Reliable capacity provisioning for distributed cloud/edge/fog computing applications

Östberg, Per-Olov; Byrne, James; Casari, Paolo; Eardley, Philip; Anta, Antonio Fernández; Forsman, Johan; Kennedy, John; Duc, Thang Le; Mariño, Manuel Noya; Loomba, Radhika; Pena, Miguel Angel Lopez; Veiga, Jose Lopez; Lynn, Theo; Mancuso, Vincenzo; Svorobej, Sergej; Torneus, Anders; Wesner, Stefan; Willis, Peter; Domaschka, Jörg

doi:10.1109/eucnc.2017.7980667

Cited by 61 publications

(34 citation statements)

References 24 publications

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“…Understanding data load generation and its propagation through a given system is a worthwhile approach for deciding on (optimal) resource placements. Data load prediction has been presented as a solution for proactive system remediation [33]. In this case, historical (big) data stored at cloud and live data collected in the fog and edge devices are used to feed models and predict important metrics, such as resource usage and content popularity distribution [34].…”

Section: Resource Managementmentioning

confidence: 99%

“…Even though the simulated annealing technique goes outside the traditional scope of discrete event simulation (DES), the general annealing approach can be useful in testing different parameter variations within edge computing such as virtual Content Delivery Network (vCDN) deployments [47] or exploring infrastructure provisioning options [48]. Moving to the fog and edge domain the need for simulating resource management approaches remains one of the main simulation analysis features [33].…”

Section: Resource Managementmentioning

confidence: 99%

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Simulating Fog and Edge Computing Scenarios: An Overview and Research Challenges

et al. 2019

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The fourth industrial revolution heralds a paradigm shift in how people, processes, things, data and networks communicate and connect with each other. Conventional computing infrastructures are struggling to satisfy dramatic growth in demand from a deluge of connected heterogeneous end points located at the edge of networks while, at the same time, meeting quality of service levels. The complexity of computing at the edge makes it increasingly difficult for infrastructure providers to plan for and provision resources to meet this demand. While simulation frameworks are used extensively in the modelling of cloud computing environments in order to test and validate technical solutions, they are at a nascent stage of development and adoption for fog and edge computing. This paper provides an overview of challenges posed by fog and edge computing in relation to simulation.

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Section: Resource Managementmentioning

confidence: 99%

Section: Resource Managementmentioning

confidence: 99%

Simulating Fog and Edge Computing Scenarios: An Overview and Research Challenges

et al. 2019

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“…In terms of edge computing resource provisioning, the ongoing Horizon 2020 RECAP project [4] proposes an integrated cloud-edge-fog architecture aimed at solving application placement, infrastructure management and capacity provisioning. Cloud/Edge infrastructure monitoring is enriched with application, infrastructure and workload models, which are in turn fed into an optimisation system that orchestrates applications and continuously configures the infrastructure.…”

Section: Edge Computing Resource and Service Provisioningmentioning

confidence: 99%

“…Classification process implemented in a fog node can be used to detect electric connections and human activities. Other services can be implemented using IoT communication 4…”

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confidence: 99%

Deployment of IoT Edge and Fog Computing Technologies to Develop Smart Building Services

et al. 2018

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Advances in embedded systems, based on System-on-a-Chip (SoC) architectures, have enabled the development of many commercial devices that are powerful enough to run operating systems and complex algorithms. These devices integrate a set of different sensors with connectivity, computing capacities and cost reduction. In this context, the Internet of Things (IoT) potential increases and introduces other development possibilities: “Things” can now increase computation near the source of the data; consequently, different IoT services can be deployed on local systems. This paradigm is known as “edge computing” and it integrates IoT technologies and cloud computing systems. Edge computing reduces the communications’ bandwidth needed between sensors and the central data centre. Management of sensors, actuators, embedded devices and other resources that may not be continuously connected to a network (such as smartphones) are required for this method. This trend is very attractive for smart building designs, where different subsystems (energy, climate control, security, comfort, user services, maintenance, and operating costs) must be integrated to develop intelligent facilities. In this work, a method to design smart services based on the edge computing paradigm is analysed and proposed. This novel approach overcomes some drawbacks of existing designs related to interoperability and scalability of services. An experimental architecture based on embedded devices is described. Energy management, security system, climate control and information services are the subsystems on which new smart facilities are implemented.

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“…Capacity provisioning is of crucial importance in modern distributed computation infrastructures. To determine the size of data centers, and properly dimension the resources to be allocated in each geographic location, most data center owners use predictions of the computational needs [29,36]. The computational resource within a data center is then used to serve requests coming from multiple clients, providing the illusion of infinite capacity and, as a result, the possibility of bounding the latency [5,6,14,24,25,42].…”

Section: Introductionmentioning

confidence: 99%

Cloud Application Predictability through Integrated Load-Balancing and Service Time Control

Nylander

Andrén

Årzén

et al. 2018

2018 IEEE International Conference on Autonomic Computing (ICAC)

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Cloud computing provides the illusion of infinite capacity to application developers. However, data center provisioning is complex and it is still necessary to handle the risk of capacity shortages. To handle capacity shortages, graceful degradation techniques sacrifice user experience for predictability. In all these cases, the decision making policy that determines the degradation interferes with other decisions happening at the infrastructure level, like load-balancing choices. Here, we reconcile the two approaches, developing a load-balancing strategy that also handles capacity shortages and graceful degradation when necessary. The proposal is based on a sound control-theoretical approach. The design of the approach avoids the pitfalls of interfering control decisions. We describe the technique and provide evidence that it allows us to achieve higher performance in terms of emergency management and user experience.

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Reliable capacity provisioning for distributed cloud/edge/fog computing applications

Cited by 61 publications

References 24 publications

Simulating Fog and Edge Computing Scenarios: An Overview and Research Challenges

Simulating Fog and Edge Computing Scenarios: An Overview and Research Challenges

Deployment of IoT Edge and Fog Computing Technologies to Develop Smart Building Services

Cloud Application Predictability through Integrated Load-Balancing and Service Time Control

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