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Recent data stream processing systems (DSPSs) can achieve excellent performance when processing large volumes of data under tight latency constraints. However, they sacrifice support for concurrent state access that eases the burden of…
Serverless functions provide high levels of parallelism, short startup times, and "pay-as-you-go" billing. These attributes make them a natural substrate for data analytics workflows. However, the impossibility of direct communication…
As tremendous amount of data being generated everyday from human activity and from devices equipped with sensing capabilities, cloud computing emerges as a scalable and cost-effective platform to store and manage the data. While benefits of…
We present a framework for performance optimization in serverless edge-cloud platforms using dynamic task placement. We focus on applications for smart edge devices, for example, smart cameras or speakers, that need to perform processing…
Transitioning cloud-based Hadoop from IaaS to PaaS, which are commercially conceptualized as pay-as-you-go or pay-per-use, often reduces the associated system costs. However, managed Hadoop systems do present a black-box behavior to the…
With the increasing importance of distributed scientific workflows, there is a critical need to ensure Quality of Service (QoS) constraints, such as minimizing time or limiting execution to resource subsets. However, the unpredictable…
A crowdsourced stream processing system (CSP) is a system that incorporates crowdsourced tasks in the processing of a data stream. This can be seen as enabling crowdsourcing work to be applied on a sample of large-scale data at high speed,…
The Serverless Computing is becoming increasingly popular due to its ease of use and fine-grained billing. These features make it appealing for stateful application or serverless workflow. However, current serverless workflow systems…
Graph processing systems are essential for analyzing large-scale data with complex relationships, yet most existing frameworks rely on statically provisioned clusters, resulting in poor elasticity and inefficient resource utilization under…
funcX is a distributed function as a service (FaaS) platform that enables flexible, scalable, and high performance remote function execution. Unlike centralized FaaS systems, funcX decouples the cloud-hosted management functionality from…
Stream processing is extensively used in the IoT-to-Cloud spectrum to distill information from continuous streams of data. Streaming applications usually run in dedicated Stream Processing Engines (SPEs) that adopt the DataFlow model, which…
This paper argues for decoupling transaction processing from existing two-layer cloud-native databases and making transaction processing as an independent service. By building a transaction as a service (TaaS) layer, the transaction…
In a world, where complexity increases on a daily basis the Function-as-a-Service (FaaS) cloud model seams to take countermeasures. In comparison to other cloud models, the fast evolving FaaS increasingly abstracts the underlying…
Data stream processing is an increasingly important topic due to the prevalence of smart devices and the demand for real-time analytics. Geo-distributed streaming systems, where cloud-based queries utilize data streams from multiple…
A cloud-based data stream management system (DSMS) handles fast data by utilizing the massively parallel processing capabilities of the underlying platform. An important property of such a DSMS is elasticity, meaning that nodes can be…
Function-as-a-Service (FaaS) has recently emerged to reduce the deployment cost of running cloud applications compared to Infrastructure-as-a-Service (IaaS). FaaS follows a serverless 'pay-as-you-go' computing model; it comes at a higher…
Distributed Stream Processing (DSP) focuses on the near real-time processing of large streams of unbounded data. To increase processing capacities, DSP systems are able to dynamically scale across a cluster of commodity nodes, ensuring a…
The function-as-a-service (FaaS) paradigm is envisioned as the next generation of cloud computing systems that mitigate the burden for cloud-native application developers by abstracting them from cloud resource management. However, it does…
Cloud Service Providers deliver their products in form of 'as-a-Service', which are typically categorized by the level of abstraction. This approach hides the implementation details and shows only functionality to the user. However, the…
Experimental data can aid in gaining insights about a system operation, as well as determining critical aspects of a modelling or simulation process. In this paper, we analyze the data acquired from an extensive experimentation process in a…