ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- Creators: Huang, Dijiang
Description
Multi-tenancy architecture (MTA) is often used in Software-as-a-Service (SaaS) and
the central idea is that multiple tenant applications can be developed using compo
nents stored in the SaaS infrastructure. Recently, MTA has been extended where
a tenant application can have its own sub-tenants as the tenant application acts
like a SaaS infrastructure. In other words, MTA is extended to STA (Sub-Tenancy
Architecture ). In STA, each tenant application not only need to develop its own
functionalities, but also need to prepare an infrastructure to allow its sub-tenants to
develop customized applications. This dissertation formulates eight models for STA,
and proposes a Variant Point based customization model to help tenants and sub
tenants customize tenant and sub-tenant applications. In addition, this dissertation
introduces Crowd- sourcing to become the core of STA component development life
cycle. To discover fit tenant developers or components to help building and com
posing new components, dynamic and static ranking models are proposed. Further,
rank computation architecture is presented to deal with the case when the number of
tenants and components becomes huge. At last, an experiment is performed to prove
rank models and the rank computation architecture work as design.
the central idea is that multiple tenant applications can be developed using compo
nents stored in the SaaS infrastructure. Recently, MTA has been extended where
a tenant application can have its own sub-tenants as the tenant application acts
like a SaaS infrastructure. In other words, MTA is extended to STA (Sub-Tenancy
Architecture ). In STA, each tenant application not only need to develop its own
functionalities, but also need to prepare an infrastructure to allow its sub-tenants to
develop customized applications. This dissertation formulates eight models for STA,
and proposes a Variant Point based customization model to help tenants and sub
tenants customize tenant and sub-tenant applications. In addition, this dissertation
introduces Crowd- sourcing to become the core of STA component development life
cycle. To discover fit tenant developers or components to help building and com
posing new components, dynamic and static ranking models are proposed. Further,
rank computation architecture is presented to deal with the case when the number of
tenants and components becomes huge. At last, an experiment is performed to prove
rank models and the rank computation architecture work as design.
ContributorsZhong, Peide (Author) / Davulcu, Hasan (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Huang, Dijiang (Committee member) / Tsai, Wei-Tek (Committee member) / Arizona State University (Publisher)
Created2017
Description
Internet of Things (IoT) is emerging as part of the infrastructures for advancing a large variety of applications involving connections of many intelligent devices, leading to smart communities. Due to the severe limitation of the computing resources of IoT devices, it is common to offload tasks of various applications requiring substantial computing resources to computing systems with sufficient computing resources, such as servers, cloud systems, and/or data centers for processing. However, this offloading method suffers from both high latency and network congestion in the IoT infrastructures.
Recently edge computing has emerged to reduce the negative impacts of tasks offloading to remote computing systems. As edge computing is in close proximity to IoT devices, it can reduce the latency of task offloading and reduce network congestion. Yet, edge computing has its drawbacks, such as the limited computing resources of some edge computing devices and the unbalanced loads among these devices. In order to effectively explore the potential of edge computing to support IoT applications, it is necessary to have efficient task management and load balancing in edge computing networks.
In this dissertation research, an approach is presented to periodically distributing tasks within the edge computing network while satisfying the quality-of-service (QoS) requirements of tasks. The QoS requirements include task completion deadline and security requirement. The approach aims to maximize the number of tasks that can be accommodated in the edge computing network, with consideration of tasks’ priorities. The goal is achieved through the joint optimization of the computing resource allocation and network bandwidth provisioning. Evaluation results show the improvement of the approach in increasing the number of tasks that can be accommodated in the edge computing network and the efficiency in resource utilization.
Recently edge computing has emerged to reduce the negative impacts of tasks offloading to remote computing systems. As edge computing is in close proximity to IoT devices, it can reduce the latency of task offloading and reduce network congestion. Yet, edge computing has its drawbacks, such as the limited computing resources of some edge computing devices and the unbalanced loads among these devices. In order to effectively explore the potential of edge computing to support IoT applications, it is necessary to have efficient task management and load balancing in edge computing networks.
In this dissertation research, an approach is presented to periodically distributing tasks within the edge computing network while satisfying the quality-of-service (QoS) requirements of tasks. The QoS requirements include task completion deadline and security requirement. The approach aims to maximize the number of tasks that can be accommodated in the edge computing network, with consideration of tasks’ priorities. The goal is achieved through the joint optimization of the computing resource allocation and network bandwidth provisioning. Evaluation results show the improvement of the approach in increasing the number of tasks that can be accommodated in the edge computing network and the efficiency in resource utilization.
ContributorsSong, Yaozhong (Author) / Yau, Sik-Sang (Thesis advisor) / Huang, Dijiang (Committee member) / Sarjoughian, Hessam S. (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2018