Dynamic Resource Provisioning and Scheduling in SDN/NFV-Enabled Core Networks

The service-oriented fifth-generation (5G) core networks are featured by customized network services with differentiated quality-of-service (QoS) requirements, which can be provisioned through network slicing enabled by the software defined networking (SDN) and network function virtualization (NFV) paradigms. Multiple network services are embedded in a common physical infrastructure, generating service-customized network slices. Each network slice supports a composite service via virtual network function (VNF) chaining, with dedicated packet processing functionality at each VNF. For a network slice with a target traffic load, the end-to-end (E2E) service delivery is enabled by VNF placement at NFV nodes (e.g., data centers and commodity servers) and traffic routing among corresponding NFV nodes, with static resource allocations. To provide continuous QoS performance guarantee over time, it is essential to develop dynamic resource management schemes for the embedded services experiencing traffic dynamics in different time granularities during virtual network operation. In this thesis, we focus on processing resources and investigate three research problems on dynamic processing resource provisioning and scheduling for embedded delay-sensitive services, in presence of both large-timescale traffic statistical changes and bursty traffic dynamics in smaller time granularities. In problem I, we investigate a dynamic flow migration problem for multiple embedded services, to accommodate the large-timescale changes in the average traffic rates with average E2E delay guarantee, while addressing a trade-off between load balancing and flow migration overhead. We develop optimization problem formulations and efficient heuristic algorithms, based on a simplified M/M/1 queueing model with Poisson traffic arrivals. Motivated by the limitations of Poisson traffic model, in problem II, we restrict to a local network scenario and study a dynamic VNF scaling problem based on a real-world traffic trace with nonstationary traffic statistics in large timescale. Under the assumption that the nonstationary traffic trace can be partitioned into non-overlapping stationary traffic segments with unknown change points in time, a change point detection driven traffic parameter learning and resource demand prediction scheme is proposed, based on which dynamic VNF migration decisions are made at variable-length decision epochs via deep reinforcement learning. The long-term trade-off between load balancing and migration overhead is studied. A fractional Brownian motion (fBm) traffic model is employed for each detected stationary traffic segment, based on properties of Gaussianity and self-similarity of the real-world traffic. In Problem III, we focus on a sufficiently long time duration with given VNF placement and stationary traffic statistics, and study a delay-aware VNF scheduling problem to coordinate VNF scheduling for multiple services, which achieves network utility maximization with timely throughput guarantee for each service, in presence of bursty and unpredictable small-timescale traffic dynamics, while using a realistic state-of-the-art time quantum (slot) for CPU processing resource scheduling among VNF software processes. Based on the Lyapunov optimization technique, an online distributed VNF scheduling algorithm is derived, which greedily schedules a VNF at each NFV node based on a weight incorporating the backpressure-based weighted differential backlogs with the downstream VNF, the service throughput performance indicated by virtual queue lengths, and the packet delay. With the proposed dynamic resource management framework, resources can be efficiently and fairly allocated to the embedded services, to avoid congestion and QoS degradation in the presence of traffic dynamics. This research provides some insights in dynamic resource management for delay-sensitive services in a virtualized network environment with CPU processing resources

Performance-oriented Cloud Provisioning: Taxonomy and Survey

Cloud computing is being viewed as the technology of today and the future. Through this paradigm, the customers gain access to shared computing resources located in remote data centers that are hosted by cloud providers (CP). This technology allows for provisioning of various resources such as virtual machines (VM), physical machines, processors, memory, network, storage and software as per the needs of customers. Application providers (AP), who are customers of the CP, deploy applications on the cloud infrastructure and then these applications are used by the end-users. To meet the fluctuating application workload demands, dynamic provisioning is essential and this article provides a detailed literature survey of dynamic provisioning within cloud systems with focus on application performance. The well-known types of provisioning and the associated problems are clearly and pictorially explained and the provisioning terminology is clarified. A very detailed and general cloud provisioning classification is presented, which views provisioning from different perspectives, aiding in understanding the process inside-out. Cloud dynamic provisioning is explained by considering resources, stakeholders, techniques, technologies, algorithms, problems, goals and more.Comment: 14 pages, 3 figures, 3 table

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless Communications and Mobile Computing journa

Energy Efficiency and Quality of Services in Virtualized Cloud Radio Access Network

Cloud Radio Access Network (C-RAN) is being widely studied for soft and green fifth generation of Long Term Evolution - Advanced (LTE-A). The recent technology advancement in network virtualization function (NFV) and software defined radio (SDR) has enabled virtualization of Baseband Units (BBU) and sharing of underlying general purpose processing (GPP) infrastructure. Also, new innovations in optical transport network (OTN) such as Dark Fiber provides low latency and high bandwidth channels that can support C-RAN for more than forty-kilometer radius. All these advancements make C-RAN feasible and practical. Several virtualization strategies and architectures are proposed for C-RAN and it has been established that C-RAN offers higher energy efficiency and better resource utilization than the current decentralized radio access network (D-RAN). This project studies proposed resource utilization strategy and device a method to calculate power utilization. Then proposes and analyzes a new resource management and virtual BBU placement strategy for C-RAN based on demand prediction and inter-BBU communication load. The new approach is compared with existing state of art strategies with same input scenarios and load. The trade-offs between energy efficiency and quality of services is discussed. The project concludes with comparison between different strategies based on complexity of the system, performance in terms of service availability and optimization efficiency in different scenarios