FaaS (function-as-a-service) is becoming a popular workload in cloud environments due to its virtues such as auto-scaling and pay-as-you-go. High-level languages like JavaScript and Java are commonly used in FaaS for programmability, but their managed runtimes complicate memory management in the cloud. This paper first observes the issue of frozen garbage, which is caused by freezing cached function instances where their threads have been paused but the unused memory (e.g., garbage) is not reclaimed due to the semantic gap between FaaS and the managed runtime. This paper presents the first characterization of the negative effects induced by frozen garbage with various functions, which uncovers that it can occupy more than half of FaaS instances' memory resources on average. To this end, this paper proposes Desiccant, a freeze-aware memory manager for managed workloads in FaaS, which reclaims idle memory resources consumed by frozen garbage from managed runtime instances and thus notably improves memory efficiency. The evaluation on various FaaS workloads shows that Desiccant can reduce FaaS functions' peak memory consumption by up to 6.72×. Such saved memory consumption allows caching more FaaS instances to reduce the frequency of cold boots (creating instances before function execution) and p99 latency by up to 4.49× and 37.5%, respectively.

Function-as-a-service (FaaS), an emerging cloud computing paradigm, is expected to provide strong elasticity due to its promise to auto-scale fine-grained functions rapidly. Although appealing for applications with good parallelism and dynamic workload, this paper shows that it is non-trivial to adapt existing monolithic applications (like web services) to FaaS due to their complexity. To bridge the gap between complicated web services and FaaS, this paper proposes a runtime-based Semi-FaaS execution model, which dynamically extracts time-consuming code snippets (closures) from applications and offloads them to FaaS platforms for execution. It further proposes BeeHive, an offloading framework for Semi-FaaS, which relies on the managed runtime to provide a fallback-based execution model and addresses the performance issues in traditional offloading mechanisms for FaaS. Meanwhile, the runtime system of BeeHive selects offloading candidates in a user-transparent way and supports efficient object sharing, memory management, and failure recovery in a distributed environment. The evaluation using various web applications suggests that the Semi-FaaS execution supported by BeeHive can reach sub-second resource provisioning on commercialized FaaS platforms like AWS Lambda, which is up to two orders of magnitude better than other alternative scaling approaches in cloud computing.

FaaS, or function as a service, promises unprecedented cost-efficiency and elasticity thanks to its on-demand and fine-grained execution nature. However, modern FaaS platforms mainly adopt virtual machines (VMs) or containers as a computing abstraction, which incurs costs like high startup latency, large memory footprint, and high communication overhead. Multi-tasking virtual machines (MVMs), which allow co-executing multiple functions in the same managed language runtime, are appealing for FaaS due to their lightweight nature. Unfortunately, existing MVMs are not designed for FaaS. The proposed abstraction of MVMs does not provide specialized support for fine-grained, latency-sensitive functions and their chain-like execution patterns. Meanwhile, the underlying runtime still contains many global modules and lacks essential support for function-level resource accounting and isolation. To this end, this work proposesFlock, a retrofitted MVM for FaaS execution, which provides FaaS-aware abstractions namedfuncletsand enhanced runtime support for isolation.Flockis implemented atop the HotSpot JVM of OpenJDK 8. Performance evaluation shows thatFlockresults in up to three orders of magnitude performance improvement over state-of-the-art FaaS platforms like OpenWhisk while providing sufficient isolation support for FaaS functions.

Fast, byte-addressable non-volatile memory (NVM) embraces both near-DRAM latency and disk-like persistence, which has generated considerable interests to revolutionize system software stack and programming models. However, it is less understood how NVM can be combined with managed runtime like Java virtual machine (JVM) to ease persistence management. This paper proposes Espresso, a holistic extension to Java and its runtime, to enable Java programmers to exploit NVM for persistence management with high performance. Espresso first provides a general persistent heap design called Persistent Java Heap (PJH) to manage persistent data as normal Java objects. The heap is then strengthened with a recoverable mechanism to provide crash consistency for heap metadata. Espresso further provides a new abstraction called Persistent Java Object (PJO) to provide an easy-to-use but safe persistence programming model for programmers to persist application data. Evaluation confirms that Espresso significantly outperforms state-of-art NVM support for Java (i.e., JPA and PCJ) while being compatible to data structures in existing Java programs.