Distributed Data Stream Processing and Edge Computing: A Survey on Resource Elasticity and Future Directions

TL;DR

This survey reviews recent advances in distributed data stream processing and edge computing, focusing on resource elasticity challenges and future research directions in scalable, low-latency data handling.

Contribution

It provides a comprehensive overview of current stream processing engines, discusses resource elasticity in edge-cloud environments, and identifies open challenges and future research directions.

Findings

Edge computing enhances data stream processing scalability.

Resource elasticity is complex in distributed edge-cloud environments.

Existing solutions address some elasticity challenges but gaps remain.

Abstract

Under several emerging application scenarios, such as in smart cities, operational monitoring of large infrastructure, wearable assistance, and Internet of Things, continuous data streams must be processed under very short delays. Several solutions, including multiple software engines, have been developed for processing unbounded data streams in a scalable and efficient manner. More recently, architecture has been proposed to use edge computing for data stream processing. This paper surveys state of the art on stream processing engines and mechanisms for exploiting resource elasticity features of cloud computing in stream processing. Resource elasticity allows for an application or service to scale out/in according to fluctuating demands. Although such features have been extensively investigated for enterprise applications, stream processing poses challenges on achieving elastic systems that can make efficient resource management decisions based on current load. Elasticity becomes even more challenging in highly distributed environments comprising edge and cloud computing resources. This work examines some of these challenges and discusses solutions proposed in the literature to address them.