Qianlin Liang | publications

2024

Going Green for Less Green: Optimizing the Cost of Reducing Cloud Carbon Emissions

Hanafy, Walid, Liang, Qianlin, Bashir, Noman, Souza, Abel, Irwin, David, and Shenoy, Prashant

In Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) Jun 2024
CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency

Hanafy, Walid, Liang, Qianlin, Bashir, Noman, Irwin, David, and Shenoy, Prashant

In Proceedings of ACM Special Interest Group on Measurement and Evaluation (SIGMETRICS) Jun 2024

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2023

Energy Time Fairness: Balancing Fair Allocation of Energy and Time for GPU Workloads

Liang, Qianlin, Hanafy, Walid A., Bashir, Noman, Irwin, David, and Shenoy, Prashant

In Proceedings of the 8th ACM/IEEE Symposium on Edge Computing (SEC) Dec 2023

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Dělen: Enabling Flexible and Adaptive Model-Serving for Multi-Tenant Edge AI

Liang, Qianlin, Hanafy, Walid A., Bashir, Noman, Ali-Eldin, Ahmed, Irwin, David, and Shenoy, Prashant

In Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation (IoTDI) 2023

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Ecovisor: A Virtual Energy System for Carbon-Efficient Applications

Souza, Abel, Bashir, Noman, Murillo, Jorge, Hanafy, Walid, Liang, Qianlin, Irwin, David, and Shenoy, Prashant

In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) 2023

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Model-Driven Cluster Resource Management for AI Workloads in Edge Clouds

Liang, Qianlin, Hanafy, Walid A., Ali-Eldin, Ahmed, and Shenoy, Prashant

ACM Trans. Auton. Adapt. Syst. Mar 2023

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2020

AI on the Edge: Rethinking AI-based IoT Applications Using Specialized Edge Architectures

Liang, Qianlin, Shenoy, Prashant, and Irwin, David

In Proceedings of IEEE International Symposium on Workload Characterization, Beijing, China 2020

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@article{Liang2020AIOT, bibtex_show = {true}, title = {AI on the Edge: Rethinking AI-based IoT Applications Using Specialized Edge Architectures}, author = {Liang, Qianlin and Shenoy, Prashant and Irwin, David}, journal = {In Proceedings of IEEE International Symposium on Workload Characterization, Beijing, China}, year = {2020}, url = {https://arxiv.org/abs/2003.12488}, html = {https://arxiv.org/abs/2003.12488}, pdf = {liangIISWC2020edgeAI.pdf}, volume = {abs/2003.12488} }

2017

Exploiting Spot and Burstable Instances for Improving the Cost-Efficacy of In-Memory Caches on the Public Cloud

Wang, Cheng, Urgaonkar, Bhuvan, Gupta, Aayush, Kesidis, George, and Liang, Qianlin

In Proceedings of the Twelfth European Conference on Computer Systems 2017

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In order to keep the costs of operating in-memory storage on the public cloud low, we devise novel ideas and enabling modeling and optimization techniques for combining conventional Amazon EC2 instances with the cheaper spot and burstable instances. Whereas a naturally appealing way of using failure-prone spot instances is to selectively store unpopular ("cold") content, we show that a form of "hot-cold mixing" across regular and spot instances might be more cost-effective. To overcome performance degradation resulting from spot instance revocations, we employ a highly available passive backup using the recently emergent burstable instances. We show how the idiosyncratic resource allocations of burstable instances make them ideal candidates for such a backup. We implement all our ideas in an EC2-based memcached prototype. Using simulations and live experiments on our prototype, we show that (i) our hot-cold mixing, informed by our modeling of spot prices, helps improve cost savings by 50-80% compared to only using regular instances, and (ii) our burstable-based backup helps reduce performance degradation during spot revocation, e.g., the 95% latency during failure recovery improves by 25% compared to a backup based on regular instances.
@inproceedings{10.1145/3064176.3064220, bibtex_show = {true}, author = {Wang, Cheng and Urgaonkar, Bhuvan and Gupta, Aayush and Kesidis, George and Liang, Qianlin}, title = {Exploiting Spot and Burstable Instances for Improving the Cost-Efficacy of In-Memory Caches on the Public Cloud}, year = {2017}, isbn = {9781450349383}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3064176.3064220}, html = {https://doi.org/10.1145/3064176.3064220}, doi = {10.1145/3064176.3064220}, booktitle = {Proceedings of the Twelfth European Conference on Computer Systems}, pages = {620–634}, numpages = {15}, keywords = {burstable instance, in-memory caches, public cloud, spot instance}, location = {Belgrade, Serbia}, pdf = {wangEurosys2017spot.pdf}, series = {EuroSys '17} }
An Empirical Analysis of Amazon EC2 Spot Instance Features Affecting Cost-Effective Resource Procurement

Wang, Cheng, Liang, Qianlin, and Urgaonkar, Bhuvan

In Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering 2017

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Many cost-conscious public cloud workloads ("tenants") are turning to Amazon EC2’s spot instances because, on average, these instances offer significantly lower prices (up to 10 times lower) than on-demand and reserved instances of comparable advertized resource capacities. To use spot instances effectively, a tenant must carefully weigh the lower costs of these instances against their poorer availability. Towards this, we empirically study four features of EC2 spot instance operation that a cost-conscious tenant may find useful to model. Using extensive evaluation based on both historical and current spot instance data, we show shortcomings in the state-of-the-art modeling of these features that we overcome. Our analysis reveals many novel properties of spot instance operation some of which offer predictive value while others do not. Using these insights, we design predictors for our features that offer a balance between computational efficiency (allowing for online resource procurement) and cost-efficacy. We explore "case studies" wherein we implement prototypes of dynamic spot instance procurement advised by our predictors for two types of workloads. Compared to the state-of-the-art, our approach achieves (i) comparable cost but much better performance (fewer bid failures) for a latency-sensitive in-memory Memcached cache, and (ii) an additional 18% cost-savings with comparable (if not better than) performance for a delay-tolerant batch workload.
@inproceedings{10.1145/3030207.3030210, bibtex_show = {true}, author = {Wang, Cheng and Liang, Qianlin and Urgaonkar, Bhuvan}, title = {An Empirical Analysis of Amazon EC2 Spot Instance Features Affecting Cost-Effective Resource Procurement}, year = {2017}, isbn = {9781450344043}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3030207.3030210}, html = {https://doi.org/10.1145/3030207.3030210}, doi = {10.1145/3030207.3030210}, booktitle = {Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering}, pages = {63–74}, numpages = {12}, keywords = {spot instance features, resource procurement}, location = {L'Aquila, Italy}, pdf = {wangIcpe2017spot.pdf}, series = {ICPE '17} }

2016

Spot Characterization: What Are the Right Features to Model

Liang, Qianlin, Wang, Cheng, and Urgaonkar, Bhuvan

In The 1st International Workshop on System Analytics and Characterization 2016

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@inproceedings{liangsac2016spot, bibtex_show = {true}, author = {Liang, Qianlin and Wang, Cheng and Urgaonkar, Bhuvan}, title = {Spot Characterization: What Are the Right Features to Model}, year = {2016}, booktitle = {The 1st International Workshop on System Analytics and Characterization}, url = {https://drive.google.com/file/d/0B-75gd4swZPMZ1QwT1lwM1l6dEU/view?resourcekey=0-yI1D1oXRuivJGi4IKbaQmA}, pdf = {SAC_2016_paper_3.pdf} }