With the advent of Smart Cities, Intelligent Transport System (ITS) has become an efficient way of offering an accessible, safe, and sustainable transportation system. Utilizing advances in Information and Communication Technology (ICT), ITS can maximize the capacity of existing transportation system without building new infrastructure. However, in spite of these technical feasibilities and significant performance-cost ratios, the deployment of ITS is limited in the real world because of several challenges associated with its architectural design.

This thesis studies how to design a highly flexible and deployable architecture for ITS, which can utilize the recent technologies such as - cloud computing and the publish/subscribe communication model. In particular, our aim is to offer an ITS infrastructure which provides the opportunity for transport authorities to allocate on-demand computing resources through virtualization technology, and supports a wide range of ITS applications. We propose to use an Infrastructure as a Service (IaaS) model to host large-scale ITS applications for transport authorities in the cloud, which reduces infrastructure cost, improves management flexibility and also ensures better resource utilization. Moreover, we use a publish/subscribe system as a building block for developing a low latency ITS application, which is a promising technology for designing scalable and distributed applications within the ITS domain. Although cloud-based architectures provide the flexibility of adding, removing or moving ITS services within the underlying physical infrastructure, it may be difficult to provide the required quality of service (QoS) which decrease application productivity and customer satisfaction, leading to revenue losses. Therefore, we investigate the impact of service mobility on related QoS in the cloud-based infrastructure. We investigate different strategies to improve performance of a low latency ITS application during service mobility such as utilizing multiple paths to spread network traffic, or deploying recent queue management schemes.

Evaluation results from a private cloud testbed using OpenStack show that our proposed architecture is suitable for hosting ITS applications which have stringent performance requirements in terms of scalability, QoS and latency.

Baksidestext:

Intelligent Transport System (ITS) can utilize advances in Information and Communication Technology (ICT) and maximize the capacity of existing transportation systems without building new infrastructure. However, in spite of these technical feasibilities and significant performance-cost ratios, the deployment of ITS is limited in the real world because of several challenges associated with its architectural design.  This thesis studies how to design an efficient deployable architecture for ITS, which can utilize the advantages of cloud computing and the publish/subscribe communication model. In particular, our aim is to offer an ITS infrastructure which provides the opportunity for transport authorities to allocate on-demand computing resources through virtualization technology, and supports a wide range of ITS applications. We propose to use an Infrastructure as a Service (IaaS) model to host large-scale ITS applications, and to use a publish/subscribe system as a building block for developing a low latency ITS application. We investigate different strategies to improve performance of an ITS application during service mobility such as utilizing multiple paths to spread network traffic, or deploying recent queue management schemes.

High availability, cost effectiveness and ease of application deployment have accelerated the adoption rate of cloud computing. This fast proliferation of cloud computing promotes the rapid development of large-scale infrastructures. However, large cloud datacenters (DCs) require infrastructure, design, deployment, scalability and reliability and need better management techniques to achieve sustainable design benefits. Resources inside cloud infrastructures often operate at low utilization, rarely exceeding 20-30%, which increases the operational cost significantly, especially due to energy consumption. To reduce operational cost without affecting quality of service (QoS) requirements, cloud applications should be allocated just enough resources to minimize their completion time or to maximize utilization. 

The focus of this thesis is to enable resource-efficient and performance-aware cloud infrastructures by addressing above mentioned cost and performance related challenges. In particular, we propose algorithms, techniques, and deployment strategies for improving the dynamic allocation of virtual machines (VMs) into physical machines (PMs). 

For minimizing the operational cost, we mainly focus on optimizing energy consumption of PMs by applying dynamic VM consolidation methods. To make VM consolidation techniques more efficient, we propose to utilize multiple paths to spread traffic and deploy recent queue management schemes which can maximize network resource utilization and reduce both downtime and migration time for live migration techniques. In addition, a dynamic resource allocation scheme is presented to distribute workloads among geographically dispersed DCs considering their location based time varying costs due to e.g. carbon emission or bandwidth provision. For optimizing performance level objectives, we focus on interference among applications contending in shared resources and propose a novel VM consolidation scheme considering sensitivity of the VMs to their demanded resources. Further, to investigate the impact of uncertain parameters on cloud resource allocation and applications’ QoS such as unpredictable variations in demand, we develop an optimization model based on the theory of robust optimization. Furthermore, in order to handle the scalability issues in the context of large scale infrastructures, a robust and fast Tabu Search algorithm is designed and evaluated.

High availability, cost effectiveness and ease of application deployment have accelerated the adoption rate of cloud computing. This fast proliferation of cloud computing promotes the rapid development of large-scale infrastructures. However, large cloud datacenters (DCs) require infrastructure, design, deployment, scalability and reliability and need better management techniques to achieve sustainable design benefits. Resources inside cloud infrastructures often operate at low utilization, rarely exceeding 20-30%, which increases the operational cost significantly, especially due to energy consumption. To reduce operational cost without affecting quality of service (QoS) requirements, cloud applications should be allocated just enough resources to minimize their completion time or to maximize utilization. 

The focus of this thesis is to enable resource-efficient and performance-aware cloud infrastructures by addressing above mentioned cost and performance related challenges. In particular, we propose algorithms, techniques, and deployment strategies for improving the dynamic allocation of virtual machines (VMs) into physical machines (PMs).