Modern data centre networks demand ultra-low latency and high throughput, making Remote Direct Memory Access (RDMA) increasingly important. However, RDMA’s performance degrades significantly under congestion due to limited built-in congestion control. Existing approaches, based on Explicit Congestion Notification (ECN) and Round-Trip Time (RTT) suffer from limitations including delayed feedback and noisy signals when used alone. We propose Dynamic Hybrid ECN-RTT Congestion Control (DHERC), a novel algorithm that integrates RTT-based early detection with ECN-based confirmation to achieve balanced congestion control. Through extensive simulations, we demonstrate that DHERC outperforms state-of-the-art schemes across key performance metrics. DHERC achieves better flow completion time performance, improved throughput fairness, and good goodput efficiency while maintaining competitive tail performance. The results establish multi-signal congestion control as a promising approach for RDMA networks requiring predictable performance, efficiency, and fairness.

As 6G networks must support diverse applications with heterogeneous quality-of-service requirements, efficient allocation of limited network resources becomes important. This paper addresses the critical challenge of user admission control in 6G networks enhanced by Reconfigurable Intelligent Surfaces (RIS) and Mobile Edge Computing (MEC). We propose an optimization framework that leverages RIS technology to enhance user admission based on spatial characteristics, priority levels, and resource constraints. Our approach first filters users based on angular alignment with RIS reflection directions, then constructs priority queues considering service requirements and arrival times, and finally performs user grouping to maximize RIS resource utilization. The proposed algorithm incorporates a utility function that balances Quality of Service (QoS) performance, RIS utilization, and MEC efficiency in admission decisions. Simulation results demonstrate that our approach significantly improves system performance with RIS-enhanced configurations. For high-priority eURLLC services, our method maintains over 90% admission rates even at maximum load, ensuring mission-critical applications receive guaranteed service quality.

Most existing transaction sharding technologies allocate transactions randomly or based on transaction addresses, leading to imbalanced workloads and high ratio of cross-shard transactions (CST). The uneven workload makes it impossible for blockchain to fully utilize resources for transaction processing, which degrades system throughput. CST incur additional overheads in communication, verification, and processing, increasing block confirmation latency. This paper proposes a blockchain sharding transaction allocation algorithm known as correlation and workload-based transaction allocation (CWTA) to address the above issues. CWTA regularly pumps a certain number of pending transactions from the transactions pool to establish a multiple-directed transaction graph (MDTG) and achieves faster allocation efficiency by quickly gathering the out-edges of each account based on MDTG. This process has successfully changed the distribution mode of transactions from one-by-one to group-by-group which depends on the correlation between the groups for current and upcoming workloads of each shard. CWTA has demonstrated its effectiveness through abundant transaction allocation results with 2-16 shards. Compared to similar existing work, CWTA can achieve a much more balanced transaction workload and less allocation time without increasing the CST ratio which denotes better scalability, and achieve better throughput.

The increasing demand for real-time, adaptive monitoring across various domains, such as environmental surveillance and disaster management, necessitates a shift from static to dynamic sensor deployments within Mobile Wireless Sensor Networks (MWSNs). One prominent application scenario involves dynamic changes in the Region of Interest (RoI), requiring sensor redeployment. However, defining dynamic RoI scenarios lacks specificity. To address this gap, this study introduces two distinct categories defining a change in RoI: transformed RoI, where the initial RoI undergoes affine transformations, and evolved RoI, representing entirely new polygonal configurations. Additionally, the study explores limitations within current state-of-the-art distributed deployment algorithms, which hinder performance in dynamic RoI settings. These limitations are investigated through experiments involving two main types of distributed algorithms: geometric-based and virtual force-based deployment algorithms. The findings underscore significant performance challenges faced by existing algorithms in dynamic RoI scenarios, emphasizing the need for the development of more resilient algorithms capable of adapting to dynamic RoIs while ensuring network connectivity and minimizing node movement.

Decentralised technology backed by blockchain has gained a huge popularity in recent years as it secures autonomous ecosystems without needing a central authority. The origin of the blockchain concept began in the financial domain using cryptocurrency, but over the last few years, blockchain has been applied to a variety of industries. In the era of Industry 4.0, most industries are leveraging automation by using Internet of Things (IoT). Despite numerous applications of blockchain in the industries, due to the significant latency in consensus algorithm in blockchain, businesses using IoT technology are facing performance issues in adopting blockchain. A number of studies address the obstacles for transaction processing performance and system scalability, mostly based on a public blockchain. However, they still involve centralised components, and thus fail to fully utilise decentralisation. Therefore, a private blockchain-based IoT data integration platform is proposed to achieve data integrity and system scalability. Along with the lightweight IoT gateway instead of any other additional middleware, the process and the system configuration are streamlined. By using Hyperledger Fabric, the design is validated and it is shown that the proposed architecture outperforms other conventional model in IoT data processing.

Low latency is critical to applications such as control of unmanned aerial vehicles. Such latency-sensitive services can be hosted closer to the user at the fog layer which can reduce overall latency through the reduction of transmission time and network congestion. To keep the latency low for mobile users connected to services deployed at the fog, these services need to be constantly migrated to follow the users. Unlike the cloud nodes, fog nodes are less reliable and are therefore subject to higher failure rate. In this paper, we propose an enhancement to the post-copy live migration algorithm to make it robust against failure. Simulation results show that robust migration reduces total migration time between 10-26% and downtime between 2-23% compared to non-robust migration. Furthermore, when the bandwidth to the backup node is lower, robust migration provides further improvement in both metrics.

Cloud computing, a distributed computing paradigm, enables delivery of IT resources over the Internet and follows the pay-as-you-go billing model. Workflow scheduling is one of the most challenging problems in cloud computing. Although, workflow scheduling on distributed systems like grids and clusters have been extensively studied, however, these solutions are not viable for a cloud environment. It is because, a cloud environment differs from other distributed environment in two major ways: on-demand resource provisioning and pay-as-you-go pricing model. Thus, to achieve the true benefits of workflow orchestration onto cloud resources novel approaches that can capitalize the advantages and address the challenges specific to a cloud environment needs to be developed. This work proposes a dynamic cost-effective deadline-constrained heuristic algorithm for scheduling a scientific workflow in a public cloud. The proposed technique aims to exploit the advantages offered by cloud computing while taking into account the virtual machine (VM) performance variability and instance acquisition delay to identify a just-in-time schedule of a deadline constrained scientific workflow at lesser costs. Performance evaluation on some well-known scientific workflows exhibit that the proposed algorithm delivers better performance in comparison to the current state-of-the-art heuristics.