Abstract
Mission-critical networks, which for example can be found in autonomous cars and avionics, are complex systems with a multitude of interconnected embedded nodes and various service demands. Their resilience against failures and attacks is a crucial property and has to be already considered in their design phase. In this paper, we introduce a novel approach for optimal joint service allocation and routing, leveraging virtualized embedded devices and shared backup capacity for the fault-tolerant design of mission-critical networks. This approach operates in phases utilizing multiple optimization models. Furthermore, we propose a new heuristic that ensures resource efficiency and fault-tolerance against single node and link failures as pre-requisite for resilience. Our experiments for different application scenarios indicate that our heuristic achieves results close to the optimum and provides 50% of capacity gain compared to a dedicated capacity protection scheme. Moreover, our heuristic ensures fault-tolerance against at least 90% of all potential single node failures.
Highlights
M ISSION-CRITICAL embedded systems as used in autonomous vehicles, airplanes, and industrial networks have evolved to complex ecosystems
We describe (i) our computational resources and tools used to run our experiments, (ii) our topology and service overlay generation approach, (iii) what we measured and related parameters, and lastly (iv) the metrics we used for the comparisons
As we have summarized in the complexity discussion of our previous work [10], finding the optimal configuration, which is resilient to all single node failures without considering any shared capacity, might take days as it is formulated as a single linear problem
Summary
M ISSION-CRITICAL embedded systems as used in autonomous vehicles, airplanes, and industrial networks have evolved to complex ecosystems. This configuration should guarantee a certain degree of resilience against the potential malfunctions or threats To satisfy those requirements, we have modeled the resilient service placement and routing problem addressing single node failures in our preliminary work [10]. Leveraging virtualized embedded devices and virtual services, we have found alternative configurations of the network to reserve required resources for migrating services and flows in case of failures. Propose three separate optimization models to solve (i) service placement and routing, (ii) allocation of backup paths with shared capacity use against link failures and (iii) a service migration scheme in case of node failures. Our heuristic results in near-optimal results for the shared backup capacity allocation It provides more than 90% fault-tolerance against single random node failures that can happen on the host nodes.
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