In Serverless Computing, function cold-start is a major issue that causes delay of the system. Various solutions have been proposed to address function cold-start issue, among which keeping containers alive after function completion is an easy and commonly adopted way in real serverless clouds. However, when reusing warm containers for function warm starts, existing systems only match functions to containers with the same configurations. This greatly limits the warm resource utilization. Our analysis of real-world applications reveals that many serverless applications share the same operating system and language frameworks. Thus, we propose multi-level container reuse that tries to reduce the startup latency of functions using "similar" containers to greatly improve warm resource utilization. Due to the complexity of selecting the best container reuse solutions, we designed a Deep Reinforcement Learning (DRL) based scheduler to efficiently and effectively address the problem. Moreover, we released a new serverless benchmark named FStartBench that contains detailed package information for comparing the effectiveness of different function cold-start methods. Experiments based on FStartBench show that, given a warm resource pool with fixed size, our DRL-based scheduler can achieve up to 53% reduction on the average function startup latency compared to state-of-the-art solutions.

Array databases are important data management systems for scientific applications. In array databases, version handling is an important problem due to the no-overwrite feature of scientific data. Existing studies for optimizing data versioning in array databases are relatively simple, which either focus on minimizing storage sizes or improving simple version chains. In this paper, we focus on two challenges: (1) how to balance the tradeoff between storage size and query time for numerous version data, which may have derivative relationships with each other; (2) how to dynamically maintain this balance with continuously added new versions. To address the above challenges, this paper presents DyVer, a versioning framework for SciDB which is one of the most well-known array databases. DyVer includes two techniques, including an efficient storage layout optimizer to quickly reduce data query time under storage capacity constraint and a version segment technique to cope with dynamic version additions. We evaluate DyVer using real-world scientific datasets. Results show that DyVer can achieve up to 95% improvement on the average query time compared to state-of-the-art data versioning techniques under the same storage capacity constraint.

Recently, we have witnessed many HPC applications developed and hosted in the cloud, which can benefit from the elastic and diversified resources on the cloud, while on the other hand confronting high costs for executing the long-running HPC applications. Although public clouds such as Amazon EC2 offer spot instances with dynamic and usually low prices compared to on-demand ones, the spot prices can vary significantly and sometimes can even be more expensive than on-demand prices of the same type. Previous work on reducing the monetary cost for HPC applications using spot instances focused on designing fault tolerance techniques or selecting appropriate instance types/bid prices to make good usage of the low spot prices. However, with the recent update of spot pricing model on Amazon EC2, these work may become either inefficient or invalid. In this paper, we present FarSpot which is an optimization framework for HPC applications in the latest cloud spot market with the goal of minimizing application cost while ensuring performance constraints. FarSpot provides accurate long-term price prediction for a wide range of spot instance types using ensemble-based learning method. It further incorporates a cost-aware deadline assignment algorithm to distribute application deadline to each task according to spot price changes. With the assigned subdeadline of each task, FarSpot dynamically migrates tasks among spot instances to reduce execution cost. Evaluation results using real HPC benchmark show that 1) the prediction error of FarSpot is very low (below 3%), 2) FarSpot reduced the monetary cost by 32% on average compared to state-of-the-art algorithms, and 3) FarSpot satisfies the user-specified deadline constraints at all time.