Beate Ottenwälder

Bio: Beate Ottenwälder is an academic researcher from University of Stuttgart. The author has contributed to research in topics: Complex event processing & Event (computing). The author has an hindex of 8, co-authored 11 publications receiving 873 citations. Previous affiliations of Beate Ottenwälder include Bosch.

Mobile fog: a programming model for large-scale applications on the internet of things

Abstract: The ubiquitous deployment of mobile and sensor devices is creating a new environment, namely the Internet of Things(IoT), that enables a wide range of future Internet applications. In this work, we present Mobile Fog, a high level programming model for the future Internet applications that are geospatially distributed, large-scale, and latency-sensitive. We analyze use cases for the programming model with camera network and connected vehicle applications to show the efficacy of Mobile Fog. We also evaluate application performance through simulation.

Incremental deployment and migration of geo-distributed situation awareness applications in the fog

Abstract: Geo-distributed Situation Awareness applications are large in scale and are characterized by 24/7 data generation from mobile and stationary sensors (such as cameras and GPS devices); latency-sensitivity for converting sensed data to actionable knowledge; and elastic and bursty needs for computational resources. Fog computing [7] envisions providing computational resources close to the edge of the network, consequently reducing the latency for the sense-process-actuate cycle that exists in these applications. We propose Foglets, a programming infrastructure for the geo-distributed computational continuum represented by fog nodes and the cloud. Foglets provides APIs for a spatio-temporal data abstraction for storing and retrieving application generated data on the local nodes, and primitives for communication among the resources in the computational continuum. Foglets manages the application components on the Fog nodes. Algorithms are presented for launching application components and handling the migration of these components between Fog nodes, based on the mobility pattern of the sensors and the dynamic computational needs of the application. Evaluation results are presented for a Fog network consisting of 16 nodes using a simulated vehicular network as the workload. We show that the discovery and deployment protocol can be executed in 0.93 secs, and joining an already deployed application can be as quick as 65 ms. Also, QoS-sensitive proactive migration can be accomplished in 6 ms.

MigCEP: operator migration for mobility driven distributed complex event processing

Abstract: A recent trend in communication networks --- sometimes referred to as fog computing --- offers to execute computational tasks close to the access points of the networks. This enables real-time applications, like mobile Complex Event Processing (CEP), to significantly reduce end-to-end latencies and bandwidth usage. Most work studying the placement of operators in such an environment completely disregards the migration costs. However, the mobility of users requires frequent migration of operators, together with possibly large state information, to meet latency restrictions and save bandwidth in the infrastructure.This paper presents a placement and migration method for providers of infrastructures that incorporate cloud and fog resources. It ensures application-defined end-to-end latency restrictions and reduces the network utilization by planning the migration ahead of time. Furthermore, we present how the application knowledge of the CEP system can be used to improve current live migration techniques for Virtual Machines to reduce the required bandwidth during the migration. Our evaluations show that we safe up to 49% of the network utilization with perfect knowledge about a users mobility pattern and up to 27% of the network utilization when considering the uncertainty of those patterns.

MCEP: A Mobility-Aware Complex Event Processing System

Abstract: With the proliferation of mobile devices and sensors, complex event proceesing (CEP) is becoming increasingly important to scalably detect situations in real time. Current CEP systems are not capable of dealing efficiently with highly dynamic mobile consumers whose interests change with their location. We introduce the distributed mobile CEP (MCEP) system which automatically adapts the processing of events according to a consumer's location. MCEP significantly reduces latency, network utilization, and processing overhead by providing on-demand and opportunistic adaptation algorithms to dynamically assign event streams and computing resources to operators of the MCEP system.

Opportunistic spatio-temporal event processing for mobile situation awareness

Abstract: With the proliferation of mobile devices and sensors, mobile situation awareness is becoming an important class of applications. The key requirement of this class of applications is low-latency processing of events stemming from sensor data in order to provide timely situational information to mobile users. To satisfy the latency requirement, we propose an opportunistic spatio-temporal event processing system that uses prediction-based continuous query handling. Our system predicts future query regions for moving consumers and starts processing events early so that the live situational information is available when the consumer reaches the future location. In contrast to existing systems, our system provides timely information about a consumer's current position by hiding computation latency for processing recent events. To evaluate our system, we measure the quality of results and timeliness of live situational information with various query parameters. Our evaluation shows that we can achieve highly meaningful query results with near-zero latency in most cases.

Mobile Edge Computing: A Survey

Abstract: Mobile edge computing (MEC) is an emergent architecture where cloud computing services are extended to the edge of networks leveraging mobile base stations. As a promising edge technology, it can be applied to mobile, wireless, and wireline scenarios, using software and hardware platforms, located at the network edge in the vicinity of end-users. MEC provides seamless integration of multiple application service providers and vendors toward mobile subscribers, enterprises, and other vertical segments. It is an important component in the 5G architecture which supports variety of innovative applications and services where ultralow latency is required. This paper is aimed to present a comprehensive survey of relevant research and technological developments in the area of MEC. It provides the definition of MEC, its advantages, architectures, and application areas; where we in particular highlight related research and future directions. Finally, security and privacy issues and related existing solutions are also discussed.

A Survey of Fog Computing: Concepts, Applications and Issues

Abstract: Despite the increasing usage of cloud computing, there are still issues unsolved due to inherent problems of cloud computing such as unreliable latency, lack of mobility support and location-awareness. Fog computing can address those problems by providing elastic resources and services to end users at the edge of network, while cloud computing are more about providing resources distributed in the core network. This survey discusses the definition of fog computing and similar concepts, introduces representative application scenarios, and identifies various aspects of issues we may encounter when designing and implementing fog computing systems. It also highlights some opportunities and challenges, as direction of potential future work, in related techniques that need to be considered in the context of fog computing.

iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments

Abstract: Summary Internet of Things (IoT) aims to bring every object (eg, smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive volume of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay that is caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real-time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need an evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit of RAM consumption and execution time is verified under different circumstances.

Finding your Way in the Fog: Towards a Comprehensive Definition of Fog Computing

Abstract: The cloud is migrating to the edge of the network, where routers themselves may become the virtualisation infrastructure, in an evolution labelled as "the fog". However, many other complementary technologies are reaching a high level of maturity. Their interplay may dramatically shift the information and communication technology landscape in the following years, bringing separate technologies into a common ground. This paper offers a comprehensive definition of the fog, comprehending technologies as diverse as cloud, sensor networks, peer-to-peer networks, network virtualisation functions or configuration management techniques. We highlight the main challenges faced by this potentially breakthrough technology amalgamation.