Matching Items (3)
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- All Subjects: Optimization
- Genre: Doctoral Dissertation
- Creators: Huang, Dijiang
- Member of: Theses and Dissertations
Description
In this dissertation, two interrelated problems of service-based systems (SBS) are addressed: protecting users' data confidentiality from service providers, and managing performance of multiple workflows in SBS. Current SBSs pose serious limitations to protecting users' data confidentiality. Since users' sensitive data is sent in unencrypted forms to remote machines owned and operated by third-party service providers, there are risks of unauthorized use of the users' sensitive data by service providers. Although there are many techniques for protecting users' data from outside attackers, currently there is no effective way to protect users' sensitive data from service providers. In this dissertation, an approach is presented to protecting the confidentiality of users' data from service providers, and ensuring that service providers cannot collect users' confidential data while the data is processed or stored in cloud computing systems. The approach has four major features: (1) separation of software service providers and infrastructure service providers, (2) hiding the information of the owners of data, (3) data obfuscation, and (4) software module decomposition and distributed execution. Since the approach to protecting users' data confidentiality includes software module decomposition and distributed execution, it is very important to effectively allocate the resource of servers in SBS to each of the software module to manage the overall performance of workflows in SBS. An approach is presented to resource allocation for SBS to adaptively allocating the system resources of servers to their software modules in runtime in order to satisfy the performance requirements of multiple workflows in SBS. Experimental results show that the dynamic resource allocation approach can substantially increase the throughput of a SBS and the optimal resource allocation can be found in polynomial time
ContributorsAn, Ho Geun (Author) / Yau, Sik-Sang (Thesis advisor) / Huang, Dijiang (Committee member) / Ahn, Gail-Joon (Committee member) / Santanam, Raghu (Committee member) / Arizona State University (Publisher)
Created2012
Description
Lighting systems and air-conditioning systems are two of the largest energy consuming end-uses in buildings. Lighting control in smart buildings and homes can be automated by having computer controlled lights and window blinds along with illumination sensors that are distributed in the building, while temperature control can be automated by having computer controlled air-conditioning systems. However, programming actuators in a large-scale environment for buildings and homes can be time consuming and expensive. This dissertation presents an approach that algorithmically sets up the control system that can automate any building without requiring custom programming. This is achieved by imbibing the system self calibrating and self learning abilities.
For lighting control, the dissertation describes how the problem is non-deterministic polynomial-time hard(NP-Hard) but can be resolved by heuristics. The resulting system controls blinds to ensure uniform lighting and also adds artificial illumination to ensure light coverage remains adequate at all times of the day, while adjusting for weather and seasons. In the absence of daylight, the system resorts to artificial lighting.
For temperature control, the dissertation describes how the temperature control problem is modeled using convex quadratic programming. The impact of every air conditioner on each sensor at a particular time is learnt using a linear regression model. The resulting system controls air-conditioning equipments to ensure the maintenance of user comfort and low cost of energy consumptions. The system can be deployed in large scale environments. It can accept multiple target setpoints at a time, which improves the flexibility and efficiency of cooling systems requiring temperature control.
The methods proposed work as generic control algorithms and are not preprogrammed for a particular place or building. The feasibility, adaptivity and scalability features of the system have been validated through various actual and simulated experiments.
For lighting control, the dissertation describes how the problem is non-deterministic polynomial-time hard(NP-Hard) but can be resolved by heuristics. The resulting system controls blinds to ensure uniform lighting and also adds artificial illumination to ensure light coverage remains adequate at all times of the day, while adjusting for weather and seasons. In the absence of daylight, the system resorts to artificial lighting.
For temperature control, the dissertation describes how the temperature control problem is modeled using convex quadratic programming. The impact of every air conditioner on each sensor at a particular time is learnt using a linear regression model. The resulting system controls air-conditioning equipments to ensure the maintenance of user comfort and low cost of energy consumptions. The system can be deployed in large scale environments. It can accept multiple target setpoints at a time, which improves the flexibility and efficiency of cooling systems requiring temperature control.
The methods proposed work as generic control algorithms and are not preprogrammed for a particular place or building. The feasibility, adaptivity and scalability features of the system have been validated through various actual and simulated experiments.
ContributorsWang, Yuan (Author) / Dasgupta, Partha (Thesis advisor) / Davulcu, Hasan (Committee member) / Huang, Dijiang (Committee member) / Reddy, T. Agami (Committee member) / Arizona State University (Publisher)
Created2015
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