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Cloud applications based on the "Functions as a Service" (FaaS) paradigm have become very popular. Yet, due to their stateless nature, they must frequently interact with an external data store, which limits their performance. To mitigate this issue, we introduce OFC, a transparent, vertically and horizontally elastic in-memory caching system for FaaS platforms, distributed over the worker nodes. OFC provides these benefits cost-effectively by exploiting two common sources of resource waste: (i) most cloud tenants overprovision the memory resources reserved for their functions because their footprint is non-trivially input-dependent and (ii) FaaS providers keep function sandboxes alive for several minutes to avoid cold starts. Using machine learning models adjusted for typical function input data categories (e.g., multimedia formats), OFC estimates the actual memory resources required by each function invocation and hoards the remaining capacity to feed the cache. We build our OFC prototype based on enhancements to the OpenWhisk FaaS platform, the Swift persistent object store, and the RAM-Cloud in-memory store. Using a diverse set of workloads, we show that OFC improves by up to 82 % and 60 % respectively the execution time of single-stage and pipelined functions. 

Ensuring high scalability (elastic scale-out and consolidation), as well as high availability (failure resiliency) are critical in encouraging adoption of software-based network functions (NFs). In recent years, two paradigms have evolved in terms of the way the NFs manage their state - namely the Stateful (state is coupled with the NF instance) and a Stateless (state is externalized to a datastore) manner. These two paradigms present unique challenges and opportunities for ensuring high scalability and high availability of NFs and NF chains. In this work, we assess the impact on ensuring the correctness of NF state including the implications of non-determinism in packet processing, and carefully analyze and present the benefits and disadvantages of the two state management paradigms. We leverage OpenNetVM and Redis in-memory datastore to implement both state management paradigms and empirically compare the two. Although the stateless paradigm is desirable for elastic scaling, our experimental results show that, even at line-rate packet processing (10 Gbps), stateful NFs can achieve chain-level failover across servers in a LAN incurring less than 10% performance. The state-of-the-art stateless counterparts incur severe throughput penalties. We observe 30-85% overhead on normal processing, depending on the mode of state updated to the externalized datastore.