network Working Group proposed that monitoring both the DCI and the Edge workloads (software containers) is critical for an Edge Computing Platform to maintain HA IoT application deployment. Indeed, the possibility of the infrastructure remaining stable and healthy while the workloads suffer a fatal failure simultaneously necessitates failover functionality that monitors both the infrastructure and the Edge workloads. In this paper, we first propose a dynamic failover functionality that centrally monitors Edge workloads to recover from deployment or Edge node failures, motivated by the IETF direction. Second, we experimentally optimize the failover functionality for monitoring a microservice-architected IoT application deployed on a StarlingX-based DCI testbed to collect temperature sensor readings from Raspberry Pis. Regardless of how quickly the Edge workload health checks are collected, the recorded failover measurements reveal that the recovery time will not drop below a predetermined level controlled by Edge resources and network speed. Furthermore, reducing the statistics collection timeout reduces the recovery time of an Edge node failure. When the timeout value is less than the minimum achievable recovery time, false-positive failures (FPFs) can occur. Third, to supplement the StarlingX fault management service, we provide a modular implementation of the proposed failover functionality. Finally, we present the first-ever introduction of the StarlingX platform’s software stack to promote its use in academic research.

Orchestrator for Synchronizing Network Events in SDNs

In Software-defined networks (SDNs), synchronization among network devices is an emergent phenomenon for enhancing the network scalability while preserving the centralized control. Owing to the fixed timeout values attributed to flow rules by the controller, flow table entries that are installed simultaneously are likely to timeout simultaneously and tend to trigger <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">packet_in</i> events at once during the upcoming sampling period. This phenomena in SDNs lead to serious performance and scalability issues when a large number of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">packet_in</i> events are invoked simultaneously. In the proposed model, the orchestrating agent and the controller duo modulate an ensemble of networking devices from their heterogeneous frequency and incoherent nature of querying the controller into a conciliatory and systematic manner. The orchestrating agent modulates the control flows by altering timeouts, especially for long-lived TCP connections so that the devices do not overwhelm the controller with bursty requests during the future events. In addition, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">flow_mods</i> to intermediate devices over a multi-hop path are scheduled based on the estimated flow setup latency to avoid redundant <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">packet_ins</i> . The proposed orchestrating agent is implemented in conjunction with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Floodlight</i> controller. The experimental results show that apart from the influence of external delays majority of the flows were well-synchronized and the results show significant improvement in scalability and in the overall performance of the network.

Block diffusion delay attack and its countermeasures in a Bitcoin network

SummaryIn the Bitcoin system, transactions and their collections (i.e., blocks) are distributed over a peer‐to‐peer (P2P) network (i.e., Bitcoin network) constructed by participating nodes. Each node maintains a distributed ledger (i.e., blockchain) consisting of retrieved blocks. Therefore, speedy block distribution over the Bitcoin network is essential for all nodes to reach a global consensus on the blockchain. On the other hand, Bitcoin clients are developed as open source software, and thus they can be modified by malicious users. Existing work has pointed out that an attacker can delay the block propagation between neighboring nodes by exploiting the regular timeout mechanism for unexpected slow block transfer caused by temporal network trouble. In this paper, we focus on block diffusion delay attacks, where multiple attackers collude with a specific miner (i.e., a special node that creates new blocks and broadcasts these blocks to the other miners) to disturb the propagation of blocks generated by competing miners. Through simulation experiments, we first reveal that about 30% of honest nodes cannot normally retrieve a block when there are only 1% of the nodes in the system are high‐degree adversary nodes in the system. This indicates that the malicious miner colluding with the attackers can intentionally delay the diffusion of the block mined by the competing miner, so as to win the competitive block diffusion even if it loses at the competitive block mining. To alleviate the block diffusion delay attack, we propose two kinds of countermeasures: a proactive approach that is a speedy recovery method from the interruption by adjusting the timeout value and a reactive approach that is a block retrieval node selection method based on the past download rate from each neighbor. Through simulation experiments, we show the countermeasures can effectively alleviate the risk.

The Challenge of Only One Flow Problem for Traffic Classification in Identity Obfuscation Environments

As encrypted traffic grows, network flow classification has become a significant issue because of the impossibility to parse the payload in an encrypted packet. A possible packet sniffing location for organizations is an under control gateway between intranet and internet to inspect network traffic. However, when an intranet user uses an identity obfuscation protocol such as VPN or TOR, the packet IP and port would be rewritten to preserve user privacy. The same user's packet sniffed between a user and TOR entry node/VPN proxy always has the same 5-tuples (packets with the same source IP, destination IP, source port, destination port, and IP protocol defined as flow). Thus, we cannot rely on the 5-tuples rule to split traffic into flows. This challenge is called the “only one flow problem” and poses an obstacle for flow classification. A previous solution uses timeout value to determine flow separation points to address this issue. However, the predefined static time threshold cannot fit all user habits, which leads to separation errors. To overcome timeout limitations, we propose a flexible method called AI-FlowDet by leveraging the scene change concept and a CNN model to find behavior change points of traffic based on learning data. AI-FlowDet can apply to the traffic of the identity obfuscation protocols. Next, we propose 294 size-based and direction-based features that can be used with AI-FlowDet to evaluate flow type classification performance. Every experiment leverages different machine learning algorithms. The results show that AI-FlowDet with the proposed features can achieve 98.5% weighted accuracy, which is increased by 12.6% versus the previous timeout method with baseline features. We proved that the proposed splitting methods for the only one flow problem and proposed features for flow type classification are effective based on the good results obtained for both the VPN and TOR datasets.

A Framework for Managing Dynamic Routing in Industrial Networks Driven by Software-Defined Networking Technology

In order to meet strict Quality-of-Service (QoS) constraints imposed by some industrial applications, the configuration of industrial networks must address the requirements of traffic flows with different priorities such as minimum delay and packet loss. The performance is affected significantly if the end-to-end delay and packet loss surpasses a specific limit, and may become unbefitting for the destination. In this paper, we select Software-Defined Networking (SDN) technology to manage centralized devices and design an optimal path to guarantee the QoS requirements by taking into consideration two types of traffic flows: the first is low-delay while the second is both low-delay and low-loss. By using this optimal path, the Reactive Flow Installation (RFI) method increases the time delay in the forwarding of packets. So as to solve this issue, we propose a Mixed Flow Installation (MFI) method based on caching the flow rules which correspond to the optimal paths in the hash table deployed in the SDN controller memory in order to reduce the computation time of the forwarding path and the load at the SDN controller. Alternatively, the pre-configured flow rules in switches by the Proactive Flow Installation (PFI) method achieves the delay-sensitive. However, the PFI is not modified when the network status or the traffic type changes till the timeout value expires. This can affect the QoS requirements for industrial applications. To handle this challenge, we propose a PFI Re-routing (PFIR) method that redefined faster a new optimal path according to change without waiting the SDN controller for new flow rules. With the care of wireless and wired networks, we have built a simulation network via the OpenDayLight SDN controller and conducted an experimental testbed. The framework results are demonstrated by the performances through reduction of end-to-end delay, packet loss rate, and packet violation ratio.

Modelling user satisfaction for power-usage optimisation of computer fleets

Power consumption costs of computer fleets can be one of the main operational costs of medium or large-sized office sites. In order to optimise the power consumption of the fleet, a set of optimal power management policies must be generated and enforced.Generating these policies is an optimisation problem of finding the power off timeout value for each computer that maximises energy savings while guaranteeing user satisfaction. To solve this problem, understanding the computer utilisation patterns of the users and defining a metric of user satisfaction is fundamental.This paper presents a method to analyse user activity and inactivity, extract models from previously recorded utilisation logs and use them to manage a whole computer fleet. A tool that implements this method is also introduced. This tool generates power management policies from utilisation logs. It analyses the effects of variations in fleet characteristics on policies by means of discrete event simulation. It also seeks to understand the behavioural patterns of users over weekly periods. Finally, it generates utilisation logs from high level descriptions of fleets. This tool offers a simulation to study diverse fleet configurations and generation of synthetic fleets.

Optimasi Algoritma Rate Adaptation Control CARA untuk Meningkatkan Throughput pada Wireless Mesh Network

This paper examined throughput optimization issue in wireless mesh network (WMN), the weakest point of this network in regard to this matter. A number of previous studies on this issue have been conducted, but most focus has been on general wireless network, only few studies so far have attempted on this network. This research aimed to optimize the CARA rate adaptation control algorithm in WMN network. The optimization was performed by adjusting the successtreshold and timeout parameters in the CARA algorithm to obtain an optimal throughput. The optimal result is showed that the optimal points of the success threshold and timeout are at range of 30-35. It is obviously seen in grid 4×5 and 5x5 where the throughput value of the optimization result continues to increase. Moreover, by adding the data transmission time for 100 seconds on grid 5×5 resulting the throughput value of 0.52206412 Mbps and after the optimization the throughput value increases up to 117% to 1.1350768 Mbps, when the success threshold and timeout value are 30. For an additional 150 seconds, the throughput value is 0.5074419333 Mbps and after the optimization the throughput increases up to 120% to 1.1211402 Mbps, when the success threshold and timeout value are 35.

EVALUATION OF THE EFFECTIVENESS OF CHANNEL LEVEL PROTOCOLL PARAMETERS

There is justification for a simulation model for assessing channel level protocols. The effect of the data link layer protocol parameters on the data transfer function is analyzed. The data link protocol parameters have been specified and split into two categories, which are customizable and not configurable. In the work, parameters such as the acceptable information frame size are related in detail to the tunable data link protocol parameter; service personnel format; time-out value; unconfirmed frame window size, etc. That nominal data transfer rate, error-correcting codes used, etc. are non-configurable parameters.

Improving End-Users Utility in Software-Defined Wide Area Network Systems

Software Defined Networks (SDNs) has brought a new form of network architecture that simplifies network management through innovations and programmability. But, the distributed control plane of SD-Wide Area Network is challenged by load imbalance problem due to the dynamic change of the traffic pattern. The packet_in messages are one of the major contributors of the control’s load. When such packet rate exceeds a certain threshold limit, the response time for control request increases non-linearly. In order to achieve better end-user experience, most of the previous works considered the optimal switch to controller association with an objective to minimize the response time on LAN environment but ignores the consequence of large scale network. In this regard, the proposed work realizes the necessity of layer-2 and layer-3 controller in LAN and WAN environment separately. A load prediction based alertness approach has been introduced to reduce the burden of the controllers. This approach may create an additional delay for the initial packets of the flow entry that lead to more prediction error. However, the proposed method reduces the error by selecting an optimal timeout value of the flow. Further, minimization of the response time between router to the controller has been taken care of. An extensive simulation shows the efficacy of the proposed scheme.

Generic Validation Criteria and Methodologies for SDN Applications

Programmable control plane in software-defined networking (SDN), plays an essential role in the SDN architecture. The network function provided by the specialized hardware in a legacy network can be created in the form of software-based “SDN application” running on the controllers to manipulate entire network configurations. Therefore, the risk of having software bugs and errors in the SDN applications may threaten the normal operations of SDN networks. This paper presents systematic validation criteria and test cases based on the proposed novel methodologies for SDN application testing. The test framework can perform testbed build-up, generate desired packet sequences, and analyze results automatically. According to the results of a generic test suite, several issues are unveiled in the application under test (AUT). Some AUTs, which need to check all the incoming packets from OpenFlow switches, fail to meet the test criteria of burst packet-in and flow self-recycling. For most of the applications based on the Ryu controller, the evaluation results reveal that some are unable to recycle flow entries after they are unloaded. It is recommended that all flows populated by SDN applications must have timeout value specified to prevent unnecessary entries kept in the flow table.