Architecture and algorithm for reliable 5G network design

This Ph.D. thesis investigates the resilient and cost-efficient design of both C-RAN and Xhaul architectures. Minimization of network resources as well as reuse of already deployed infrastructure, either based on fiber, wavelength, bandwidth or Processing Units (PU), is investigated and shown to be effective to reduce the overall cost. Moreover, the design of a survivable network against a single node (Baseband Unit hotel (BBU), Centralized/Distributed Unit (CU/DU) or link failure proposed. The novel function location algorithm, which adopts dynamic function chaining in relation to the evolution of the traffic estimation also proposed and showed remarkable improvement in terms of bandwidth saving and multiplexing gain with respect to conventional C-RAN. Finally, the adoption of Ethernet-based fronthaul and the introduction of hybrid switches is pursued to further decrease network cost by increasing optical resource usage

System Support For Stream Processing In Collaborative Cloud-Edge Environment

Stream processing is a critical technique to process huge amount of data in real-time manner. Cloud computing has been used for stream processing due to its unlimited computation resources. At the same time, we are entering the era of Internet of Everything (IoE). The emerging edge computing benefits low-latency applications by leveraging computation resources at the proximity of data sources. Billions of sensors and actuators are being deployed worldwide and huge amount of data generated by things are immersed in our daily life. It has become essential for organizations to be able to stream and analyze data, and provide low-latency analytics on streaming data. However, cloud computing is inefficient to process all data in a centralized environment in terms of the network bandwidth cost and response latency. Although edge computing offloads computation from the cloud to the edge of the Internet, there is not a data sharing and processing framework that efficiently utilizes computation resources in the cloud and the edge. Furthermore, the heterogeneity of edge devices brings more difficulty to the development of collaborative cloud-edge applications. To explore and attack the challenges of stream processing system in collaborative cloudedge environment, in this dissertation we design and develop a series of systems to support stream processing applications in hybrid cloud-edge analytics. Specifically, we develop an hierarchical and hybrid outlier detection model for multivariate time series streams that automatically selects the best model for different time series. We optimize one of the stream processing system (i.e., Spark Streaming) to reduce the end-to-end latency. To facilitate the development of collaborative cloud-edge applications, we propose and implement a new computing framework, Firework that allows stakeholders to share and process data by leveraging both the cloud and the edge. A vision-based cloud-edge application is implemented to demonstrate the capabilities of Firework. By combining all these studies, we provide comprehensive system support for stream processing in collaborative cloud-edge environment

Survivable Cloud Networking Services

Cloud computing paradigms are seeing very strong traction today and are being propelled by advances in multi-core processor, storage, and high-bandwidth networking technologies. Now as this growth unfolds, there is a growing need to distribute cloud services over multiple data-center sites in order to improve speed, responsiveness, as well as reliability. Overall, this trend is pushing the need for virtual network (VN) embedding support at the underlying network layer. Moreover, as more and more mission-critical end-user applications move to the cloud, associated VN survivability concerns are also becoming a key requirement in order to guarantee user service level agreements. Overall, several different types of survivable VN embedding schemes have been developed in recent years. Broadly, these schemes offer resiliency guarantees by pre-provisioning backup resources at service setup time. However, most of these solutions are only geared towards handling isolated single link or single node failures. As such, these designs are largely ineffective against larger regional stressors that can result in multiple system failures. In particular, many cloud service providers are very concerned about catastrophic disaster events such as earthquakes, floods, hurricanes, cascading power outages, and even malicious weapons of mass destruction attacks. Hence there is a pressing need to develop more robust cloud recovery schemes for disaster recovery that leverage underlying distributed networking capabilities. In light of the above, this dissertation proposes a range of solutions to address cloud networking services recovery under multi-failure stressors. First, a novel failure region-disjoint VN protection scheme is proposed to achieve improved efficiency for pre-provisioned protection. Next, enhanced VN mapping schemes are studied with probabilistic considerations to minimize risk for VN requests under stochastic failure scenarios. Finally, novel post-fault VN restoration schemes are also developed to provide viable last-gap recovery mechanisms using partial and full VN remapping strategies. The performance of these various solutions is evaluated using discrete event simulation and is also compared to existing strategies