Mobile edge computing is envisioned as a promising paradigm to overcome the computational limitations of vehicles. However, current resource allocation optimizations for vehicular services in mobile edge computing are typically performed under quasi-static scenarios. Due to the time-varying wireless communication environments between vehicles and the base station, the communication uncertainty becomes a key factor to hinder the service reliability. To tackle this problem, the reliability-aware mobile edge resource allocation for vehicular services is explored in this paper. Firstly, the model of computation, communication, and reliability is reviewed integratively, and the problem is then formulated as a fractional programming model. Secondly, conditional value-at-risk (CVaR) is adopted to transform the original problem into a solvable semidefinite programming model. Finally, we propose an efficient two-layer reliability-aware resource allocation approach that decomposes the original problem into two sub-problems with lower complexity. Lagrange multiplier and Dinkelbach strategy are then applied for these sub-problems, which minimize average energy consumption while ensuring reliability for vehicles. Simulation results examine the performance of our proposed approach and demonstrate its superiority over other approaches.

Edge computing plays a pivotal role in supporting services necessitating sub-second latency, notably in domains like Industry 4.0 and autonomous driving. However, unpredictable failure occurring at edge servers can result in prolonged response time and decreased service reliability, posing significant risks to both safety and property. Traditional reliability mechanisms, namely task re-execution and task replication, are often inadequate for edge environments. The former struggles to meet the stringent end-to-end service latency requirements, while the latter imposes a high resource consumption burden on resource-limited edge clouds. To address this issue, this paper introduces a novel Flexible Shadow mechanism, where the backup instance, referred to as the Flexible Shadow, is allocated fewer computation resources compared to its primary instance to conserve computation resources, and temporally preempts a portion of resources from neighboring shadows to accelerate when necessary. To support the implementation of this mechanism, we propose the Flexible Shadow Backup Framework, a resource-efficient reliability enhancement framework for edge services through dynamic shadow coordination. This framework integrates three key components: a deployment algorithm for resource allocation, an adjustment algorithm for migration cost-latency tradeoffs, and a reconfiguration algorithm for adaptation optimization. Comprehensive experiments conducted on a Docker-based prototype demonstrate the effectiveness of the Flexible Shadow mechanism, achieving nearly 60% reduction in computing resource consumption compared to traditional approaches while maintaining sub-second latency.

Edge computing plays a pivotal role in supporting services necessitating sub-second latency, notably in domains like Industry 4.0 and autonomous driving. However, unpredictable failure occurring at edge servers can result in prolonged response time and decreased service reliability, posing significant risks to both safety and property. Traditional reliability mechanisms, namely re-execution and replication, are often inadequate for edge environments, with the former often struggle to meet latency requirements and the latter imposing a high resource consumption burden on resource-limited edge clouds. To address this issue, this paper introduces a novel "Flexible Shadow" mechanism, where the backup instance, referred to as the "Flexible Shadow", is allocated fewer computation resources compared to its primary instance to conserve computation resources, and temporally preempts a portion of resources from neighboring shadows to accelerate when necessary. To tackle the implementation challenges of this framework arising from diverse service requirements, dynamic nature of edge environments and potential deadlock in the reconfiguration process, we devised the Flexible Shadow Deployment Algorithm for accurate shadow deployment and the Flexible Shadow Reconfiguration Algorithm for dynamic strategy adjustment. We have implemented our Flexible Shadow framework on Docker and evaluated it via comprehensive experiments. The experiment results demonstrate a nearly 60% reduction in computing resource consumption while ensuring sub-second latency.

Edge computing serves as a critical solution for latency-sensitive services on mobile and IoT devices. However, the high cost and limited edge resources present significant challenges for service and infrastructure providers in establishing efficient collaborations, particularly with conflicting profit objectives. Inspired by the pseudo elbow formation of octopuses, we propose a multi-participant double auction for resource allocation and pricing between service and infrastructure providers. We introduce a neutral third-party auctioneer to eliminate direct bargaining among participants, leading to an improved amount of allocated resources and matching efficiency. The presence of heterogeneous participants, many-to-many mapping and an advisable payment strategy that satisfies economic properties exacerbate the difficulty. To address these challenges, we propose a Matching and Pricing Resource Allocation algorithm for a long-term steady market, and a Truthful Resource Allocation algorithm for a short-term market. Simulation results demonstrate that the proposed algorithms exhibit superior performance not only in maximizing social welfare and utility of both service and infrastructure providers, but also in improving resource utilization.

As infrastructure deployment continues to expand worldwide, the development of the Internet of Vehicles has become increasingly feasible. With widespread cellular connectivity and powerful roadside computing capabilities, advanced driving assistance systems can now rely on roadside decision models in addition to vehicle-side ones, overcoming the limitations of a single vehicle’s perception range. This shift has led to improvements in manufacturing efficiency, cruising range, and battery life of intelligent vehicles. However, maintaining the ultra-low latency and high-reliability requirements of on-vehicle services is still a challenge due to air interface fluctuations and edge server computing loads, which could potentially jeopardize driving safety. To tackle this issue, we conducted real-world measurements of edge server access delay in LTE and 5G cellular networks. Our analysis identified key factors affecting delay distribution, leading to the development of an approximate fitting function for the delay probability density function. We also proposed a reliability-aware task replication algorithm that leverages delay samples and edge server status information to make real-time task replication and offloading decisions, minimizing replication while ensuring service reliability. Simulations based on real-world datasets indicate our approach reduces task completion delay by up to 42.11% and limits the maximum task replication redundancy peak value to 63.37%, effectively ensuring the reliability of on-vehicle services during driving.

Edge computing is extending the frontier of computation beyond terrestrial boundaries, with Low Earth Orbit (LEO) constellations emerging as the cutting-edge paradigm, aspiring to deliver ubiquitous computing capabilities globally. However, the unresolved service reliability issues within LEO constellations continue to barricade the realization of the vision for space computing. Terrestrial-native classical methods struggle to meet the stringent environmental conditions of space computing and often fail to satisfy the rigorous requirements of LEO constellations for failure response times and computational resource overhead. To address these challenges, we introduce the LEO Service Reliability Enhancement Framework (LSREF). LSREF employs the Variable Speed Replication technology to balance failure response times and computational resource overhead and adopts a decentralized design, more congruent with the characteristics of LEO constellations. Specifically, to counter the immense scale and high dynamism of LEO constellations, LSREF proposes orbital plane-based autonomous domains and leverages the Satellite Autonomous Backup Deployment Algorithm in conjunction with the Registry Polling Mechanism to enable autonomous decision-making for service backup strategies on each satellite. Our simulation experiments demonstrate that, compared to classical methods, LSREF reduces average failure response times by 10.76% and diminishes computational resource consumption by nearly 15.53%.