Search for: All records

This paper introduces Beldi, a library and runtime system for writing and composing fault-tolerant and transactional stateful serverless functions. Beldi runs on existing providers and lets developers write complex stateful applications that require fault tolerance and transactional semantics without the need to deal with tasks such as load balancing or maintaining virtual machines. Beldi’s contributions include extending the log-based fault-tolerant approach in Olive (OSDI 2016) with new data structures, transaction protocols, function invocations, and garbage collection. They also include adapting the resulting framework to work over a federated environment where each serverless function has sovereignty over its own data. We implement three applications on Beldi, including a movie review service, a travel reservation system, and a social media site. Our evaluation on 1,000 AWS Lambdas shows that Beldi’s approach is effective and affordable. 

Network functions like firewalls, proxies, and NATs are instances of distributed systems that lie on the critical path for a substantial fraction of today's cloud applications. Unfortunately, validating these systems remains difficult due to their complex stateful, timed, and distributed behaviors. In this paper, we present the design and implementation of Aragog, a runtime verification system for distributed network functions that achieves high expressiveness, fidelity, and scalability. Given a property of interest, Aragogefficiently checks running systems for violations of the property with a scale-out architecture consisting of a collection of global verifiers and local monitors. To improve performance and reduce communication overhead, Aragog includes an array of optimizations that leverage properties of networked systems to suppress provably unnecessary system events and to shard verification over every available local and global component. We evaluate Aragog over several network functions including a NAT Gateway that powers Azure, identifying both design and implementation bugs in the process. 

Resource disaggregation is a new architecture for data centers in which resources like memory and storage are decoupled from the CPU, managed independently, and connected through a high-speed network. Recent work has shown that although disaggregated data centers (DDCs) provide operational benefits, applications running on DDCs experience degraded performance due to extra network latency between the CPU and their working sets in main memory. DBMSs are an interesting case study for DDCs for two main reasons: (1) DBMSs normally process data-intensive workloads and require data movement between different resource components; and (2) disaggregation drastically changes the assumption that DBMSs can rely on their own internal resource management. We take the first step to thoroughly evaluate the query execution performance of production DBMSs in disaggregated data centers. We evaluate two popular open-source DBMSs (MonetDB and PostgreSQL) and test their performance with the TPC-H benchmark in a recently released operating system for resource disaggregation. We evaluate these DBMSs with various configurations and compare their performance with that of single-machine Linux with the same hardware resources. Our results confirm that significant performance degradation does occur, but, perhaps surprisingly, we also find settings in which the degradation is minor or where DDCs actually improve performance.