This paper develops a novel approach to design, actuate, and manufacture a space debris collector based on the conical Kresling origami pattern. The deployable nature of origami structures and the radial closability of the conical Kresling pattern are leveraged to form an enclosure volume for collecting space debris at different sizes. We first introduce the geometric, volume, and energy models of the conical Kresling pattern. Based on these models, the debris collector design problem is formulated as a parameter optimization problem to minimize the actuation energy for the folding process while satisfying the minimum volume constraint and geometric/functional constraints. To automatically capture debris in space, an actuation system is designed, which is compatible with space environments. Moreover, the multi-material three-dimensional printing technology is applied to build the designed debris collector, which makes it feasible for manufacturing the product in orbit. The proposed design, actuation, and manufacturing approaches are verified with experimental tests using a designed prototype.

Communications and radar are expected to be integrated in the same waveform and hardware platform. Mobile joint communications-radar (JCR) networks, e.g., being used in autonomous driving systems, are subject to the severe challenge of mutual interference. When frequency modulation continuous waveform (FMCW) signal is used, its inherent robustness is leveraged to combat the interference. Beyond this capability, various approaches combating the JCR interference are proposed, including the interference detection, elimination and diversity. Moreover, the interference can be leveraged to obtain extra sensing information of the environment. Simulations and experiments are carried out to demonstrate the proposed interference management approaches in JCR networks.

Cloud-based services are surging into popularity in recent years. However, outages, i.e., severe incidents that always impact multiple services, can dramatically affect user experience and incur severe economic losses. Locating the root-cause service, i.e., the service that contains the root cause of the outage, is a crucial step to mitigate the impact of the outage. In current industrial practice, this is generally performed in a bootstrap manner and largely depends on human efforts: the service that directly causes the outage is identified first, and the suspected root cause is traced back manually from service to service during diagnosis until the actual root cause is found. Unfortunately, production cloud systems typically contain a large number of interdependent services. Such a manual root cause analysis is often time-consuming and labor-intensive. In this work, we propose COT, the first outage triage approach that considers the global view of service correlations. COT mines the correlations among services from outage diagnosis data. After learning from historical outages, COT can infer the root cause of emerging ones accurately. We implement COT and evaluate it on a real-world dataset containing one year of data collected from Microsoft Azure, one of the representative cloud computing platforms in the world. Our experimental results show that COT can reach a triage accuracy of 82.1%-83.5%, which outperforms the state-of-the-art triage approach by 28.0%-29.7%.

The management of cloud service incidents (unplanned interruptions or outages of a service/product) greatly affects customer satisfaction and business revenue. After years of efforts, cloud enterprises are able to solve most incidents automatically and timely. However, in practice, we still observe critical service incidents that occurred in an unexpected manner and orchestrated diagnosis workflow failed to mitigate them. In order to accelerate the understanding of unprecedented incidents and provide actionable recommendations, modern incident management system employs the strategy of AIOps (Artificial Intelligence for IT Operations). In this paper, to provide a broad view of industrial incident management and understand the modern incident management system, we conduct a comprehensive empirical study spanning over two years of incident management practices at Microsoft. Particularly, we identify two critical challenges (namely, incomplete service/resource dependencies and imprecise resource health assessment) and investigate the underlying reasons from the perspective of cloud system design and operations. We also present IcM BRAIN, our AIOps framework towards intelligent incident management, and show its practical benefits conveyed to the cloud services of Microsoft.

In cloud service systems, customers will report the service issues they have encountered to cloud service providers. Despite many issues can be handled by the support team, sometimes the customer issues can not be easily solved, thus raising customer incidents. Quick troubleshooting of a customer incident is critical. To this end, a customer incident should be assigned to its responsible team accurately in a timely manner.