Browsing by Author "Jue, Jason P."
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Item Dynamic Resource Scheduling and Optimization in Elastic Optical Networks(2017-08) Wang, Nannan; Jue, Jason P.Due to rapid growth of network traffic volume, traditional Wavelength Division Multiplexing (WDM) networks cannot meet current growing demands. Elastic optical networks (EONs) are promising optical backbone network candidates to satisfy these new challenges. Based on optical orthogonal frequency division multiplexing (O-OFDM) technology, elastic optical networks support different high line rates beyond 100 Gb/s, while achieving higher spectrum resource efficiency. Furthermore, in elastic optical networks, we can use different modulation formats such as DP-BPSK, DP-QPSK, and DP-16-QAM to set up lightpath connections so as to raise spectrum resource utilization. In this dissertation, in terms of different traffic demands, we study the problem of routing, modulation and spectrum assignment (RMSA) in elastic optical networks. First, we study the routing, modulation and spectrum assignment problem for holding-time-aware network requests. In elastic optical networks, for each line rate request with certain holding time, we need to allocate enough spectrum resource to satisfy the requirement. We divide the requests into two types, those that include immediate reservation (IR) requests and those with advance reservation (AR) requests. In terms of different requests, we design different heuristic algorithms to satisfy their requirement. Second, spectrum fragmentation in elastic optical networks is another important issue that we need to consider. When we set up and tear down lightpath connections in EONs, the spectrum resource will be fragmented, which decreases spectrum resource utilization. In this work, we mainly consider two types of fragmentation including spectrum fragmentation and time fragmentation. We design fragmentation-aware routing, modulation and spectrum assignment algorithms to proactively prevent spectrum fragments. Third, large data-flow transfer is a big challenge for elastic optical networks. Data-flow transfer such as data backup and data migration grow exponentially among datacenters. Hence, we study how to transfer fixed data amount in a certain time domain. A dynamic routing, modulation and spectrum assignment algorithm is designed to meet data-flow transfer in elastic optical networks. Finally, the survivability problem for data-flow transfer plays a vital role in elastic optical inter-datacenter networks. Man-made errors and uncontrollable natural disaster will result in a huge data loss. In order to meet service level agreement (SLA), we need to set up a protection lightpath connection for each data-flow transfer. In this dissertation, we design survivable bulk data-flow transfer strategies for each data-flow transfer.Item Embedding Chains of Virtual Network Functions in Inter-Datacenter Networks(Institute of Electrical and Electronics Engineers Inc.) Kobayashi, H.; Ishigaki, Genya; Gour, Riti; Jue, Jason P.; Shinomiya, N.; Ishigaki, Genya; Gour, Riti; Jue, Jason P.This paper discusses the problem of embedding service function chains (SFCs) in an interconnected network with multiple datacenter sites. The problem is formulated as a Subtopology Composition Problem (SCP), which is to design a subnetwork that includes terminal nodes and datacenter nodes from the substrate network, aimed at optimizing distance-based latency in SFCs. The intractability of the problem is discussed, and a heuristic is proposed for the problem. Simulations are conducted to demonstrate the effectiveness of the proposed method in different graph models. © 2018 IEEE.Item FOGPLAN: A Lightweight QoS-Aware Dynamic Fog Service Provisioning Framework(Institute of Electrical and Electronics Engineers Inc., 2019-01-30) Yousefpour, Ashkan; Patil, Ashish; Ishigaki, Genya; Kim, I.; Wang, X.; Cankaya, H. C.; Zhang, Q.; Xie, W.; Jue, Jason P.; 0000-0003-4869-9183 (Yousefpour, A); 0000-0003-3655-7532 (Ishigaki, G); Yousefpour, Ashkan; Patil, Ashish; Ishigaki, Genya; Jue, Jason P.Recent advances in the areas of Internet of Things (IoT), big data, and machine learning have contributed to the rise of a growing number of complex applications. These applications will be data-intensive, delay-sensitive, and real-time as smart devices prevail more in our daily life. Ensuring quality of service (QoS) for delay-sensitive applications is a must, and fog computing is seen as one of the primary enablers for satisfying such tight QoS requirements, as it puts compute, storage, and networking resources closer to the user. In this paper, we first introduce FOGPLAN, a framework for QoS-aware dynamic fog service provisioning (QDFSP). QDFSP concerns the dynamic deployment of application services on fog nodes, or the release of application services that have previously been deployed on fog nodes, in order to meet low latency and QoS requirements of applications while minimizing cost. FOGPLAN framework is practical and operates with no assumptions and minimal information about IoT nodes. Next, we present a possible formulation (as an optimization problem) and two efficient greedy algorithms for addressing the QDFSP at one instance of time. Finally, the FOGPLAN framework is evaluated using a simulation based on real-world traffic traces. © 2019 IEEE.Item Guaranteed-Availability Network Function Virtualization in Inter-Datacenter Networks(Institute of Electrical and Electronics Engineers Inc.) Kong, Jian; Kim, I.; Wang, X.; Zhang, Q.; Xie, W.; Cankaya, H. C.; Wang, N.; Ikeuchi, T.; Jue, Jason P.; Kong, Jian; Jue, Jason P.Considering the availability of the datacenter's network elements, we propose a coordinated protection mechanism that adopts both backup path protection and SFC replicas distributed among datacenters to support high availability while reducing total cost.Item Improving Quality of Service in Emerging IoT and ML Applications through Fog Computing(2019-12) Yousefpour, Ashkan; 0000-0003-4869-9183 (Yousefpour, A); Jue, Jason P.The recent advances in the Internet of Things (IoT), Big Data, and Machine Learning (ML) have contributed to the rise of a growing number of complex and intelligent applications. Examples of such applications are real-time disease detection, self-driving vehicles, drone package delivery, and smart manufacturing. These emerging applications are often realtime, data-intensive, and delay-sensitive, and ensuring Quality of Service (QoS) for these applications is a challenge. Fog computing is seen as one promising solution for providing QoS for these applications, as it puts compute, storage, and networking resources closer to the user. In this dissertation, we showcase how fog computing can improve the QoS in several emerging IoT and ML applications. To do so, we first provide a tutorial on fog computing and its related computing paradigms, such as edge computing and cloudlets, discussing their similarities and differences. Secondly, we propose two novel schemes, ResiliNet and deepFogGuard, for improving the QoS of deep learning applications that are deployed over network devices, such as fog nodes and edge devices. Specifically, the two schemes are for improving the failure-resiliency of inference in distributed neural network in the presence of physical node failures. deepFogGuard’s design is based on the concept of skip connections, whereas ResiliNet uses a novel technique, introduced in this dissertation, called failout, which is inspired by dropout in neural networks. Next, we propose two fog-based frameworks, FogPlan and FogOffload, that can improve the QoS in terms of the reduced service delay. FogPlan is a planning framework, for the dynamic deployment or release of application services on fog nodes, in order to meet the low latency and the QoS requirements of applications while minimizing the cost. FogOffload is a delay-minimizing collaboration and offloading policy for fog-capable devices that aims to reduce the service delay of IoT applications. The above four frameworks are designed to improve the QoS in emerging IoT and ML applications through Fog Computing. The former two frameworks improve failure-resiliency, whereas the latter two reduce the service delay. The outcomes of this research have appeared in the proceedings of a conference and a workshop, three journal papers, and a paper in preparation. A website has been created to serve as a hub for the details of fog computing conference and journals. This research has also attracted the attention of our industry partner, Fujitsu, who partially funded this research. Additionally, two graduate Master’s students and seven undergraduate students were mentored and actively involved in various research projects, which were defined based on the topics of this dissertation. This dissertation has also been selected to be funded by the Dissertation Research Award at UT Dallas.Item Proactive Dynamic Network Slicing with Deep Learning Based Short-Term Traffic Prediction for 5G Transport Network(IEEE, 2019-03-03) Guo, Qize; Gu, Rentao; Wang, Zihao; Zhao, Tianyi; Ji, Yuefeng; Kong, Jian; Gour, Riti; Jue, Jason P.; Kong, Jian; Gour, Riti; Jue, Jason P.We propose a proactive dynamic network slicing scheme that utilizes a deep-learning based short-term traffic prediction approach for 5G transport networks. The demonstration shows utilization efficiency improvement from 46.33% to 71.53% under the evaluated scenario.