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Calvin Constrained: A Framework for IoT Applications in Heterogeneous Environments
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
Vise andre og tillknytning
2017 (engelsk)Inngår i: 2017 IEEE 37TH International Conference on Distributed Computing Systems (ICDCS 2017) / [ed] Lee, K., Liu, L., IEEE Computer Society, 2017, s. 1063-1073Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Calvin is an IoT framework for application development, deployment and execution in heterogeneous environments, that includes clouds, edge resources, and embedded or constrained resources. Inside Calvin, all the distributed resources are viewed as one environment by the application. The framework provides multi-tenancy and simplifies development of IoT applications, which are represented using a dataflow of application components (named actors) and their communication. The idea behind Calvin poses similarity with the serverless architecture and can be seen as Actor as a Service instead of Function as a Service. This makes Calvin very powerful as it does not only scale actors quickly but also provides an easy actor migration capability. In this work, we propose Calvin Constrained, an extension to the Calvin framework to cover resource-constrained devices. Due to limited memory and processing power of embedded devices, the constrained side of the framework can only support a limited subset of the Calvin features. The current implementation of Calvin Constrained supports actors implemented in C as well as Python, where the support for Python actors is enabled by using MicroPython as a statically allocated library, by this we enable the automatic management of state variables and enhance code re-usability. As would be expected, Python-coded actors demand more resources over C-coded ones. We show that the extra resources needed are manageable on current off-the-shelve micro-controller-equipped devices when using the Calvin framework.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2017. s. 1063-1073
Serie
IEEE International Conference on Distributed Computing Systems, ISSN 1063-6927
Emneord [en]
IoT, Distributed Cloud, Serverless Architecture, Dataflow Application Development Model
HSV kategori
Identifikatorer
URN: urn:nbn:se:umu:diva-142013DOI: 10.1109/ICDCS.2017.181ISI: 000412759500098ISBN: 978-1-5386-1791-5 (tryckt)ISBN: 978-1-5386-1792-2 (digital)ISBN: 978-1-5386-1793-9 (tryckt)OAI: oai:DiVA.org:umu-142013DiVA, id: diva2:1158912
Konferanse
37th IEEE International Conference on Distributed Computing Systems (ICDCS), JUN 05-08, 2017, Atlanta, GA
Forskningsfinansiär
Swedish Research Council, C0590801Tilgjengelig fra: 2017-11-21 Laget: 2017-11-21 Sist oppdatert: 2018-09-07bibliografisk kontrollert
Inngår i avhandling
1. Resource allocation for Mobile Edge Clouds
Åpne denne publikasjonen i ny fane eller vindu >>Resource allocation for Mobile Edge Clouds
2018 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Recent advances in Internet technologies have led to the proliferation of new distributed applications in the transportation, healthcare, mining, security, and entertainment sectors. The emerging applications have characteristics such as being bandwidth-hungry, latency-critical, and applications with a user population contained within a limited geographical area, and require high availability, low jitter, and security.

One way of addressing the challenges arising because of these emerging applications, is to move the computing capabilities closer to the end-users, at the logical edge of a network, in order to improve the performance, operating cost, and reliability of applications and services. These distributed new resources and software stacks, situated on the path between today's centralized data centers and devices in close proximity to the last mile network, are known as Mobile Edge Clouds (MECs). The distributed MECs provides new opportunities for the management of compute resources and the allocation of applications to those resources in order to minimize the overall cost of application deployment while satisfying end-user demands in terms of application performance.

However, these opportunities also present three significant challenges. The first challenge is where and how much computing resources to deploy along the path between today's centralized data centers and devices for cost-optimal operations. The second challenge is where and how much resources should be allocated to which applications to meet the applications' performance requirements while minimizing operational costs. The third challenge is how to provide a framework for application deployment on resource-constrained IoT devices in heterogeneous environments. 

This thesis addresses the above challenges by proposing several models, algorithms, and simulation and software frameworks. In the first part, we investigate methods for early detection of short-lived and significant increase in demand for computing resources (also called spikes) which may cause significant degradation in the performance of a distributed application. We make use of adaptive signal processing techniques for early detection of spikes. We then consider trade-offs between parameters such as the time taken to detect a spike and the number of false spikes that are detected. In the second part, we study the resource planning problem where we study the cost benefits of adding new compute resources based on performance requirements for emerging applications. In the third part, we study the problem of allocating resources to applications by formulating as an optimization problem, where the objective is to minimize overall operational cost while meeting the performance targets of applications. We also propose a hierarchical scheduling framework and policies for allocating resources to applications based on performance metrics of both applications and compute resources. In the last part, we propose a framework, Calvin Constrained, for resource-constrained devices, which is an extension of the Calvin framework and supports a limited but essential subset of the features of the reference framework taking into account the limited memory and processing power of the resource-constrained IoT devices.

sted, utgiver, år, opplag, sider
Umeå: Umeå University, 2018. s. 30
Serie
Report / UMINF, ISSN 0348-0542 ; 18.10
Emneord
Mobile Edge Clouds, Edge/Fog Computing, IoTs, Distributed Resource Allocation
HSV kategori
Forskningsprogram
datalogi; datorteknik
Identifikatorer
urn:nbn:se:umu:diva-151480 (URN)978-91-7601-925-2 (ISBN)
Disputas
2018-10-01, MA121, MIT-huset, Umeå, 13:30 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2018-09-10 Laget: 2018-09-04 Sist oppdatert: 2018-09-07bibliografisk kontrollert

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