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1. Microphysics regimes due to haze–cloud interactions: cloud oscillation and cloud collapse

   https://doi.org/10.5194/acp-25-3785-2025  

   Yang, Fan; Sadi, Hamed Fahandezh; Shaw, Raymond A; Hoffmann, Fabian; Hou, Pei; Wang, Aaron; Ovchinnikov, Mikhail (January 2025, Atmospheric Chemistry and Physics)

   Abstract. It is known that aqueous haze particles can be activated into cloud droplets in a supersaturated environment. However, haze–cloud interactions have not been fully explored, partly because haze particles are not represented in most cloud-resolving models. Here, we conduct a series of large-eddy simulations (LESs) of a cloud in a convection chamber using a haze-capable Eulerian-based bin microphysics scheme to explore haze–cloud interactions over a wide range of aerosol injection rates. Results show that the cloud is in a slow microphysics regime at low aerosol injection rates, where the cloud responds slowly to an environmental change and droplet deactivation is negligible. The cloud is in a fast microphysics regime at moderate aerosol injection rates, where the cloud responds quickly to an environmental change and haze–cloud interactions are important. More interestingly, two more microphysics regimes are observed at high aerosol injection rates due to haze–cloud interactions. Cloud oscillation is driven by the oscillation of the mean supersaturation around the critical supersaturation of aerosol due to haze–cloud interactions. Cloud collapse happens under weaker forcing of supersaturation where the chamber transfers cloud droplets to haze particles efficiently, leading to a significant decrease (collapse) in cloud droplet number concentration. One special case of cloud collapse is the haze-only regime. It occurs at extremely high aerosol injection rates, where droplet activation is inhibited, and the sedimentation of haze particles is balanced by the aerosol injection rate. Our results suggest that haze particles and their interactions with cloud droplets should be considered, especially in polluted conditions. 

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2. Cloud Watching: Understanding Attacks Against Cloud-Hosted Services

   https://doi.org/10.1145/3618257.3624818  

   Izhikevich, Liz; Tran, Manda; Kallitsis, Michalis; Fass, Aurore; Durumeric, Zakir (October 2023, ACM)
3. Cloud Watching: Understanding Attacks Against Cloud-Hosted Services

   L. Izhikevich, M. Tran (October 2023, Internet Measurement Conference)
4. Testing Cloud Applications under Cloud-Uncertainty Performance Effects

   Wang, Wei; Tian, Ningjing; Huang, Sunzhou; He, Sen; Srivastava, Abhijeet; Soffa, Mary Lou; Pollock, Lori (April 2018, the IEEE Conference on Software Testing, Validation and Verification (ICST))

   The paradigm shift of deploying applications to the cloud has introduced both opportunities and challenges. Although clouds use elasticity to scale resource usage at runtime to help meet an application’s performance requirements, developers are still challenged by unpredictable performance, little control of execution environment, and differences among cloud service providers, all while being charged for their cloud usages. Application performance stability is particularly affected by multi-tenancy in which the hardware is shared among varying applications and virtual machines. Developers porting their applications need to meet performance requirements, but testing on the cloud under the effects of performance uncertainty is difficult and expensive, due to high cloud usage costs. This paper presents a first approach to testing an application with typical inputs for how its performance will be affected by performance uncertainty, without incurring undue costs of bruteforce testing in the cloud. We specify cloud uncertainty testing criteria, design a test-based strategy to characterize the blackbox cloud’s performance distributions using these testing criteria, and support execution of tests to characterize the resource usage and cloud baseline performance of the application to be deployed. Importantly, we developed a smart test oracle that estimates the application’s performance with certain confidence levels using the above characterization test results and determines whether it will meet its performance requirements. We evaluated our testing approach on both the Chameleon cloud and Amazon web services; results indicate that this testing strategy shows promise as a cost-effective approach to test for performance effects of cloud uncertainty when porting an application to the cloud. 

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5. EdgStr: Automating Client-Cloud to Client-Edge-Cloud Transformation

   An, Kijin; Tilevich, Eli (July 2024, Proceedings of the 44th IEEE International Conference on Distributed Computing (ICDCS 2024))