Eager to see the finest serverless meetup videos from August? You’ve come to the right place! See our hand-picked selection below.
Visualizing Cloud Systems
Cloud-native systems increasingly integrate services, or functions. Microservices and serverless patterns produce many small parts. See how effective visualization matters in solution design and implementation. Understand emergent visualization by example.
Infrastructure as Software
Paul Stack will demonstrate why writing infrastructure in general programming languages is a better way for infrastructure management. Pulumi is an open source tool that allows users to write their infrastructure code in TypeScript, Python, DotNet or Go.
General purpose languages allow infrastructure code to have integrated testing, compile time checks as well as being able to create infrastructure APIs and is more suited to infrastructure management than DSLs, JSON or YAML. In addition, he will demonstrate how to build infrastructure that manages systems across multiple cloud providers.
Redesigning Minecraft-Like Games as Serverless Systems
How can Minecraft-like games become scalable cloud services? Hundreds of Minecraft-like games, that is, games acting as modifiable virtual environments (MVEs), are currently played by over 100 million players. Surprisingly, they do not scale and are frequently not published as cloud services. Jesse Donkervliet, Animesh Trivedi, and Alexandru Iosup envision a new architecture for large-scale MVEs, supporting much larger numbers of concurrent users by scaling up and out using serverless technology. In their vision, developers focus on the game (business) logic, while cloud providers manage resource management and scheduling (RMS) and guarantee non-functional properties. They provide a definition for MVEs, model their services and deployments, present a vision for large-scale MVEs architected as serverless systems, and suggest concrete steps towards realizing this vision.
Lightweight Isolation for Efficient Stateful Serverless Computing
Serverless computing is an excellent fit for big data processing because it can scale quickly and cheaply to thousands of parallel functions. Existing serverless platforms isolate functions in ephemeral, stateless containers, preventing them from directly sharing memory. This forces users to duplicate and serialise data repeatedly, adding unnecessary performance and resource costs. Simon Shillaker and Peter Pietzuch believe that a new lightweight isolation approach is needed, which supports sharing memory directly between functions and reduces resource overheads.
Resource Allocation in serverless
Resource allocation for serverless query processing is a challenge. Unfortunately, prior approaches have treated queries as black boxes, thereby missing significant resource optimization opportunities. In this paper, Malay Bag, Alekh Jindal, and Hiren Patel proposed a plan-aware resource allocation approach where the resources are adaptively allocated based on the runtime characteristics of the query plan. They show the savings opportunity from such an allocation scheme over production SCOPE workloads at Microsoft. They present their current implementation of a greedy version that periodically estimates the peak resource for the remaining of the query as the query execution progresses. Their experimental evaluation shows that such an implementation could already save more than 8% resource usage over one of our production virtual clusters. They conclude by opening the discussion on various strategies for plan-aware resource allocation and their implications on the cloud computing stack.
Want to see even more Serverless content? How about our monthly roundup from October?