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Utility-based Allocation of Industrial IoT Applications in Mobile Edge Clouds
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Distributed Systems)
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
2018 (Engelska)Rapport (Övrigt vetenskapligt)
Abstract [en]

Mobile Edge Clouds (MECs) create new opportunities and challenges in terms of scheduling and running applications that have a wide range of latency requirements, such as intelligent transportation systems, process automation, and smart grids. We propose a two-tier scheduler for allocating runtime resources to Industrial Internet of Things (IIoTs) applications in MECs. The scheduler at the higher level runs periodically – monitors system state and the performance of applications – and decides whether to admit new applications and migrate existing applications. In contrast, the lower-level scheduler decides which application will get the runtime resource next. We use performance based metrics that tells the extent to which the runtimes are meeting the Service Level Objectives (SLOs) of the hosted applications. The Application Happiness metric is based on a single application’s performance and SLOs. The Runtime Happiness metric is based on the Application Happiness of the applications the runtime is hosting. These metrics may be used for decision-making by the scheduler, rather than runtime utilization, for example.

We evaluate four scheduling policies for the high-level scheduler and five for the low-level scheduler. The objective for the schedulers is to minimize cost while meeting the SLO of each application. The policies are evaluated with respect to the number of runtimes, the impact on the performance of applications and utilization of the runtimes. The results of our evaluation show that the high-level policy based on Runtime Happiness combined with the low-level policy based on Application Happiness outperforms other policies for the schedulers, including the bin packing and random strategies. In particular, our combined policy requires up to 30% fewer runtimes than the simple bin packing strategy and increases the runtime utilization up to 40% for the Edge Data Center (DC) in the scenarios we evaluated.

Ort, förlag, år, upplaga, sidor
Umeå: Umeå universitet , 2018. , s. 28
Serie
Report / UMINF, ISSN 0348-0542 ; 18.11
Nyckelord [en]
Edge/Fog Computing, Hierarchical Resource Allocation, IoTs, Mobile Edge Clouds
Nationell ämneskategori
Datorsystem
Forskningsämne
datorteknik
Identifikatorer
URN: urn:nbn:se:umu:diva-151455OAI: oai:DiVA.org:umu-151455DiVA, id: diva2:1244980
Tillgänglig från: 2018-09-04 Skapad: 2018-09-04 Senast uppdaterad: 2018-09-07Bibliografiskt granskad
Ingår i avhandling
1. Resource allocation for Mobile Edge Clouds
Öppna denna publikation i ny flik eller fönster >>Resource allocation for Mobile Edge Clouds
2018 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Umeå: Umeå University, 2018. s. 30
Serie
Report / UMINF, ISSN 0348-0542 ; 18.10
Nyckelord
Mobile Edge Clouds, Edge/Fog Computing, IoTs, Distributed Resource Allocation
Nationell ämneskategori
Datorsystem
Forskningsämne
datalogi; datorteknik
Identifikatorer
urn:nbn:se:umu:diva-151480 (URN)978-91-7601-925-2 (ISBN)
Disputation
2018-10-01, MA121, MIT-huset, Umeå, 13:30 (Engelska)
Opponent
Handledare
Tillgänglig från: 2018-09-10 Skapad: 2018-09-04 Senast uppdaterad: 2018-09-07Bibliografiskt granskad

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