Matteo Nardelli

Data Stream Processing (DSP) has emerged over the years as the reference paradigm for the analysis of continuous and fast information flows, which have often to be processed with low-latency requirements to extract insights and knowledge from raw data. Dealing with unbounded data flows, DSP applications are typically long-running and, thus, likely...

Cloud-native applications increasingly adopt the microservices architecture, which favors elasticity to satisfy the application performance requirements in face of variable workloads. To simplify the elasticity management, the trend is to create an auto-scaler instance per microservice, which controls its horizontal scalability by using the classic...

Emerging fog and edge computing environments enable the analysis of Big Data collected from devices (e.g., IoT sensors) with reduced latency compared to cloud-based solutions. In particular, many applications deal with continuous data flows in latency-sensitive domains (e.g., healthcare monitoring), where Data Stream Processing (DSP) systems repres...

The fast increasing presence of Internet-of-Things and fog computing resources exposes new challenges due to heterogeneity and non-negligible network delays among resources as well as the dynamism of operating conditions. Such a variable computing environment leads the applications to adopt an elastic and decentralized execution. To simplify the ap...

Software containers are changing the way applications are designed and executed. Moreover, in the last few years, we see the increasing adoption of container orchestration tools, such as Kubernetes, to simplify the management of multi-container applications. Kubernetes includes simple deployment policies that spread containers on computing resource...

In the last few years, a large number of real-time analytics applications rely on the Data Stream Processing (DSP) so to extract, in a timely manner, valuable information from distributed sources. Moreover, to efficiently handle the increasing amount of data, recent trends exploit the emerging presence of edge/Fog computing resources so to decentra...

Software containers are changing the way dis-tributed applications are executed and managed on cloud com-puting resources. Interestingly, containers offer the possibilityof handling workload fluctuations by exploiting both horizontaland vertical elasticity “on the fly”. However, most of the existingcontrol policies consider horizontal and vertical...

Elastic data stream processing enables applications to query and analyze streams of real time data. This is commonly facilitated by processing the flow of the data streams using a collection of stream processing operators which are placed in the cloud. However, the cloud follows a centralized approach which is prone to high latency delay. For avoid...

Data Stream Processing (DSP) applications should be capable to efficiently process high-velocity continuous data streams by elastically scaling the parallelism degree of their operators so to deal with high variability in the workload. Moreover, to efficiently use computing resources, modern DSP frameworks should seamlessly support infrastructure e...

By exploiting on-the-fly computation, Data Stream Processing (DSP) applications can process huge volumes of data in a near real-time fashion. Adapting the application parallelism at run-time is critical in order to guarantee a proper level of QoS in face of varying workloads. In this paper, we consider Reinforcement Learning based techniques in ord...

The capability of efficiently processing the data streams emitted by nowadays ubiquitous sensing devices enables the development of new intelligent services. Data Stream Processing (DSP) applications allow for processing huge volumes of data in near real-time. To keep up with the high volume and velocity of data, these applications can elastically...

Traditional networks are transformed to enable full integration of heterogeneous hardware and software functions, that are configured at runtime, with minimal time to market, and are provided to their end users on “as a service” principle. Therefore, a countless number of possibilities for further innovation and exploitation opens up. Network Funct...

Data Stream Processing (DSP) applications are widely used to develop new pervasive services, which require to seamlessly process huge amounts of data in a near real-time fashion. To keep up with the high volume of daily produced data, these applications need to dynamically scale their execution on multiple computing nodes, so to process the incomin...

The continuing growth of the Internet of Things (IoT) requires established stream processing engines (SPEs) to cope with new challenges, like the geographic distribution of IoT sensors and clouds hosting the SPEs. These challenges obligate SPEs to support distributed stream processing across different geographic locations which also require a new a...

In the Big Data era, Data Stream Processing (DSP) applications should be capable to seamlessly process huge amount of data. Hence, they need to dynamically scale their execution on multiple computing nodes so to adjust to unpredictable data source rate. In this paper, we present a hierarchical and distributed architecture for the autonomous control...

With the emerging IoT and Cloud-based networked systems that rely heavily on virtualization technologies, elasticity becomes a dominant system engineering attribute for providing QoS-aware services to their users. Although the concept of elasticity can introduce significant QoS and cost benefits, its implementation in real systems is full of challe...

Traditionally, research in Business Process Management has put a strong focus on centralized and intra-organizational processes. However, today's business processes are increasingly distributed, deviating from a centralized layout, and therefore calling for novel methodologies of detecting and responding to unforeseen events, such as errors occurri...

The Internet of Things (IoT) leads to an ever-growing presence of ubiquitous networked computing devices in public, business, and private spaces. These devices do not simply act as sensors, but feature computational, storage, and networking resources. Being located at the edge of the network, these resources can be exploited to execute IoT applicat...

Processing data in a timely manner, data stream processing (DSP) applications are receiving an increasing interest for building new pervasive services. Due to the unpredictability of data sources, these applications often operate in dynamic environments; therefore, they require the ability to elastically scale in response to workload variations. In...

Exploiting on-the-fly computation, Data Stream Processing (DSP) applications are widely used to process unbounded streams of data and extract valuable information in a near real-time fashion. As such, they enable the development of new intelligent and pervasive services that can improve our everyday life. To keep up with the high volume of daily pr...

Fog computing provides a decentralized approach to data processing and resource provisioning in the Internet of Things (IoT). Particular challenges of adopting fog-based computational resources are the adherence to geographical distribution of IoT data sources, the delay sensitivity of IoT services, and the potentially very large amounts of data em...

Docker containers enable to package an application together with all its dependencies and easily run it in any environment. Thanks to their ease of use and portability, containers are gaining an increasing interest and promise to change the way how Cloud platforms are designed and managed. For their execution in the Cloud, we need to solve the cont...

The rapid evolution of Internet of Things (IoT) devices (e.g., sensors and gateways) and the almost ubiquitous connectivity (e.g., 4G, Wi-Fi, RFID/NFC, Bluetooth, IEEE 802.15.4) are forcing us to radically rethink how to effectively deal with massive volume, velocity, and variety of big data produced by such IoT devices. There are currently 6.4 bil...

Docker containers wrap up a piece of software together with everything it needs for the execution and enable to easily run it on any machine. For their execution in the Cloud, we need to identify an elastic set of virtual machines that can accommodate those containers, while considering the diversity of their requirements. In this paper, we briefly...

In the last few years, several processing approaches have emerged to deal with Big Data. Exploiting on-the-fly computation, Data Stream Processing (DSP) applications can process unbounded streams of data to extract valuable information in a near real-time fashion. To keep up with the high volume of daily produced data, the operators that compose a...

In this paper, we present a solution to the DEBS 2016 Grand Challenge that leverages Apache Flink, an open source platform for distributed stream and batch processing. We design the system architecture focusing on the exploitation of parallelism and memory efficiency so to enable an effective processing of high volume data streams on a distributed...

Data Stream Processing (DSP) applications are widely used to timely extract information from distributed data sources, such as sensing devices, monitoring stations, and social networks. To successfully handle this ever increasing amount of data, recent trends investigate the possibility of exploiting decentralized computational resources (e.g., Fog...

Data Stream Processing (DSP) applications can extract, in a timely manner, valuable information from distributed data sources (e.g., sensing devices, social networks). These applications are subject to unpredictable and varying workloads and have to satisfy strict quality requirements, usually expressed in terms of latency, availability, and throug...

The advent of the Big Data era and the diffusion of the Cloud computing paradigm have renewed the interest in Data Stream Processing (DSP) applications, which can timely extract valuable information from an increasing number of data sources (e.g., sensing devices, social networks). The distribution of data sources, the huge and unpredictable volume...

The ever increasing diffusion of sensing and computing devices enables a new generation of data stream processing (DSP) applications that operate in a distributed Cloud environment. Despite this, most of the existing solutions, such as Apache Storm, are designed to run in a local cluster. In this paper we present our extension of Storm, which provi...

Storm is a distributed stream processing system that has recently gained increasing interest. We extend Storm to make it suitable to operate in a geographically distributed and highly variable environment such as that envisioned by the convergence of Fog computing, Cloud computing, and Internet of Things.

Fog computing is rapidly changing the distributed computing landscape by extending the Cloud computing paradigm to include wide-spread resources located at the network edges. This diffused infrastructure is well suited for the implementation of data stream processing (DSP) applications, by possibly exploiting local computing resources. Storm is an...