A novel fault-tolerant scheduling approach for collaborative workflows in an edge-IoT environment

As a newly emerging computing paradigm, edge computing shows great capability in supporting and boosting 5G and Internet-of-Things (IoT) oriented applications, e.g., scientific workflows with low-latency, elastic, and on-demand provisioning of computational resources. However, the geographically distributed IoT resources are usually interconnected with each other through unreliable communications and ever-changing contexts, which brings in strong heterogeneity, potential vulnerability, and instability of computing infrastructures at different levels. It thus remains a challenge to enforce high fault-tolerance of edge-IoT scientific computing task flows, especially when the supporting computing infrastructures are deployed in a collaborative, distributed, and dynamic environment that is prone to faults and failures. This work proposes a novel fault-tolerant scheduling approach for edge-IoT collaborative workflows. The proposed approach first conducts a dependency-based task allocation analysis, then leverages a Primary-Backup (PB) strategy for tolerating task failures that occur at edge nodes, and finally designs a deep Q-learning algorithm for identifying the near-optimal workflow task scheduling scheme. We conduct extensive simulative case studies on multiple randomly-generated workflow and real-world edge-IoT server position datasets. Results clearly suggest that our proposed method outperforms the state-of-the-art competitors in terms of task completion ratio, server active time, and resource utilization

A Novel Fault-Tolerant Approach for Dynamic Redundant Path Selection Service Migration in Vehicular Edge Computing

Vehicular Edge Computing (VEC) provides users with low-latency and highly responsive services by deploying Edge Servers (ESs) close to applications. In practice, vehicles are usually moving rapidly. To ensure the continuity of services, edge service migration technology is in high need, by which an application, infrastructure or any edge-hosted applications or services are not locked into a single vendor and allowed to shift between different edge resource vendors. Nevertheless, due to their complex and dynamic nature, real edge computing environments are error and fault prone and thus the reliability of edge service migrations can be easily compromised if the proactive measures are not taken to counter failures at different levels. In this paper, we propose a novel fault-tolerant approach for Dynamic Redundant Path Selection service migration (DRPS). The DRPS approach consists of path selection algorithm and service migration algorithm. The path selection algorithm is capable of evaluating time-varying failure rates of ESs by leveraging a sliding window-based model and identifying a set of service migration paths. The service migration algorithm incorporates resubmission and replication mechanisms as well and decides edge service migration schemes by choosing multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions by 17.45%, 13.17%, and 7.22% in terms of ACT, TCR, and AFC, respectively

A Novel Fault-Tolerant Approach for Dynamic Redundant Path Selection Service Migration in Vehicular Edge Computing

Vehicular Edge Computing (VEC) provides users with low-latency and highly responsive services by deploying Edge Servers (ESs) close to applications. In practice, vehicles are usually moving rapidly. To ensure the continuity of services, edge service migration technology is in high need, by which an application, infrastructure or any edge-hosted applications or services are not locked into a single vendor and allowed to shift between different edge resource vendors. Nevertheless, due to their complex and dynamic nature, real edge computing environments are error and fault prone and thus the reliability of edge service migrations can be easily compromised if the proactive measures are not taken to counter failures at different levels. In this paper, we propose a novel fault-tolerant approach for Dynamic Redundant Path Selection service migration (DRPS). The DRPS approach consists of path selection algorithm and service migration algorithm. The path selection algorithm is capable of evaluating time-varying failure rates of ESs by leveraging a sliding window-based model and identifying a set of service migration paths. The service migration algorithm incorporates resubmission and replication mechanisms as well and decides edge service migration schemes by choosing multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions by 17.45%, 13.17%, and 7.22% in terms of ACT, TCR, and AFC, respectively