Cost-efficient enactment of stream processing topologies

Hochreiner, Christoph; Vögler, Michael; Schulte, Stefan; Dustdar, Schahram

doi:10.7717/peerj-cs.141

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…The deployment of SPOs on a set of hosts is also known as operator placement. The decision where to place SPOs can be made during the design time of a stream processing topology, but also during runtime [13]. Figure 1 also shows that -apart from the public cloud or a potentially available private cloud -in a typical IoT scenario, further computational resources exist at the edge of the network, provided by so-called fog devices.…”

Section: Deployment Of Stream Processing Operators In the Fogmentioning

confidence: 99%

Towards a Framework for Data Stream Processing in the Fog

Hießl

Hochreiner

Schulte

2019

Informatik Spektrum

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Section: Deployment Of Stream Processing Operators In the Fogmentioning

confidence: 99%

Towards a Framework for Data Stream Processing in the Fog

Hießl

Hochreiner

Schulte

2019

Informatik Spektrum

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“…As presented in different other publications, e.g., [7], [16], [17], [18], the usage of resource and task scheduling algorithms during the execution of processes further lower the cost of process execution. To reap the benefits of containers, such as exemplified for data stream processing [19] or service-oriented computing [20], [21], appropriate algorithms have to be devised. These algorithms have to be able to cope with lightweight containers as entities of a potentially much higher amount as compared to coarsegrained VM-based solutions.…”

Section: Introductionmentioning

confidence: 99%

ViePEP-C: A Container-Based Elastic Process Platform

Waibel

Hochreiner

Schulte

et al. 2021

IEEE Trans. Cloud Comput.

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Business Process Management Systems (BPMS) need to be able to take into account the fluctuating demand for computational resources during the execution of business process activities. Today, BPMS rely on the leasing and releasing of virtual machines (VMs) on cloud resources, which leads to a rather coarse-grained allocation of computational resources. This may result in an increase in the execution cost, flexibility restrictions, and a negative impact on the Quality of Service. In order to overcome these drawbacks, we introduce the Vienna Platform for Elastic Processes on Containers (ViePEP-C). ViePEP-C is an elastic BPMS that uses containers instead of VMs for the execution of business process activities on cloud resources, leading to a more fine-grained execution environment. To achieve this, ViePEP-C offers cloud controller, monitoring and business process execution functionalities and provides a platform for different resource and task scheduling algorithms. To evaluate the benefits of ViePEP-C, we further present a resource and task scheduling algorithm and show that, by using containers as execution environment, the execution cost can be decreased by over 20% (compared to a state-of-the-art VM-based scheduling algorithm) while considering a high service level.

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“…Fog computing has been named as an enabler to provide IoT applications in many different scenarios, especially with regard to smart systems, for example, smart cities, smart buildings, or smart factories ( Stojmenovic, 2014 ; Hu et al, 2017 ; He et al, 2018 ; Katona & Panfilov, 2018 ). By deploying IoT applications in the fog, it is possible, for example, to prefilter data for stream processing or to conduct IoT data processing on-site instead of relying on cloud-based computational resources ( Bonomi et al, 2014 ; Hochreiner et al, 2017 ). This leads to lower latency in IoT scenarios ( Puliafito et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

FogFrame: a framework for IoT application execution in the fog

Skarlat¹,

Schulte²

2021

PeerJ Computer Science

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Recently, a multitude of conceptual architectures and theoretical foundations for fog computing have been proposed. Despite this, there is still a lack of concrete frameworks to setup real-world fog landscapes. In this work, we design and implement the fog computing framework FogFrame—a system able to manage and monitor edge and cloud resources in fog landscapes and to execute Internet of Things (IoT) applications. FogFrame provides communication and interaction as well as application management within a fog landscape, namely, decentralized service placement, deployment and execution. For service placement, we formalize a system model, define an objective function and constraints, and solve the problem implementing a greedy algorithm and a genetic algorithm. The framework is evaluated with regard to Quality of Service parameters of IoT applications and the utilization of fog resources using a real-world operational testbed. The evaluation shows that the service placement is adapted according to the demand and the available resources in the fog landscape. The greedy placement leads to the maximum utilization of edge devices keeping at the edge as many services as possible, while the placement based on the genetic algorithm keeps devices from overloads by balancing between the cloud and edge. When comparing edge and cloud deployment, the service deployment time at the edge takes 14% of the deployment time in the cloud. If fog resources are utilized at maximum capacity, and a new application request arrives with the need of certain sensor equipment, service deployment becomes impossible, and the application needs to be delegated to other fog resources. The genetic algorithm allows to better accommodate new applications and keep the utilization of edge devices at about 50% CPU. During the experiments, the framework successfully reacts to runtime events: (i) services are recovered when devices disappear from the fog landscape; (ii) cloud resources and highly utilized devices are released by migrating services to new devices; (iii) and in case of overloads, services are migrated in order to release resources.

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Cost-efficient enactment of stream processing topologies

Cited by 8 publications

References 26 publications

Towards a Framework for Data Stream Processing in the Fog

Towards a Framework for Data Stream Processing in the Fog

ViePEP-C: A Container-Based Elastic Process Platform

FogFrame: a framework for IoT application execution in the fog

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