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Item type:Publication, Access status: without files , Context-aware routing in fog computing systems(IEEE, 2021-08-06); ; ; Fog computing enables the execution of IoT applications on compute nodes which reside both in the cloud and at the edge of the network. To achieve this, most fog computing systems route the IoT data on a path which starts at the data source, and goes through various edge and cloud nodes. Each node on this path may accept the data if there are available resources to process this data locally. Otherwise, the data is forwarded to the next node on path. Notably, when the data is forwarded (rather than accepted), the communication latency increases by the delay to reach the next node. To avoid this, we propose a routing mechanism which maintains a history of all nodes that have accepted data of each context in the past. By processing this history, our mechanism sends the data directly to the closest node that tends to accept data of the same context. This lowers the forwarding by nodes on path, and can reduce the communication latency. We evaluate this approach using both prototype- and simulation-based experiments which show reduced communication latency (by up to 23%) and lower number of hops traveled (by up to 73%), compared to a state-of-the-art method.Publicationtype: Journal Article63 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Nomadic applications traveling in the fogThe emergence of the Internet of Things introduces new challenges like network congestion or data privacy. However, it also provides opportunities, such as computational resources close to data sources, which can be pooled to realize Fogs to run software applications on the edge of the network. To foster this new type of resources, we revisit the concept of mobile agents and evolve them to so-called nomadic applications, which allow addressing vital challenges for the Internet of Things. In this paper, we propose a system design to realize nomadic applications and discuss several challenges that need to be addressed in order to apply them to real-world scenarios.Publicationtype: Conference Paper22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Cost-efficient enactment of stream processing topologiesThe continuous increase of unbound streaming data poses several challenges to established data stream processing engines. One of the most important challenges is the cost-efficient enactment of stream processing topologies under changing data volume. These data volume pose different loads to stream processing systems whose resource provisioning needs to be continuously updated at runtime. First approaches already allow for resource provisioning on the level of virtual machines (VMs), but this only allows for coarse resource provisioning strategies. Based on current advances and benefits for containerized software systems, we have designed a cost-efficient resource provisioning approach and integrated it into the runtime of the Vienna ecosystem for elastic stream processing. Our resource provisioning approach aims to maximize the resource usage for VMs obtained from cloud providers. This strategy only releases processing capabilities at the end of the VMs minimal leasing duration instead of releasing them eagerly as soon as possible as it is the case for threshold-based approaches. This strategy allows us to improve the service level agreement compliance by up to 25% and a reduction for the operational cost of up to 36%.Publicationtype: Journal Article24 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Optimization of Complex Elastic ProcessesBusiness Process Management is a matter of great importance in different industries and application areas. In many cases, it involves the execution of resource-intensive tasks in terms of computing power such as CPU and RAM. Due to the emergence of Cloud computing, theoretically unlimited resources can be used for the enactment of business processes. These Cloud resources render several challenges for Business Process Management Systems to ensure a predefined Quality of Service level during Cloud-based process enactment. Therefore, new solutions for process scheduling and resource allocation are required to tackle these challenges. Within this paper, we present a novel approach to schedule business processes and optimize the used Cloud-based computational resources in a cost-efficient way, thus realizing so-called elastic processes. For that, we specify the Service Instance Placement Problem, i.e., an optimization model which defines the setting of how service instances are scheduled among resources. Through extensive evaluations we show the benefits of our contributions and compare the novel approach against a baseline which follows an ad hoc approach.Publicationtype: Journal Article19 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Elastic Stream Processing for Distributed EnvironmentsThe current development towards the Internet of Things introduces the need for more flexibility in stream processing. To counter these challenges, the authors propose elastic stream processing for distributed environments, building on top of cloud computing and allowing a scalable and more flexible solution compared to traditional approaches.Publicationtype: Journal Article23