Matteo Nardelli

Distributed QoS-aware scheduling in storm

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.

Optimal operator placement for distributed stream processing applications

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 computing) to define the applications placement. Several placement policies have been proposed in the literature, but they are based on different assumptions and optimization goals and, as such, they are not completely comparable to each other. In this paper we study the placement problem for distributed DSP applications. Our contributions are twofold. We provide a general formulation of the optimal DSP placement (for short, ODP) as an Integer Linear Programming problem which takes explicitly into account the heterogeneity of computing and networking resources and which encompasses - as special cases - the different solutions proposed in the literature. We present an ODP-based scheduler for the Apache Storm DSP framework. This allows us to compare some well-known centralized and decentralized placement solutions. We also extensively analyze the ODP scalability with respect to various parameter settings.

On QoS-aware scheduling of data stream applications over fog computing infrastructures

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 open source, scalable, and fault-tolerant DSP system designed for locally distributed clusters. We made it suitable to operate in a geographically distributed and highly variable environment; to this end, we extended Storm with new components that allow to execute a distributed QoS-aware scheduler and give self-adaptation capabilities to the system. In this paper we provide a thorough experimental evaluation of the proposed solution using two sets of DSP applications: the former is characterized by a simple topology with different requirements; the latter comprises some well known applications (i.e., Word Count, Log Processing). The results show that the distributed QoS-aware scheduler outperforms the centralized default one, improving the application performance and enhancing the system with runtime adaptation capabilities. However, complex topologies involving many operators may cause some instability that can decrease the DSP application availability.

Towards QoS-Aware Fog Service Placement

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 emitted and consumed by IoT devices. Despite existing foundations, research on fog computing is still at its very beginning. A major research question is how to exploit the ubiquitous presence of small and cheap computing devices at the edge of the network in order to successfully execute IoT services. Therefore, in this paper, we study the placement of IoT services on fog resources, taking into account their QoS requirements. We show that our optimization model prevents QoS violations and leads to 35% less cost of execution if compared to a purely cloud-based approach.

Elastic stateful stream processing in storm

The advent of the Big Data era and the diffusion of Cloud computing have renewed the interest in Data Stream Processing (DSP) applications, which can timely extract useful information from distributed data sources. Due to the unpredictable rate at which the sources may produce data, DSP applications demand high dynamism. Storm has emerged as a widely adopted DSP system, which, although having many desirable features, shows some limitations due to the lack of adaptation capabilities. In this paper, we extend Storm with two mechanisms that support the run-time adaptation of DSP applications. Specifically, we introduce new components that allow automatic elasticity and stateful migration of the application components. The experimental results show the benefits of the newly introduced functionalities that, albeit equipped with proof of concept policies, allow to properly cope with workload variations while improving the resource utilization of the underlying infrastructure.

Optimal operator deployment and replication for elastic distributed data stream processing

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 this paper, we deal with a key problem for the effective runtime management of a DSP application in geo‐distributed environments: We investigate the placement and replication decisions while considering the application and resource heterogeneity and the migration overhead, so to select the optimal adaptation strategy that can minimize migration costs while satisfying the application quality of service (QoS) requirements. We present elastic DSP replication and placement (EDRP), a unified framework for the QoS‐aware initial deployment and runtime elasticity management of DSP applications. In EDRP, the deployment and runtime decisions are driven by the solution of a suitable integer linear programming problem, whose objective function captures the relative importance between QoS goals and reconfiguration costs. We also present the implementation of EDRP and the related mechanisms on Apache Storm. We conduct a thorough experimental evaluation, both numerical and prototype‐based, that shows the benefits achieved by EDRP on the application performance.

Optimized IoT service placement in the fog

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 applications in a distributed manner. This concept is known as fog computing. While the theoretical foundations of fog computing are already established, there is a lack of resource provisioning approaches to enable the exploitation of fog-based computational resources. To resolve this shortcoming, we present a conceptual fog computing framework. Then, we model the service placement problem for IoT applications over fog resources as an optimization problem, which explicitly considers the heterogeneity of applications and resources in terms of Quality of Service attributes. Finally, we propose a genetic algorithm as a problem resolution heuristic and show, through experiments, that the service execution can achieve a reduction of network communication delays when the genetic algorithm is used, and a better utilization of fog resources when the exact optimization method is applied.

Elastic Provisioning of Virtual Machines for Container Deployment

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 container deployment problem, which deals with the identification of an elastic set of computing machines that can host and execute those containers, while considering the diversity of their requirements. In this paper, we provide a general formulation of the Elastic provisioning of Virtual machines for Container Deployment (for short, EVCD) as an Integer Linear Programming problem, which takes explicitly into account the heterogeneity of container requirements and virtual machine resources. Besides optimizing multiple QoS metrics, EVCD can reallocate containers at runtime, when a QoS improvement can be achieved. Using the proposed formulation as benchmark, we evaluate two well-known heuristics, i.e., greedy first-fit and round-robin, that are usually adopted for solving the container deployment problem.

Optimal Operator Replication and Placement for Distributed Stream Processing Systems

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 produced data, the operators that compose a DSP application can be replicated and placed on multiple, possibly distributed, computing nodes, so to process the incoming data flow in parallel. Moreover, to better exploit the abundance of diffused computational resources (e.g., Fog computing), recent trends investigate the possibility of decentralizing the DSP application placement. In this paper, we present and evaluate a general formulation of the optimal DSP replication and placement (ODRP) as an integer linear programming problem, which takes into account the heterogeneity of application requirements and infrastructural resources. We integrate ODRP as prototype scheduler in the Apache Storm DSP framework. By leveraging on the DEBS 2015 Grand Challenge as benchmark application, we show the benefits of a joint optimization of operator replication and placement and how ODRP can optimize different QoS metrics, namely response time, internode traffic, cost, availability, and a combination thereof.

Towards hierarchical autonomous control for elastic data stream processing in the Fog

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 of elastic DSP applications. It revolves around a two layered approach. At the lower level, distributed components issue requests for adapting the deployment of DSP operations as to adjust to changing workload conditions. At the higher level, a per-application centralized component works on a broader time scale; it oversees the application behavior and grants reconfigurations to control the application performance while limiting the negative effect of their enactment, i.e., application downtime. We have implemented the proposed solution in our distributed Storm prototype and evaluated its behavior adopting simple policies. The experimental results are promising and show that, even with simple policies, it is possible to limit the number of reconfigurations while at the same time guaranteeing an adequate level of application performance.