FaaSnap is a VM snapshot-based platform that uses a set of complementary optimizations to improve function cold-start performance for Function-as-a-Service (FaaS) applications. Compact loading set files take better advantage of prefetching. Per-region memory mapping tailors page fault handling depending on the contents of different guest VM memory regions. Hierarchical overlapping memory-mapped regions simplify the mapping process. Concurrent paging allows the guest VM to start execution immediately, rather than pausing until the working set is loaded. Altogether, FaaSnap significantly reduces guest VM page fault handling time on the critical path and improves overall function loading performance. Experiments on serverless benchmarks show that it reduces end-to-end function execution by up to 3.5x compared to state-of-the-art, and on average is only 3.5% slower than snapshots cached in memory. Moreover, we show that FaaSnap is resilient to changes of working set and remains efficient under bursty workloads and when snapshots are located in remote storage.

Burst-parallel serverless applications invoke thousands of short-lived distributed functions to complete complex jobs such as data analytics, video encoding, or compilation. While these tasks execute in seconds, starting and configuring the virtual network they rely on is a major bottleneck that can consume up to 84% of total startup time. In this paper we characterize the magnitude of this network cold start problem in three popular overlay networks, Docker Swarm, Weave, and Linux Overlay. We focus on end-to-end startup time that encompasses both the time to boot a group of containers as well as interconnecting them. Our primary observation is that existing overlay approaches for serverless networking scale poorly in short-lived serverless environments. Based on our findings we develop Particle, a network stack tailored for multi-node serverless overlay networks that optimizes network creation without sacrificing multi-tenancy, generality, or throughput. When integrated into a serverless burst-parallel video processing pipeline, Particle improves application runtime by 2.4--3X over existing overlays.

Game livestreaming is hugely popular and growing. Each month, Twitch hosts over two million unique broadcasters with a collective audience of 140 million unique viewers. Despite its success, livestreaming services are costly to run. AWS and Azure both charge hundreds of dollars to encode 100 hours of multi-bitrate video, and potentially thousands each month to transfer the video data of one gamer to a relatively small audience.In this work, we demonstrate that mobile edge devices are ready to play a more central role in multi-bitrate livestreaming. In particular, we explore a new strategy for game livestreaming that we call a thin-cloud approach. Under a thin-cloud approach, livestreaming services rely on commodity web infrastructure to store and distribute video content and leverage hardware acceleration on edge devices to transcode video and boost the video quality of low-bitrate streams. We have built a prototype system called LevelUp that embodies the thin-cloud approach, and using our prototype we demonstrate that mobile hardware acceleration can support real-time video transcoding and significantly boost the quality of low-bitrate video through a machine-learning technique called super resolution. We show that super-resolution can improve the visual quality of low-resolution game streams by up to 88% while requiring approximately half the bandwidth of higher-bitrate streams. Finally, energy experiments show that LevelUp clients consume only 5% of their battery capacity watching 30 minutes of video.

Sprocket is a highly configurable, stage-based, scalable, serverless video processing framework that exploits intra-video parallelism to achieve low latency. Sprocket enables developers to program a series of operations over video content in a modular, extensible manner. Programmers implement custom operations, ranging from simple video transformations to more complex computer vision tasks, in a simple pipeline specification language to construct custom video processing pipelines. Sprocket then handles the underlying access, encoding and decoding, and processing of video and image content across operations in a highly parallel manner. In this paper we describe the design and implementation of the Sprocket system on the AWS Lambda serverless cloud infrastructure, and evaluate Sprocket under a variety of conditions to show that it delivers its performance goals of high parallelism, low latency, and low cost (10s of seconds to process a 3,600 second video 1000-way parallel for less than $3).

Metadata scalability is critical for distributed systems as the storage scale is growing rapidly. Because of the strict consistency requirement of metadata, many existing metadata services utilize a fundamentally unscalable design for the sake of easy management, while others provide improved scalability but lead to unacceptable latency and management complexity. Without delivering scalable performance, metadata will be the bottleneck of the entire system. Based on the observation that real file dependencies are few, and there are usually more idempotent than non-idempotent operations, we propose a practical strategy, Replichard, allowing a tradeoff between metadata consistency and scalable performance. Replichard provides metadata services through a cluster of metadata servers, in which a flexible consistency scheme is adopted: strict consistency for non-idempotent operations with dynamic write-lock sharding, and relaxed consistency with accuracy estimations of return values where consistency for idempotent requests is relaxed to achieve high throughput. Write-locks are dynamically created at subtree-level and designated to independent metadata servers in an application-oriented manner. A subtree metadata update that occurs on a particular server is replicated to all metadata servers conforming to the application "start-end" semantics, resulting in an eventually consistent namespace. An asynchronous notification mechanism is also devised to enable users to deal with potential stale reads from operations of relaxed consistency. A prototype was implemented based on HDFS, and the experimental results show promising scalability and performance for both micro benchmarks and various real-world applications written in Pig, Hive and MapReduce.

Task scheduling is critical to reduce the make span of MapReduce jobs. It is an effective approach for scheduling optimization by improving the data locality, which involves attempting to locate a task and its related data block on the same node. However, recent studies have been insufficient in addressing the locality issue. This paper proposes BOLAS, a MapReducetask scheduling algorithm, which models the scheduling processes a bipartite-graph matching problem trying best to assign data block to the nearest task. By considering the divergence of node performance of distribution of data blocks in MapReduce cluster, BOLAS can achieve a high degree of data locality, guarantee minimal data transfer during execution, and reduces a job's makespan subsequently. As a dynamic algorithm, BOLAS solves the model using Kuhn-Munkres optimal matching algorithm, and can be deployed in either homogeneous or heterogeneous environments. In this study, BOLAS was implemented as a plug in for Hadoop, and the experimental results indicate that BOLAScan localize nearly 100% of the map tasks and reduce the execution time by up to 67.1%.

Highly available metadata services of distributed file systems are essential to cloud applications. However, existing highly available metadata designs lack client-oriented features that treat metadata discriminately, leading to a single metadata fault domain and low availability. After investigating the workload characteristics of Hadoop, we propose Client-Oriented METadata (COMET), a novel highly available metadata service design that divides and distributes metadata into independent regions in terms of clients. These regions are isolated fault domains inherently, and failures in one region will not break file operations in other regions. A prototype of COMET was implemented based on HDFS, and the experimental results show that COMET can significantly improve metadata availability of HDFS without obvious performance degradation. It can also deliver scalable performance and faster metadata recovery due to its decentralized architecture.