Lotus: Serverless In-Transit Data Processing for Edge-based Pub/Sub

Publish-subscribe systems are a popular approach for edge-based IoT use cases: Heterogeneous, constrained edge devices can be integrated easily, with message routing logic offloaded to edge message brokers. Message processing, however, is still done on constrained edge devices. Complex content-based filtering, the transformation between data representations, or message extraction place a considerable load on these systems, and resulting superfluous message transfers strain the network. In this paper, we propose Lotus, adding in-transit data processing to an edge publish-subscribe middleware in order to offload basic message processing from edge devices to brokers. Specifically, we leverage the Function-as-a-Service paradigm, which offers support for efficient multi-tenancy, scale-to-zero, and real-time processing. With a proof-of-concept prototype of Lotus, we validate its feasibility and demonstrate how it can be used to offload sensor data transformation to the publish-subscribe messaging middleware

ProFaaStinate: Delaying Serverless Function Calls to Optimize Platform Performance

Function-as-a-Service (FaaS) enables developers to run serverless applications without managing operational tasks. In current FaaS platforms, both synchronous and asynchronous calls are executed immediately. In this paper, we present ProFaaStinate, which extends serverless platforms to enable delayed execution of asynchronous function calls. This allows platforms to execute calls at convenient times with higher resource availability or lower load. ProFaaStinate is able to optimize performance without requiring deep integration into the rest of the platform, or a complex systems model. In our evaluation, our prototype built on top of Nuclio can reduce request response latency and workflow duration while also preventing the system from being overloaded during load peaks. Using a document preparation use case, we show a 54% reduction in average request response latency. This reduction in resource usage benefits both platforms and users as cost savings.Comment: Accepted for publication in Proc. of 9th International Workshop on Serverless Computing (WoSC 23

Managing Data Replication and Distribution in the Fog with FReD

The heterogeneous, geographically distributed infrastructure of fog computing poses challenges in data replication, data distribution, and data mobility for fog applications. Fog computing is still missing the necessary abstractions to manage application data, and fog application developers need to re-implement data management for every new piece of software. Proposed solutions are limited to certain application domains, such as the IoT, are not flexible in regard to network topology, or do not provide the means for applications to control the movement of their data. In this paper, we present FReD, a data replication middleware for the fog. FReD serves as a building block for configurable fog data distribution and enables low-latency, high-bandwidth, and privacy-sensitive applications. FReD is a common data access interface across heterogeneous infrastructure and network topologies, provides transparent and controllable data distribution, and can be integrated with applications from different domains. To evaluate our approach, we present a prototype implementation of FReD and show the benefits of developing with FReD using three case studies of fog computing applications

Fusionize: Improving Serverless Application Performance through Feedback-Driven Function Fusion

Serverless computing increases developer productivity by removing operational concerns such as managing hardware or software runtimes. Developers, however, still need to partition their application into functions, which can be error-prone and adds complexity: Using a small function size where only the smallest logical unit of an application is inside a function maximizes flexibility and reusability. Yet, having small functions leads to invocation overheads, additional cold starts, and may increase cost due to double billing during synchronous invocations. In this paper we present Fusionize, a framework that removes these concerns from developers by automatically fusing the application code into a multi-function orchestration with varying function size. Developers only need to write the application code following a lightweight programming model and do not need to worry how the application is turned into functions. Our framework automatically fuses different parts of the application into functions and manages their interactions. Leveraging monitoring data, the framework optimizes the distribution of application parts to functions to optimize deployment goals such as end-to-end latency and cost. Using two example applications, we show that Fusionizecan automatically and iteratively improve the deployment artifacts of the application

Streaming vs. Functions: A Cost Perspective on Cloud Event Processing

In cloud event processing, data generated at the edge is processed in real-time by cloud resources. Both distributed stream processing (DSP) and Function-as-a-Service (FaaS) have been proposed to implement such event processing applications. FaaS emphasizes fast development and easy operation, while DSP emphasizes efficient handling of large data volumes. Despite their architectural differences, both can be used to model and implement loosely-coupled job graphs. In this paper, we consider the selection of FaaS and DSP from a cost perspective. We implement stateless and stateful workflows from the Theodolite benchmarking suite using cloud FaaS and DSP. In an extensive evaluation, we show how application type, cloud service provider, and runtime environment can influence the cost of application deployments and derive decision guidelines for cloud engineers