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

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