Performance Analysis and Optimisation of In-network Caching for Information-Centric Future Internet

The rapid development in wireless technologies and multimedia services has radically shifted the major function of the current Internet from host-centric communication to service-oriented content dissemination, resulting a mismatch between the protocol design and the current usage patterns. Motivated by this significant change, Information-Centric Networking (ICN), which has been attracting ever-increasing attention from the communication networks research community, has emerged as a new clean-slate networking paradigm for future Internet. Through identifying and routing data by unified names, ICN aims at providing natural support for efficient information retrieval over the Internet. As a crucial characteristic of ICN, in-network caching enables users to efficiently access popular contents from on-path routers equipped with ubiquitous caches, leading to the enhancement of the service quality and reduction of network loads. Performance analysis and optimisation has been and continues to be key research interests of ICN. This thesis focuses on the development of efficient and accurate analytical models for the performance evaluation of ICN caching and the design of optimal caching management schemes under practical network configurations. This research starts with the proposition of a new analytical model for caching performance under the bursty multimedia traffic. The bursty characteristic is captured and the closed formulas for cache hit ratio are derived. To investigate the impact of topology and heterogeneous caching parameters on the performance, a comprehensive analytical model is developed to gain valuable insight into the caching performance with heterogeneous cache sizes, service intensity and content distribution under arbitrary topology. The accuracy of the proposed models is validated by comparing the analytical results with those obtained from extensive simulation experiments. The analytical models are then used as cost-efficient tools to investigate the key network and content parameters on the performance of caching in ICN. Bursty traffic and heterogeneous caching features have significant influence on the performance of ICN. Therefore, in order to obtain optimal performance results, a caching resource allocation scheme, which leverages the proposed model and targets at minimising the total traffic within the network and improving hit probability at the nodes, is proposed. The performance results reveal that the caching allocation scheme can achieve better caching performance and network resource utilisation than the default homogeneous and random caching allocation strategy. To attain a thorough understanding of the trade-off between the economic aspect and service quality, a cost-aware Quality-of-Service (QoS) optimisation caching mechanism is further designed aiming for cost-efficiency and QoS guarantee in ICN. A cost model is proposed to take into account installation and operation cost of ICN under a realistic ISP network scenario, and a QoS model is presented to formulate the service delay and delay jitter in the presence of heterogeneous service requirements and general probabilistic caching strategy. Numerical results show the effectiveness of the proposed mechanism in achieving better service quality and lower network cost. In this thesis, the proposed analytical models are used to efficiently and accurately evaluate the performance of ICN and investigate the key performance metrics. Leveraging the insights discovered by the analytical models, the proposed caching management schemes are able to optimise and enhance the performance of ICN. To widen the outcomes achieved in the thesis, several interesting yet challenging research directions are pointed out

DESIGN, ANALYSIS AND OPTIMIZATION OF CACHE SYSTEMS

The increase in data traffic over the past years is predicted to continue more aggressively in the years to come. However, traditional methods such as increasing the amount of spectrum or deploying more base stations are no longer sufficient to cope with the traffic growth. Caching is hence recognized as one of the most effective means to improve the performance of Internet services by bringing content closer to the end-users. Although the benefits of in-network content caching has been demonstrated in various contexts, they introduce new challenges in terms of modeling and analyzing network performance. Building on analytical results for Time-To-Live caches and the flexibility they provide in modeling caches, this thesis investigates various aspects in which caching affects network design and performance. The complexity of making optimal routing and content placement decisions is studied first. Showing the infeasibility of implementing the optimal strategy, low-complexity techniques are developed to achieve near-optimal performance in terms of the delay observed by end-users. The problem of differentiated cache services is studied next with the question ``how can Content Distribution Networks implement caching policies to provide differentiated services to different content providers?\u27\u27. A utility-maximization framework is formulated to design caching policies with fairness considerations and implications on market economy for cache service providers and content publishers. An online algorithm is also developed with the purpose of implementing the utility-based cache policies with no a priori information on the number of contents and file popularities. This thesis also analyzes caches in conjunction with data structures, e.g. Pending Interest Table, proposed in the future Internet architecture designs such as Named Data Networking. The analysis provides the means to understand system performance under different circumstances, and develop techniques to achieve optimal performance

QOE-AWARE CONTENT DISTRIBUTION SYSTEMS FOR ADAPTIVE BITRATE VIDEO STREAMING

A prodigious increase in video streaming content along with a simultaneous rise in end system capabilities has led to the proliferation of adaptive bit rate video streaming users in the Internet. Today, video streaming services range from Video-on-Demand services like traditional IP TV to more recent technologies such as immersive 3D experiences for live sports events. In order to meet the demands of these services, the multimedia and networking research community continues to strive toward efficiently delivering high quality content across the Internet while also trying to minimize content storage and delivery costs. The introduction of flexible and adaptable technologies such as compute and storage clouds, Network Function Virtualization and Software Defined Networking continue to fuel content provider revenue. Today, content providers such as Google and Facebook build their own Software-Defined WANs to efficiently serve millions of users worldwide, while NetFlix partners with ISPs such as ATT (using OpenConnect) and cloud providers such as Amazon EC2 to serve their content and manage the delivery of several petabytes of high-quality video content for millions of subscribers at a global scale, respectively. In recent years, the unprecedented growth of video traffic in the Internet has seen several innovative systems such as Software Defined Networks and Information Centric Networks as well as inventive protocols such as QUIC, in an effort to keep up with the effects of this remarkable growth. While most existing systems continue to sub-optimally satisfy user requirements, future video streaming systems will require optimal management of storage and bandwidth resources that are several orders of magnitude larger than what is implemented today. Moreover, Quality-of-Experience metrics are becoming increasingly fine-grained in order to accurately quantify diverse content and consumer needs. In this dissertation, we design and investigate innovative adaptive bit rate video streaming systems and analyze the implications of recent technologies on traditional streaming approaches using real-world experimentation methods. We provide useful insights for current and future content distribution network administrators to tackle Quality-of-Experience dilemmas and serve high quality video content to several users at a global scale. In order to show how Quality-of-Experience can benefit from core network architectural modifications, we design and evaluate prototypes for video streaming in Information Centric Networks and Software-Defined Networks. We also present a real-world, in-depth analysis of adaptive bitrate video streaming over protocols such as QUIC and MPQUIC to show how end-to-end protocol innovation can contribute to substantial Quality-of-Experience benefits for adaptive bit rate video streaming systems. We investigate a cross-layer approach based on QUIC and observe that application layer-based information can be successfully used to determine transport layer parameters for ABR streaming applications

Queuing Modelling and Performance Analysis of Content Transfer in Information Centric Networks

With the rapid development of multimedia services and wireless technology, new generation of network traffic like short-form video and live streaming have put tremendous pressure on the current network infrastructure. To meet the high bandwidth and low latency needs of this new generation of traffic, the focus of Internet architecture has moved from host-centric end-to-end communication to requester-driven content retrieval. This shift has motivated the development of Information-Centric Networking (ICN), a promising new paradigm for the future Internet. ICN aims to improve information retrieval on the Internet by identifying and routing data using unified names. In-network caching and the use of a pending interest table (PIT) are two key features of ICN that are designed to efficiently handle bulk data dissemination and retrieval, as well as reduce bandwidth consumption. Performance analysis has been and continues to be key research interests of ICN. This thesis starts with the evaluation of content delivery delays in ICN. The main component of delay is composed of propagation delay, transmission delay,processing delay and queueing delay. To characterize the main components of content delivery delay, queueing network theory has been exploited to coordinate with cache miss rate in modelling the content delivery time in ICN. Moreover, different topologies and network conditions have been taken into account to evaluate the performance of content transfer in ICN. ICN is intrinsically compatible with wireless networks. To evaluate the performance of content transfer in wireless networks, an analytical model to evaluate the mean service time based on consumer and provider mobility has been proposed. The accuracy of the analytical model is validated through extensive simulation experiments. Finally, the analytical model is used to evaluate the impact of key metrics, such as the cache size, content size and content popularity on the performance of PIT and content transfer in ICN. Pending interest table (PIT) is one of the essential components of the ICN forwarding plane, which is responsible for stateful routing in ICN. It also aggregates the same interests to alleviate request flooding and network congestion. The aggregation feature of PIT improves performance of content delivery in ICN. Thus, having an analytical model to characterize the impact of PIT on content delivery time could allow for a more precise evaluation of content transfer performance. In parallel, if the size of the PIT is not properly determined, the interest drop rate may be too high, resulting in a reduction in quality of service for consumers as their requests have to be retransmitted. Furthermore, PIT is a costly resource as it requires to operate at wirespeed in the forwarding plane. Therefore, in order to ensure that interests drop rate less than the requirement, an analytical model of PIT occupancy has been developed to determine the minimum PIT size. In this thesis, the proposed analytical models are used to efficiently and accurately evaluate the performance of ICN content transfer and investigate the key component of ICN forwarding plane. Leveraging the insights discovered by these analytical models, the minimal PIT size and proper interest timeout can be determined to enhance the performance of ICN. To widen the outcomes achieved in the thesis, several interesting yet challenging research directions are pointed out

On the design of efficient caching systems

Content distribution is currently the prevalent Internet use case, accounting for the majority of global Internet traffic and growing exponentially. There is general consensus that the most effective method to deal with the large amount of content demand is through the deployment of massively distributed caching infrastructures as the means to localise content delivery traffic. Solutions based on caching have been already widely deployed through Content Delivery Networks. Ubiquitous caching is also a fundamental aspect of the emerging Information-Centric Networking paradigm which aims to rethink the current Internet architecture for long term evolution. Distributed content caching systems are expected to grow substantially in the future, in terms of both footprint and traffic carried and, as such, will become substantially more complex and costly. This thesis addresses the problem of designing scalable and cost-effective distributed caching systems that will be able to efficiently support the expected massive growth of content traffic and makes three distinct contributions. First, it produces an extensive theoretical characterisation of sharding, which is a widely used technique to allocate data items to resources of a distributed system according to a hash function. Based on the findings unveiled by this analysis, two systems are designed contributing to the abovementioned objective. The first is a framework and related algorithms for enabling efficient load-balanced content caching. This solution provides qualitative advantages over previously proposed solutions, such as ease of modelling and availability of knobs to fine-tune performance, as well as quantitative advantages, such as 2x increase in cache hit ratio and 19-33% reduction in load imbalance while maintaining comparable latency to other approaches. The second is the design and implementation of a caching node enabling 20 Gbps speeds based on inexpensive commodity hardware. We believe these contributions advance significantly the state of the art in distributed caching systems

A User-driven Annotation Framework for Scientific Data

Annotations play an increasingly crucial role in scientific exploration and discovery, as the amount of data and the level of collaboration among scientists increases. There are many systems today focusing on annotation management, querying, and propagation. Although all such systems are implemented to take user input (i.e., the annotations themselves), very few systems are user-driven, taking into account user preferences on how annotations should be propagated and applied over data. In this thesis, we propose to treat annotations as first-class citizens for scientific data by introducing a user-driven, view-based annotation framework. Under this framework, we try to resolve two critical questions: Firstly, how do we support annotations that are scalable both from a system point of view and also from a user point of view? Secondly, how do we support annotation queries both from an annotator point of view and a user point of view, in an efficient and accurate way? To address these challenges, we propose the VIew-base annotation Propagation (ViP) framework to empower users to express their preferences over the time semantics of annotations and over the network semantics of annotations, and define three query types for annotations. To efficiently support such novel functionality, ViP utilizes database views and introduces new annotation caching techniques. The use of views also brings a more compact representation of annotations, making our system easier to scale. Through an extensive experimental study on a real system (with both synthetic and real data), we show that the ViP framework can seamlessly introduce user-driven annotation propagation semantics while at the same time significantly improving the performance (in terms of query execution time) over the current state of the art

Context prediction-based prefetching in software-defined wireless networks

In this master thesis we focus on improving in-network caching for mobile users in a large campus WiFi network. First we pinpoint the negative effects of mobility on network conditions and user experience. We propose a method leveraging SDN technology to redirect users' requests to optimally located cache servers, resulting in improved user experience and lowered burden on the backhaul and core network links. Our contribution is a network application that controls the flows in the network via an SDN controller. The application takes user's movement traces as an input, computes the optimal location of cache servers in the network and redirects user's flows accordingly. We tested our solution in a Mininet network simulator. We devised multiple scenarios using real-world movement traces from Dartmouth Campus. We measured requests delay as the main characteristic for user experience and data traffic over core and backhaul links as an indicator of network health. Our experiments show that for mobile users our dynamic redirection approach provides noticeable improvements over traditional, static caching methods