A Multi-Beam Satellite Cooperative Transmission Scheme Based on Resources Optimization and Packets Segmentation

Multi-beam satellite communication systems are promising architectures in the future. A packet is transmitted by multi-satellite and multi-beam cooperatively, which can provide efficient spectrum utilization, improve system throughput, and guarantee Quality of Services (QoS). In multi-beam satellite communication systems, multi-layer and multi-dimensional radio resources change dynamically, which leads to the discontinuity of optimal resources and the lack of mapping balance between packets and radio resources. To deal with these problems, we propose a cross-layer and cross-dimension radio resources optimization model based on the weighted discrete firefly algorithm and an adaptive packet segmentation scheme based on the irregular gradient algorithm. The cross-layer and cross-dimension radio resources optimization model based on the weighted discrete firefly algorithm simulates cross-layer and cross-dimension optimization for the high-dynamic and multi-dimensional radio resources by considering the channel state information (CSI) and QoS in the multi-beam satellite communication system. The optimal resources are taken as the weight of irregular gradient algorithm to segment packets and map packets to radio resources, which can realize the mapping balance between packets and radio resources and ensure the efficiency and reliability of communication. The simulations show that the new transmission scheme improves the normalized system throughput and user satisfaction index by 18.7% and 6.2%, respectively.

Probabilistic-QoS-Aware Multi-Workflow Scheduling Upon the Edge Computing Resources

Edge computing is an evolving decentralized computing infrastructure by which end applications are situated near the computing facilities. While the edge servers leverage the close proximity to the end-users for provisioning services at reduced latency and lower energy costs, their capabilities are constrained by limitations in computational and radio resources, which calls for smart, quality-of-service (QoS) guaranteed, and efficient task scheduling methods and algorithms. For addressing the edge-environment-oriented multi-workflow scheduling problem, the authors consider a probabilistic-QoS-aware approach to multi-workflow scheduling upon edge servers and resources. It leverages a probability-mass function-based QoS aggregation model and a discrete firefly algorithm for generating the multi-workflow scheduling plans. This research conducted an experimental case study based on varying types of workflow process models and a real-world dataset for edge server positions. It can be observed the method clearly outperforms its peers in terms of workflow completion time, cost, and deadline violation rate.

Delay Bound Optimization in NoC Using a Discrete Firefly Algorithm

The delay bound in system on chips (SoC) represents the worst-case traverse time of on-chip communication. In network on chip (NoC)-based SoC, optimizing the delay bound is challenging due to two aspects: (1) the delay bound is hard to obtain by traditional methods such as simulation; (2) the delay bound changes with the different application mappings. In this paper, we propose a delay bound optimization method using discrete firefly optimization algorithms (DBFA). First, we present a formal analytical delay bound model based on network calculus for both unipath and multipath routing in NoCs. We then set every flow in the application as the target flow and calculate the delay bound using the proposed model. Finally, we adopt firefly algorithm (FA) as the optimization method for minimizing the delay bound. We used industry patterns (video object plane decoder (VOPD), multiwindow display (MWD), etc.) to verify the effectiveness of delay bound optimization method. Experiments show that the proposed method is both effective and reliable, with a maximum optimization of 42.86%.