Muhammad Imran

Microservices-centric in-network computations, coupled with Named Data Networking (NDN) and supported by vehicular edge computing (VEC), offer a promising platform to meet the requirements of vehicular applications. However, a significant obstacle hindering high efficiency is the interdependency of microservices (MS), which may delay the timely delivery of results due to Pending Interest Table (PIT) timer expiration, resulting in unsolicited packet drops. Moreover, voluminous data transmission in a resource-constrained environment may prevent the consumer from receiving results on time. Therefore, to avoid unsolicited computation losses and enable proximate computations, this article envisions interdependent microservices offloading and semantic aware results transmission (iMSoRT) for 6G vehicular edge networks. iMSoRT formulates an efficient strategy that allows traffic controller ( $T_{c}$ ) to account for MS interdependencies and allocate the PIT timer based on computational and network resource requirements. Moreover, iMSoRT introduces a live forwarding information base (lFIB) that effectively filters out underutilized RSU-E servers and forwards computation requests to the optimal RSU-E, accelerating processing while minimizing communication costs. Furthermore, iMSoRT develops a Semantic Transformer (ST) that leverages YOLO and Convolutional Neural Networks (CNNs), placing it between the caching and forwarding modules of NDN. The ST extracts and forwards semantically meaningful information, thus reducing network resource utilization and enhancing information exchange efficiency. Simulation results revealed that iMSoRT achieved an impressive compute-hit ratio of over 90%, restrict cloud offloading to 12%, reduced computation delays over 50%, and optimized bandwidth utilization over threefold compared with benchmark schemes.

Information-centric networking (ICN) is an emerging network architecture for mobile edge computing (MEC) referred to as ICN-MEC. ICN-MEC has garnered significant attention for its capability to offload compute-intensive applications that utilize microservice architecture to edge servers. For such compute-intensive applications, these microservices depend on each other, are known as dependent microservices, and must be sorted in a specific sequence order by solving their dependency relationships before offloading and execution. However, such sequence orders result in waiting time for certain dependent microservices. We introduce Intelligent Forwarding and Computation Node Selection in ICN-MEC (IFCNS) to address this waiting time for offloading dependent microservices. IFCNS addresses microservice dependencies by partitioning them into distinct types and designing a naming scheme extractable by intermediate forwarders. The intermediate forwarder also maintains records of edge server computation loads, predicted using deep neural networks (DNN), and selects suitable edge servers for offloading. Acting as a decision-maker, the intermediate forwarder makes intelligent offloading decisions to reduce waiting time. Extensive simulations using the ns3-based ndnSIM simulator show that IFCNS significantly reduces time consumption by approximately 60%-70%, backhaul traffic load by 57%-68%, bandwidth consumption by 30%-40%, time consumption overhead by 75%, and energy consumption by one-third compared to the DVR-MPTS and OTOOA schemes, respectively. Furthermore, IFCNS enhances computation request satisfaction efficiency by 9% and 22.87%, respectively, while reducing computational resource utilization by 10%-90% compared to the aforementioned schemes across varying computational request rates.

Autonomous vehicles are revolutionizing the future of intelligent transportation systems by integrating smart and intelligent onboard units (OBUs) that minimize human intervention. These vehicles can communicate with their environment and one another, sharing critical information such as emergency alerts or media content. However, this communication infrastructure is susceptible to cyber-attacks, necessitating robust mechanisms for detection and defense. Among these, the most critical threat is the denial-of-service (DoS) attack, which can target any entity within the system that communicates with autonomous vehicles, including roadside units (RSUs), or other autonomous vehicles. Such attacks can lead to devastating consequences, including the disruption or complete cessation of service provision by the infrastructure or the autonomous vehicle itself. In this paper, we propose a system capable of detecting DoS attacks in autonomous vehicles across two scenarios: an infrastructure-based scenario and an infrastructureless scenario, corresponding to vehicle-to-everything communication (V2X) Mode 3 and Mode 4, respectively. For Mode 3, we propose an ensemble learning (EL) approach, while for the Mode 4 environment, we introduce a gossip learning (GL)-based approach. The gossip and ensemble learning approaches demonstrate remarkable achievements in detecting DoS attacks on the UNSW-NB15 dataset, with efficiencies of 98.82% and 99.16%, respectively. Moreover, these methods exhibit superior performance compared to existing schemes.

Real-time healthcare applications demand stringent communication resources to meet the QoS requirements. To meet the application resource demands, this poster proposes a Load and Bandwidth aware Forwarding strategy (LBF-ICN) in Information-Centric Networks (ICN). The LBF-ICN considers router interfaces' pending entries and available bandwidth resources in order to select the next hop for interest forwarding. The LBF-ICN strategy aims to distribute the network load along with bandwidth allocation in interest forwarding.

To efficiently allocate communication and computation resources among enhanced mobile broadband (eMBB) and ultrareliable low-latency communication (uRLLC) applications, this article proposes a joint communication and computation resource allocation scheme in sixth generation (6G) information-centric networks (ICNs) named: CCR-ICN. To achieve efficient communication and computation resources sharing, the CCR-ICN scheme designs a microservice-centric interest naming structure to enable named-based communication. Furthermore, the CCR-ICN scheme designs a robust forwarding strategy specifically tailored to efficiently allocate communication resources, such as bandwidth among eMBB and uRLLC application requests. In addition, for the allocation of computation resources, the proposed scheme designs a priority-driven computation resources allocation mechanism ensuring effective allocation of multiaccess edge computing computation resources. To evaluate the proposed scheme, we performed extensive simulations in a widely used NS-3-based ndnSIM simulator and the results reveal the CCR-ICN achieves 66.82% higher communication resource allocation, and a 17.51% lower communication overhead compared to the TS-6G scheme. Moreover, the CCR-ICN performs computation request satisfaction at 42.03% and 12.72% higher than the TS-6G and extended weighted fair queueing with latency constraint (EWFQ/LC) schemes, respectively. Furthermore, the CCR-ICN achieves computation resource utilization of 35.54% and 21.63% lower than the EWFQ/LC and TS-6G schemes, respectively.

The named data networking (NDN)-based microservice-centric in-network computation poses various challenges in terms of interest aggregation and pending interest table (PIT) lifetime management. A same-named microservice-centric interest packet may have a different number of input parameters with nonidentical input values. In addition, the same-named interest packet with the same number of parameters may have different corresponding parameter values. The vanilla NDN request aggregation (based on the interest name, while ignoring the input parameters count and/or their corresponding values) may result in false aggregation. Moreover, the microservice-centric requested computations may fail to accomplish in the default 4s PIT timer due to the input size. To address these challenges, this paper presents MIA-NDN: microservice-centric interest aggregation in named data networking. We designed microservice-centric interest-naming to enable name-based communication. MIA-NDN develops a robust interest aggregation mechanism that not only performs the interest aggregation based on the interest name but also considers the input parameter counts and their corresponding values in the interest aggregation process to avoid false packet aggregations. A dynamic PIT timer mechanism based on input size was devised that avoids the PIT entry losses if the execution time exceeds the default PIT timer value to avoid computation losses and uphold the application quality of service (QoS). Extensive software-based simulations confirm that the MIA-NDN outperforms the benchmark scheme in terms of microservice-centric interest aggregation, microservice satisfaction rate, and communication overhead.

The Internet of things (IoT) has opened new dimensions of novel services and computing power for modern living standards by introducing innovative and smart solutions. Due to the extensive usage of these services, IoT has spanned numerous devices and communication entities, which makes the management of the network a complex challenge. Hence it is urgently needed to redefine the management of the IoT network. Software-defined networking (SDN) intrinsic programmability and centralization features simplify network management, facilitate network abstraction, ease network evolution, has the potential to manage the IoT network. SDN’s centralized control plane promotes efficient network resource management by separating the control and data plane and providing a global picture of the underlying network topology. Apart from the inherent benefits, the centralized SDN architecture also brings serious security threats such as spoofing, sniffing, brute force, API exploitation, and denial of service, and requires significant attention to guarantee a secured network. Among these security threats, Distributed Denial of Service (DDoS) and its variant Low-Rate DDoS (LR-DDoS), is one of the most challenging as the fraudulent user generates malicious traffic at a low rate which is extremely difficult to detect and defend. Machine Learning (ML), especially Federated Learning (FL), has shown remarkable success in detecting and defending against such attacks. In this paper, we adopted Weighted Federated Learning (WFL) to detect Low-Rate DDoS (LR-DDoS) attacks. The extensive MATLAB experimentation and evaluation revealed that the proposed work ignites the LR-DDoS detection accuracy compared with the individual Neural Networks (ANN) training algorithms, existing packet analysis-based, and machine learning approaches.

Edge Computing (EC) and Named Data Networking (NDN) are two emerging technologies that are considered as the most representative technologies for the future Internet. NDN directly forwards an application layer name in the network layer, mitigating the requirements of an individual and complex network addressing schemes for devices. The intermediate nodes and/or routers in NDN architecture cache the content inside their memory according to the caching policy to fulfill similar requests in the future within a short time. EC, on the other hand, offers computation, software services, data, and storage that are close to end-users compared to cloud computing, which results in lower delay and high speed of task execution. In the 5G communication scenarios and beyond, ultra-low latency, efficient mobility management, and security are fundamental requirements. Thus, the synergy of NDN with EC appears as a promising candidate and has attracted extensive attention from academia in the recent past. Therefore, in this survey paper, we discuss various techniques that integrate EC with Information Centric Networking (ICN)/NDN and fulfill the aforementioned requirements. We also shed light on the limitations and open research challenges in the field of NDN and EC for the research community.

The rapid demand for Cloud services resulted in the establishment of large-scale Cloud Data Centers (CDCs), which ultimately consume a large amount of energy. An enormous amount of energy consumption eventually leads to high operating costs and carbon emissions. To reduce energy consumption with efficient resource utilization, various dynamic Virtual Machine (VM) consolidation approaches (i.e., Predictive Anti-Correlated Placement Algorithm (PACPA), Resource-Utilization-Aware Energy Efficient (RUAEE), Memory-bound Pre-copy Live Migration (MPLM), m Mixed migration strategy, Memory/disk operation aware Live VM Migration (MLLM), etc.) have been considered. Most of these techniques do aggressive VM consolidation that eventually results in performance degradation of CDCs in terms of resource utilization and energy consumption. In this paper, an Efficient Adaptive Migration Algorithm (EAMA) is proposed for effective migration and placement of VMs on the Physical Machines (PMs) dynamically. The proposed approach has two distinct features: first, selection of PM locations with optimum access delay where the VMs are required to be migrated, and second, reduces the number of VM migrations. Extensive simulation experiments have been conducted using the CloudSim toolkit. The results of the proposed approach are compared with the PACPA and RUAEE algorithms in terms of Service-Level Agreement (SLA) violation, resource utilization, number of hosts shut down, and energy consumption. Results show that proposed EAMA approach significantly reduces the number of migrations by 16% and 24%, SLA violation by 20% and 34%, and increases the resource utilization by 8% to 17% with increased number of hosts shut down from 10% to 13% as compared to the PACPA and RUAEE, respectively. Moreover, a 13% improvement in energy consumption has also been observed.