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The availability of Software Defined Network’s (SDNs) flow rule entry in the flow table is considered a key factor in the timely delivery of a certain flow. The controller is approached for instructions on how to deal with the flow when the rule for such flow is missing. The controller then updates the flow table accordingly at the switch so that flow could be dealt with locally. It becomes problematic when no rule is defined yet at the controller by the application plane. In most of these cases, such a situation is handled by programming the controller with wildcard rules. However, handling many flows at once under wildcard rules severely hampers the network performance. Flow rules formation by the application plane is sometimes critical and time-consuming which increases the latency ratio by creating a bottleneck at the switch level. To avoid the bottlenecks due to rule absence, in this paper, rather than waiting for the application plane’s response and putting the pending traffic flows in the buffer, which may be dropped, the controller is programmed in a way that has the built-in mechanism of self-flow rule formation. This atomized mechanism is based on the previously available traces of the same flows when they were forwarded on the network using the wildcard rules. To assess the performance of the proposed work, it is emulated and benchmarked with the latest research. The results show considerable performance achievement.

Software Defined Networks (SDN) is a new network paradigm that emerged to offer better network management by separating network control logic and data forwarding element. This separation speeds up network innovation without relying 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 deliver the flow to its destination. Afterwards, the SDN control logic process the corresponding routing rules 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 require many flows routing rules. In addition, the frequency of communication link failures has increased 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 analyses the weakness of the current solutions. Finally, it highlights potential challenges that need future research attention.

Software Defined Networking (SDN) is a very useful tool not only to manage networks but also to increase network security, in particular by implementing Intrusion Detection Systems (IDS) directly into the SDN architecture. The implementation of IDS within the SDN paradigm can simplify the implementation, speed up incident responses, and, in general, allow to promptly react to cyber attacks through proper countermeasures. Nevertheless, embedding IDS within SDN also introduces delays that cannot be tolerated in specific network environments, like industrial control systems. This paper focuses on the implementation of an IDS based on Machine Learning (ML) algorithms into an SDN architecture and proposes a very practical approach to reduce the delay by using the sequential implementation of prototypes of increasing software and hardware complexity so allowing quick tests to highlight the main problems, solve them and pass to the next operative step. A fully validated performance evaluation is then shown by exploiting all the presented solutions and by using further improved hardware features. The overall performance is very good and compliant with most, even if not yet all, industrial control systems constraints. Results show how the proposed solutions provide a significant improvement of the latency so opening the door to a real implementation in the field.

Data Transmission is a primary mechanism that can affect the performance of distributed storage systems. The traditional single-path transmission protocols are not efficient to serve several requirements of big data applications. In this paper, we propose an SDN-coordinated steering framework for multipath big data transfer applications. Multipath TCP protocol (MPTCP) coupled with SDN are mainly used for big data transfer in our framework. This framework is useful and cost-effective for OpenFlow networks and overlay networks. To provide a practical multipath transmission scheme for big data transfer applications using MPTCP, we propose a novel OpenFlow-Stats routing algorithm. In our algorithm, a new topology-pruning technique is applied, and the transmission paths are selected based on switch-port statistics. Our proposed framework is implemented and evaluated using the Mininet emulator and ONOS controller. The results show that our routing scheme can reduce the completion time of big data transfer up to 90% compared with the traditional routing with disjoint paths and up to 35% compared with the previous work. Moreover, our proposed routing is more scalable than other previous works in that it can provide lower complexity and system overhead. The results show that our routing scheme produces 57% less overhead compared with the previous work.

Software-defined networking makes forwarding devices easier to manage and provides centralized control. Because of the centralization, a network administrator can programme the devices cheaply. Network administrators make attempts to convert their entire network into SDN-compatible switches. A good balance of SDN and legacy switching functions can lead to a successful network scenario in network architecture. In this study, a hybrid network scenario is provided in which the external boundary forwarding devices of the service provider network are replaced with SDN devices. Still, the other internal forwarding devices continue to operate traditionally. The benefits of both SDN and legacy network design are combined, allowing the network administrator to reap the benefits of both. The network architecture employs a policy-based routing algorithm that takes advantage of free IP addresses from the free IP pool. The technique enables efficient use of available flow table space, which is critical in SDN architecture due to the small flow table size. The algorithm’s efficiency is assessed using performance metrics such as network path stretch, throughput, latency delay, and so on, compared to traditional SDN controllers such as OpenDayLight, NOX, and POX. According to the experimental results, the suggested approach outperforms specific similar state-of-the-art techniques in the hybrid SDN domain.

Internet of Things (IoT) is characterized as one of the leading actors for the next evolutionary stage in the computing world. IoT-based applications have already produced a plethora of novel services and are improving the living standard by enabling innovative and smart solutions. However, along with its rapid adoption, IoT technology also creates complex challenges regarding the management of IoT networks due to its resource limitations (computational power, energy, and security). Hence, it is urgently needed to refine the IoT-based application’s architectures to robustly manage the overall IoT infrastructure. Software-defined networking (SDN) has emerged as a paradigm that offers software-based controllers to manage hardware infrastructure and traffic flow on a network effectively. SDN architecture has the potential to provide efficient and reliable IoT network management. This research provides a comprehensive survey investigating the published studies on SDN-based frameworks to address IoT management issues in the dimensions of fault tolerance, energy management, scalability, load balancing, and security service provisioning within the IoT networks. We conducted a Systematic Literature Review (SLR) on the research studies (published from 2010 to 2022) focusing on SDN-based IoT management frameworks. We provide an extensive discussion on various aspects of SDN-based IoT solutions and architectures. We elaborate a taxonomy of the existing SDN-based IoT frameworks and solutions by classifying them into categories such as network function virtualization, middleware, OpenFlow adaptation, and blockchain-based management. We present the research gaps by identifying and analyzing the key architectural requirements and management issues in IoT infrastructures. Finally, we highlight various challenges and a range of promising opportunities for future research to provide a roadmap for addressing the weaknesses and identifying the benefits from the potentials offered by SDN-based IoT solutions.

Software-Defined networking (SDN) is a networking paradigm to enable dynamic, flexible, and programmatically efficient configuration of networks to revolutionize network control and management via separation of the control plane and data plane. The SDN technology has evolved in response to the demands from large data centers toward all types of networks, from IoT, enterprise, to ISP networks. On the one hand, SDN has provided solutions for high-demand resources, managing unpredictable data traffic patterns, and rapid network reconfiguration. It is further used to enhance network virtualization and security. On the other hand, SDN is still subject to many traditional network security threats. It also introduces new security vulnerabilities, primarily due to its logically centralized control plane infrastructure and functions. In this paper, we conduct a comprehensive survey on the core functionality of SDN from the perspective of secure communication infrastructure at different scales. A specific focus is put forward to address the challenges in securing SDN-based communications, with efforts taken up to address them. We further categorize the appropriate solutions for specific threats at each layer of SDN infrastructures. Lastly, security implications and future research trends are highlighted to provide insights for future research.

Summary: A software‐defined vehicular network (SDVN) is a concern, where roadside units are connected, and a massive amount of data is accumulated at the software‐defined network (SDN) switches, leading to the flow table overflow problem. Existing works are concentrated more on flow‐rule aggregation in a fast manner at the switches, which impacts quality of service (QoS) at SDVN. QoS‐wise flexible OpenFlow‐rule integration method is proposed to address the challenges of OpenFlow‐table overflow problem in SDN controller switches. By having minimal impact on the QoS of the vehicular network, it reduces the number of OpenFlow rules using a key‐based mechanism. However, we note that choosing a QoS path from numerous paths is important by considering the OpenFlow‐table usage at the switches. Based on this, a flexible‐fit heuristic approach is proposed, which considers the insertions of OpenFlow rule besides with congestion flow rule‐capacity switch on a path while reducing the cumulative number of OpenFlow rules in SDVN. Experimental results of this scheme show that it decreases average end‐to‐end time delay and packet dropping by 37% and 14%, respectively. Compared with the existing scheme, the average network throughput increases by 22%, and also it has a comparable improvement in the reduction of OpenFlow rules at the switches of SDVN. [ABSTRACT FROM AUTHOR]

Software-Defined Network (SDN) enables a centralized networking architecture that employs controllers to administer a global view of the network. However, the architecture of SDN and OpenFlow are susceptible to scalability and reliability issues. In fact, the problem of determining the requisite number of controllers and their locations in SDNs while maximizing the fault tolerance, i.e., Controller Placement Problem (CPP), is NP-hard. Besides, the communication latency between the forwarding nodes and the controllers are usually very high in large-scale SDNs due to sparse deployment. This paper first formulates the CPP as Nonlinear Programming (NLP). Then, we present an efficient greedy heuristic method that employs a local Optimized High Degree and Independent Dominating Set (OHDIDS) strategy to address these issues. In particular, we examine the CPP based on Silhouette analysis, Gap Statistics, and Faster Partitioning Around Medoids (FPAM) techniques. We conduct extensive experiments using Internet Topology Zoo and Mininet network simulators to show the efficacy of the proposed method with various network topologies. The proposed method outperforms the state-of-the-art methods in terms of the minimum number of controllers, average-case and worst-case latency, and reliability. We observed that our method reduces the average communication latency by 57 % when two controllers are used rather than a single controller. Moreover, it achieves a performance gain of up to 19.31 % in terms of average propagation latency. [ABSTRACT FROM AUTHOR]

This paper provides a detailed tutorial to develop a sandbox to emulate coexistence scenarios for road and railway services in terms of sharing telecommunication infrastructure using software-defined network (SDN) capabilities. This paper provides detailed instructions for the creation of network topology using Mininet–WiFi that can mimic real-life coexistence scenarios between railways and roads. The network elements are programmed and controlled by the ONOS SDN controller. The developed SDN application can differentiate the data traffic from railways and roads. Data traffic differentiation is carried out using a VLAN tagging mechanism. Further, it also provides comprehensive information about the different tools that are used to generate the data traffic that can emulate messaging, video streaming, and critical data transmission of railway and road domains. It also provides the steps to use SUMO to represent the selected coexistence scenarios in a graphical way. [ABSTRACT FROM AUTHOR]

This article deals with quality of service (QoS) in internet protocol (IP) telephony by applying software-defined networking (SDN) tools. The authors develop a new design that deterministically classifies real-time protocol (RTP) streams based on data found in session initiation protocol (SIP) using SIP proxy as a mediator, and the concept making this possible is called SDN. Compared to traditional networks, SDN allows us to approach network configuration differently. SDN networks are programmable through software applications running on top of the SDN controller. One of the technologies that might benefit from this concept is IP telephony, which often needs an additional priority management configuration to ensure consistent quality of its real-time media exchange. Typically, a session protocol for real-time communications is SIP, and as such, its infrastructure may be used to classify the traffic in question and take advantage of the centralized approach of SDN networks to distribute the class information across the switching devices. Different approaches and possible applications are discussed in the conclusion. The contribution of this paper lies in the proposal of SDN-based QoS mechanisms. The entire design of the concept was implemented and validated in a laboratory environment. The results clearly demonstrate the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) has gained tremendous growth and can be exploited in different network scenarios, from data centers to wide-area 5G networks. It shifts control logic from the devices to a centralized entity (programmable controller) for efficient traffic monitoring and flow management. A software-based controller enforces rules and policies on the requests sent by forwarding elements; however, it cannot detect anomalous patterns in the network traffic. Due to this, the controller may install the flow rules against the anomalies, reducing the overall network performance. These anomalies may indicate threats to the network and decrease its performance and security. Machine learning (ML) approaches can identify such traffic flow patterns and predict the systems' impending threats. We propose an ML-based service to predict traffic anomalies for software-defined networks in this work. We first create a large dataset for network traffic by modeling a programmable data center with a signature-based intrusion-detection system. The feature vectors are pre-processed and are constructed against each flow request by the forwarding element. Then, we input the feature vector of each request to a machine learning classifier for training to predict anomalies. Finally, we use the holdout cross-validation technique to evaluate the proposed approach. The evaluation results specify that the proposed approach is highly accurate. In contrast to baseline approaches (random prediction and zero rule), the performance improvement of the proposed approach in average accuracy, precision, recall, and f-measure is (54.14%, 65.30%, 81.63%, and 73.70%) and (4.61%, 11.13%, 9.45%, and 10.29%), respectively. [ABSTRACT FROM AUTHOR]

Packet classification, i.e., the process of categorizing packets into flows, is a first-class citizen in any networking device. Every time a new packet has to be processed, one or more header fields need to be compared against a set of pre-installed rules. This is done for basic forwarding operations, to apply security policies, application-specific processing, or quality-of-service guarantees. A lot of research efforts have identified better lookup techniques, i.e., finding the best match between packet headers and rules, by capitalizing on the rule sets characteristics. Here, ClassBench has greatly served the community by enabling the generation of IPv4 rule sets. In this paper, we present a new tool, ClassBench-ng, that creates synthetic IPv4, IPv6, and OpenFlow rules. We start from an analysis of classification rules deployed in-the-wild and we use the findings to craft our solution. ClassBench-ng can generate a user-defined number of rules as well as an associated header trace matching them. Compared to state-of-the-art solutions, the rule set generation process is usually more accurate and it is able to produce rules matching a number of different use cases, i.e., from an IPv4 router to an OpenFlow switch, which is unique among current rule set generation tools. [ABSTRACT FROM AUTHOR]

For the easy and flexible management of large scale networks, Software-Defined Networking (SDN) is a strong candidate technology that offers centralisation and programmable interfaces for making complex decisions in a dynamic and seamless manner. On the one hand, there are opportunities for individuals and businesses to build and improve services and applications based on their requirements in the SDN. On the other hand, SDN poses a new array of privacy and security threats, such as Distributed Denial of Service (DDoS) attacks. For detecting and mitigating potential threats, Machine Learning (ML) is an effective approach that has a quick response to anomalies. In this article, we analyse and compare the performance, using different ML techniques, to detect DDoS attacks in SDN, where both experimental datasets and self-generated traffic data are evaluated. Moreover, we propose a simple supervised learning (SL) model to detect flooding DDoS attacks against the SDN controller via the fluctuation of flows. By dividing a test round into multiple pieces, the statistics within each time slot reflects the variation of network behaviours. And this ”trend” can be recruited as samples to train a predictor to understand the network status, as well as to detect DDoS attacks. We verify the outcome through simulations and measurements over a real testbed. Our main goal is to find a lightweight SL model to detect DDoS attacks with data and features that can be easily obtained. Our results show that SL is able to detect DDoS attacks with a single feature. The performance of the analysed SL algorithms is influenced by the size of training set and parameters used. The accuracy of prediction using the same SL model could be entirely different depending on the training set.

Modern trends in the information technology have led to the fact that entire systems of infrastructure are becoming software-defined. Modern hyper-converged solutions use software-defined networking and soft switches for the hypervisor networking subsystem. The paper goal is to study traffic processing in hyperconverged structures with software switching based on OpenFlow versus traditional approaches. The features of the hyperconverged solutions network infrastructure are considered, approaches to the study of software-defined environments are described. A model of the processing traffic internal structure of a converged node, combining the functions of a hypervisor, a storage system and a switch, is proposed. Interface models reproduced traffic switching with the traditional approach and with higher-level OpenFlow processing have been developed. The approaches to the implementation of the developed models based on experimental studies of network equipment are described. The results of an experimental study of a network node and a synthesized model are presented. The possibility of implementing the proposed approaches within the specified accuracy are described.

• Modern packet classification algorithms need to support both high speed search and update operations. • Implementation on GPU to further improve the performance of the proposed H- HashTree scheme. • The proposed H-HashTree performs better than state-of-the-art algorithms, CutTSS and TabTree in both searches and updates. • The H-HashTree is suitable for frequent updates needed in SDN environment. Packet classification is an important functionality of modern routers/switches, needed in packet forwarding, Quality of Service (QoS), firewall etc. In order to better utilize routers on the Internet, Software Defined Network (SDN) decouples control plane from data plane to fulfill centralized management. Based on OpenFlow standards, packet classification in SDN is designed for multi-field rules which are more complex than traditional 5-tuple rules. In the paper, we propose a novel packet classification algorithm, called hierarchical hash tree (H-HashTree), based on the two IP address fields and the 7 exact-match fields to partition rules into groups. An extended Bloom filter is also proposed to accelerate search process by skipping groups in the hash tree. To further improve the performance, H-HashTree is implemented on GPU. We tested on 100K rules including synthesized rules containing characteristics of ACL, FW, and IPC with different wildcard ratios in exact-match fields, and real OpenFlow rules from Open vSwitch. Compared with the existing state-of-the-art algorithms, CutTSS and TabTree [19] [18] , H-HashTree achieves the best performance on both search and update speeds. H-HashTree achieves 1.17-13.9 and 2.48-12.7 times faster in search speed and 2.03-6.0 and 1.87-4.53 times faster in rule updates from synthesized rulesets than CutTSS and TabTree, respectively. On the GPU platform, H-HashTree can achieve up to 114 MPPS in search speed and less than 0.04 usec/rule in rule updates. [ABSTRACT FROM AUTHOR]

Nowadays, voice over IP (VoIP) is a cost-effective and efficient technology in the communications industry. Free applications for transferring multimedia on the Internet are becoming more attractive and pervasive day by day. Nevertheless, the traditional, close, and hardware-defined nature of the VoIP networks' structure makes the management of these networks more complicated and costly. Besides, its elementary and straightforward mechanisms for routing call requests have lost their efficiency, causing some problems, such as SIP servers' overload. In order to tackle these problems, we introduce VoIP network softwarization and virtualization and propose two novel frameworks in this article. In this regard, we take advantage of the SDN and NFV concepts such that we separate data and control planes and provide the possibility for centralized and softwarized control of this network. This matter leads to effective routing. The NFV also makes this network's dynamic resource management possible by functions virtualization of the VoIP network. The proposed frameworks are implemented in a real testbed, including Open vSwitch and Floodlight, examined by various scenarios. The results demonstrate an improvement in signaling and media quality in the VoIP network. As an example, the average throughput and resource efficiency increased by at least 28% and the average response time decreased by 34%. The overall latency has also been reduced by almost 39%. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is a new networking paradigm that provides centralized control, programmability, and a global view of topology in the controller. SDN is becoming more popular due to its high audibility, which also raises security and privacy concerns. SDN must be outfitted with the best security scheme to counter the evolving security attacks. A Distributed Denial-of-Service (DDoS) attack is a network attack that floods network links with illegitimate data using high-rate packet transmission. Illegitimate data traffic can overload network links, causing legitimate data to be dropped and network services to be unavailable. Low-rate Distributed Denial-of-Service (LDDoS) is a recent evolution of DDoS attack that has been emerged as one of the most serious vulnerabilities for the Internet, cloud computing platforms, the Internet of Things (IoT), and large data centers. Moreover, LDDoS attacks are more challenging to detect because this attack sends a large amount of illegitimate data that are disguised as legitimate traffic. Thus, traditional security mechanisms such as symmetric/asymmetric detection schemes that have been proposed to protect SDN from DDoS attacks may not be suitable or inefficient for detecting LDDoS attacks. Therefore, more research studies are needed in this domain. There are several survey papers addressing the detection mechanisms of DDoS attacks in SDN, but these studies have focused mainly on high-rate DDoS attacks. Alternatively, in this paper, we present an extensive survey of different detection mechanisms proposed to protect the SDN from LDDoS attacks using machine learning approaches. Our survey describes vulnerability issues in all layers of the SDN architecture that LDDoS attacks can exploit. Current challenges and future directions are also discussed. The survey can be used by researchers to explore and develop innovative and efficient techniques to enhance SDN's protection against LDDoS attacks. [ABSTRACT FROM AUTHOR]

The industrial ecosystem has been unprecedentedly affected by the COVID-19 pandemic because of its immense contact restrictions. Therefore, the manufacturing and socio-economic operations that require human involvement have significantly intervened since the beginning of the outbreak. As experienced, the social-distancing lesson in the potential new-normal world seems to force stakeholders to encourage the deployment of contactless Industry 4.0 architecture. Thus, human-less or less-human operations to keep these IoT-enabled ecosystems running without interruptions have motivated us to design and demonstrate an intelligent automated framework. In this research, we have proposed "EdgeSDN-I4COVID" architecture for intelligent and efficient management during COVID-19 of the smart industry considering the IoT networks. Moreover, the article presents the SDN-enabled layer, such as data, control, and application, to effectively and automatically monitor the IoT data from a remote location. In addition, the proposed convergence between SDN and NFV provides an efficient control mechanism for managing the IoT sensor data. Besides, it offers robust data integration on the surface and the devices required for Industry 4.0 during the COVID-19 pandemic. Finally, the article justified the above contributions through particular performance evaluations upon appropriate simulation setup and environment. [ABSTRACT FROM AUTHOR]

The smart grid (SG) is an integration of a traditional power grid with advanced information and communication infrastructure for a large number of electrical applications. Despite all these advantages that the SG will bring, certain issues arise when designing a high-quality SG communication network. One of the critical challenges is that the existing routing strategies in smart power grids are incapable of guaranteeing differentiated QoS requirements considering the network dynamics. To address this limitation, we propose an SDN routing algorithm called the QoS-guaranteed and congestion-controlled OpenFlow routing strategy (QCORS) to satisfy the various communication demands by utilizing the flexibility of SDN. Gaining from its open and programmable idea in SDN, the proposed strategy is expected to divide the link into different congestion levels based on predicting the future congestion status from transmission links. Then packets are expected to be transmitted to routers through links under lower load conditions. The simulation results have demonstrated that the proposed method can reduce the average peer-to-peer delay of all the vocational flow and guarantee the reliability of the network. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is a new paradigm that influences all networking aspects. SDN decouples the control and data planes. Decoupling the control and data planes makes possible the management of network equipment in a more comfortable way by a control unit called the SDN controller. The SDN controller is the main component with a global view of the whole network. SDN controller is designed in a central or distributed manner. The main problem related to the design of the SDN controller is performance. In this paper, we propose an approach for dynamic creation, allocation, and management of SDN controllers in single and multi-domain networks on XEN hypervisor. In this approach, as soon as the OpenFlow packets are detected, the SDN controller can be created and assigned to multiple domains in the shortest possible time, by managing and monitoring the controllers created. The results show that the proposed approach improves the OpenFlow protocol discovery time compared to the other methods. [ABSTRACT FROM AUTHOR]

Yazılım tanımlı ağlar, geleneksel ağ mimarilerindeki ağ cihazları üzerinde bulunan veri düzlemi ve kontrol düzleminin ayrıldığı ve ağın tamamına hâkim merkezi kontrolcüler tarafından paket iletim kararlarının alındığı bir ağ teknolojisidir. Kontrolcünün ağın bütün işleyişini yönetme zorunluluğu kontrol kolaylığı sağladığı gibi güvenlik açıklıkları da yaratabilmektedir. Bu çalışmada, ele geçirilen uç cihazlar üzerinden kontrolcünün yazılım tanımlı ağ topolojisini keşfetmede kullandığı OpenFlow Keşif Protokolünün (OFDP – OpenFlow Discovery Protocol) temelini oluşturan bağlantı katmanı keşif protokolünün (LLDP- Link Layer Discovery Protocol) kötüye kullanımı vasıtasıyla LLDP Enjeksiyonu ve LLDP yeniden gönderim saldırıları gerçekleştirilmiş, saldırı sonrasında kontrolcü üzerinde tutulan topoloji ve ağdaki uç cihazların bağlantı durumları tespit edilmiştir. Yapılan saldırı testleri sonucunda Floodlight kontrolcünün LLDP istismarını temel alan saldırılara karşı savunmasız olduğu ve kontrolcü üzerinde sahte bağlantı kayıtları oluşturulmasının mümkün olduğu görülmüştür. Ayrıca, kontrolcünün saldırı neticesinde kayıtlanmış sahte linkleri dikkate alarak geçersiz rotalar hesapladığı, bu rotaları kullanması ön görülen uç cihazların erişiminin kesildiği ve ağın genel başarımında azalma olduğu gözlemlenmiştir.

أصبح تصميم وإدارة شبكات الحاسوب أكثر إبداعاً خلال السنوات القليلة الماضية بمساعدة الشبكات المعرّفة برمجيّا (SDN)، على الرغم من أنَّ هذه التكنولوجيا ظهرت فجأةً لكنها في الواقع جزء من تاريخ طويل لمحاولة جعل شبكات الحاسوب أكثر قابلية للبرمجة، وتعتبر الشبكات المعرَّفة برمجيّاً (Software Defined Networks SDN) مهيأة لتغيير التعقيد في تصميم وإدارة الشبكات من خلال تقديم واجهة نظيفة ومفتوحة بين كل من الأجهزة التي تعمل على الشبكة والبرامج التي تتحكم فيها، حيث توصَف بأنها طريقةُ ربطٍ شبكيٍّ يكون فيها مستوى التحكم مفصولاً عن العتاد الصّلب، على خلاف التجهيزات الشبكية التقليدية حيث يتواجد مستوي التحكم ومستوى البيانات في نفس الجهاز، وتُمنح مسؤولية التحكم في الشبكات المعرفة برمجياً إلى تطبيق برمجي يسمَّى (المتحكِّم) (Controller) والذي يتم اعتباره كنظام تشغيل الشبكة (Network Operating System) أي أنه تم نقل التحكم من تحكم موزع إلى تحكم مركزي. يملك المتحكم رؤية كاملة عن طوبولوجيا الشبكة التي يديرها وبالتالي يكون هو المسؤول عن توجيه حركة المرور وموازنة الحمل في الشبكة ككُل. أظهرت الشبكات المعرَّفة برمجيّاً (SDN) فوائد عديدة في نواح كثيرة مقارنة بالشبكات التقليدية. ومع ذلك، فإنَّ التوجيه غير الكفوء لحركة تدفق البيانات في الشبكات المعرَّفة برمجيّاً (SDN) يؤثر على الكفاءة ويسبب في بعض الأحيان حالات اختناق عنق الزجاجة، وبالتالي يؤثر توزيع الحمل غير المتكافئ في شبكات (SDN) بشكل كبير على أداء الشبكة، لذلك لجأَ العديد من الباحثين إلى تطبيق تقنيات ساكنة أو ديناميكية لتحقيق موازنة الحمل (Load Balancing) بهدف تحسين كفاءة شبكات (SDN). نقدِّم في هذه الورقة آلية لتحسين موازنة الحمل على حركة تدفق البيانات واختيار الطرق الأفضل لتوجيه الحزم ضمن الشبكة، وتعتمد الآلية المُقترحة على تصميم وتنفيذ آلية توجيه خفيفة الوزن وفعالة من حيث الكلفة، تم استخدام المحاكي (Mininet) لتنفيذ الشبكة وربطها مع المتحكم (HPE VAN) وتم تنفيذ الخوارزمية باستخدام (Python) وإجراء التجارب باستخدام تعليمات الأداة (iperf).