Cloud computing relies on a deep stack of system layers: virtual machine, operating system, distributed middleware and language runtime. However, those numerous, distributed, virtual layers prevent any low-level understanding of the properties of FaaS applications, considered as programs running on real hardware. As a result, most research analyses only consider coarse-grained properties such as global performance of an application, and existing datasets include only sparse data. FaaSLoad is a tool to gather fine-grained data about performance and resource usage of the programs that run on Function-as-a-Service cloud platforms. It considers individual instances of functions to collect hardware and operating-system performance information, by monitoring them while injecting a workload. FaaSLoad helps building a dataset of function executions to train machine learning models, studying at fine grain the behavior of function runtimes, and replaying real workload traces for in situ observations. This research software project aims at being useful to cloud system researchers with features such as guaranteeing reproducibility and correctness, and keeping up with realistic FaaS workloads. Our evaluations show that FaaSLoad helps us understanding the properties of FaaS applications, and studying the latter under real conditions.

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.

With the increasing popularity of containers, managing them on top of virtual machines becomes a common practice, called nested virtualization. This paper presents BrFusion and Hostlo, two solutions that address each of two networking issues of nested virtualization: network virtualization duplication and virtual machine-bounded pod deployments. The first issue lengthens network packet paths while the second issue leads to resource fragmentation. For instance, in respect with the first issue, we measured a throughput degradation of about 68% and a latency increase of about 31% in comparison with a single networking layer. We prototype BrFusion and Hostlo in Linux KVM/QEMU, Docker and Kubernetes systems. The evaluation results show that BrFusion leads to the same performance as a single-layer virtualization deployment. Concerning Hostlo, the results show that more than 11% of cloud clients see their cloud utilization cost reduced by down to 40%.

In a modern data center (DC), a large majority of costs arise from energy consumption. The most popular technique used to mitigate this issue is virtualization and more precisely virtual machine (VM) consolidation. Although consolidation may increase server usage by about 5-10%, it is difficult to actually witness server loads greater than 50%. By analyzing the traces from our cloud provider partner, confirmed by previous research work, we have identified that some VMs have sporadic moments of data computation followed by large periods of idleness. These VMs often hinder the consolidation system which cannot further increase the energy efficiency of the DC. In this paper we propose a novel DC power management system called Drowsy-DC, which is able to identify the aforementioned VMs which have matching patterns of idleness. These VMs can thus be colocated on the same server so that their idle periods are exploited to put the server to a low power mode (suspend to RAM) until some data computation is required. While introducing a negligible overhead, our system is able to significantly improve any VM consolidation system; evaluations showed improvements up to 81% and more when compared to OpenStack Neat.