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1. Supporting IoT Applications with Serverless Edge Clouds

   https://doi.org/10.1109/CloudNet51028.2020.9335805  

   Wang, I.; Liri, E.; Ramakrishnan, K. K. (November 2020, 2020 IEEE 9th International Conference on Cloud Networking (CloudNet))

   null (Ed.)

   Cloud computing has grown because of lowered costs due to economies of scale and multiplexing. Serverless computing exploits multiplexing in cloud computing however, for low latency required by IoT applications, the cloud should be moved nearer to the IoT device and the cold start problem should be addressed. Using a real-world dataset, we showed through implementation in an open-source cloud environment based on Knative that a serverless approach to manage IoT traffic is feasible, uses less resources than a serverfull approach and traffic prediction with prefetching can mitigate the cold start delay penalty. However applying the Knative framework directly to IoT traffic without considering the execution context gives unnecessary overhead. 

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2. An Extensible Orchestration and Protection Framework for Confidential Cloud Computing

   Ahmad, Adil; Schultz, Alex; Lee, Byoungyoung; Fonseca, Pedro (July 2023, 17th USENIX Symposium on Operating Systems Design and Implementation (OSDI 23))

   Confidential computing solutions are crucial to address the cloud privacy concerns. Although SGX has witnessed significant adoption in the cloud, the reliance on hardware implementation is restrictive for cloud providers in terms of orchestrating deployments and providing stronger security to their clients’ enclaves. eOPF addresses this limitation by providing a comprehensive, secure hypervisor-level instrumentation framework with the ability to monitor all enclave-OS interactions and implement protected services. eOPF overcomes several challenges including bridging the semantic gap between the hypervisor and SGX and attesting the co-location of the framework with enclaves. Using eOPF, we implement two protected services that provide platform resource orchestration and complementary enclave side-channel defense. Our evaluation shows that eOPF incurs very low performance overhead (<2%) in its default state and only a modest overhead (geometric mean of 17% on SPEC) when strong, complementary side-channel defenses are enabled, making eOPF an efficient and practical solution for the cloud. 

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3. Tackling Cold Start of Serverless Applications by Efficient and Adaptive Container Runtime Reusing

   https://doi.org/10.1109/Cluster48925.2021.00018  

   Suo, Kun; Son, Junggab; Cheng, Dazhao; Chen, Wei; Baidya, Sabur (September 2021, Tackling Cold Start of Serverless Applications by Efficient and Adaptive Container Runtime Reusing)

   During the past few years, serverless computing has changed the paradigm of application development and deployment in the cloud and edge due to its unique advantages, including easy administration, automatic scaling, built-in fault tolerance, etc. Nevertheless, serverless computing is also facing challenges such as long latency due to the cold start. In this paper, we present an in-depth performance analysis of cold start in the serverless framework and propose HotC, a container-based runtime management framework that leverages the lightweight containers to mitigate the cold start and improve the network performance of serverless applications. HotC maintains a live container runtime pool, analyzes the user input or configuration file, and provides available runtime for immediate reuse. To precisely predict the request and efficiently manage the hot containers, we design an adaptive live container control algorithm combining the exponential smoothing model and Markov chain method. Our evaluation results show that HotC introduces negligible overhead and can efficiently improve the performance of various applications with different network traffic patterns in both cloud servers and edge devices. 

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4. A Verified Confidential Computing as a Service Framework for Privacy Preservation

   Chen, Hongbo; Chen, Haobin; Sun, Mingshen; Li, Kang; Chen, Zhaofeng; Wang, XiaoFeng (August 2023, the Proceedings of the 32nd USENIX Security Symposium)

   Joe Calandrino and Carmela Troncoso (Ed.)

   As service providers are moving to the cloud, users are forced to provision sensitive data to the cloud. Confidential computing leverages hardware Trusted Execution Environment (TEE) to protect data in use, no longer requiring users’ trust to the cloud. The emerging service model, Confidential Computing as a Service (CCaaS), is adopted by service providers to offer service similar to the Function-as-a-Serivce manner. However, privacy concerns are raised in CCaaS, especially in multi-user scenarios. CCaaS need to assure the data providers that the service does not leak their privacy to any unauthorized parties and clear their data after the service. To address such privacy concerns with security guarantees, we first formally define the security objective, Proof of Being Forgotten (PoBF), and prove under which security constraints PoBF can be satisfied. Then, these constraints serve as guidelines in the implementation of the PoBF-compliant Framework (PoCF). PoCF consists of a generic library for different hardware TEEs, CCaaS prototype enclaves, and a verifier to prove PoBF-compliance. PoCF leverages Rust’s robust type system and security features, to construct a verified state machine with privacy-preserving contracts. Last, the experiment results show that the protections introduced by PoCF incur minor runtime performance overhead. 

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5. ACTS: Autonomous Cost-Efficient Task Orchestration for Serverless Analytics

   https://doi.org/10.1109/IWQoS57198.2023.10188782  

   Jananie Jarachanthan, Li Chen (June 2023, Proceedings of 31st IEEE/ACM International Symposium on Quality of Service (IWQoS 2023))

   Serverless computing has become increasingly popular for cloud applications, due to its compelling properties of high-level abstractions, lightweight runtime, high elasticity and pay-per-use billing. In this revolutionary computing paradigm shift, challenges arise when adapting data analytics applications to the serverless environment, due to the lack of support for efficient state sharing, which attract ever-growing research attention. In this paper, we aim to exploit the advantages of task level orchestration and fine-grained resource provisioning for data analytics on serverless platforms, with the hope of fulfilling the promise of serverless deployment to the maximum extent. To this end, we present ACTS, an autonomous cost-efficient task orchestration framework for serverless analytics. ACTS judiciously schedules and coordinates function tasks to mitigate cold-start latency and state sharing overhead. In addition, ACTS explores the optimization space of fine-grained workload distribution and function resource configuration for cost efficiency. We have deployed and implemented ACTS on AWS Lambda, evaluated with various data analytics workloads. Results from extensive experiments demonstrate that ACTS achieves up to 98% monetary cost reduction while maintaining superior job completion time performance, in comparison with the state-of-the-art baselines. 

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