End-User Mapping

While a number of Internet applications determine the optimal service tailored for a client based on its identifier, the client's identifier, if explicitly leaked to the Internet applications, raises critical privacy concerns. To address the privacy problem for those sender-sensitive systems, this paper proposes a privacy-preserving service of pseudonymizing Internet client. Replacing real identifier with pseudonymizing identifier in talking to Internet applications preserves the client's privacy. Delegating sender-sensitive optimization tasks to a trustworthy third party ensures the performance of Internet applications. The usage of pseudonymizing-client service in CDN is presented to improve CDN performance as well as privacy on client's IP address.

The presence of bottlenecks in the Internet reduces the performance of content delivery in typical client-server applications such as Web browsing and media streaming. Caching and server farm architectures have been a good solution to this problem since the World Wide Web became very popular, but now they are not enough to support new e-business solutions. Content delivery networks (CDNs) distribute content by placing it on several surrogate servers across the Internet and try to overcome these bottlenecks. In CDN scenarios a good request routing algorithm selects the best surrogate for a client's request; it makes its decision using metrics received by the network and the surrogate. Typical metrics are network latency, hop count, available Internet bandwidth, and surrogate load. We analyze the impact of different request routing algorithms on the delivery performance of CDNs. A new request routing algorithm, called WSE (worst surrogate exclusion), is proposed. Worst surrogate exclusion excludes those surrogates that provide the client with low performance and prevents surrogates overloading. Simulation results show that WSE can reduce users average response time and efficaciously exploit the CDN's capacity.

Content streaming is the dominant application in today's Internet, which is typically distributed via content delivery networks (CDNs). CDNs usually use caching as a means to reduce user access latency so as to enable faster content downloads. Typical analysis of caching systems either focuses on content admission, which decides whether to cache a content, or content eviction to decide which content to evict when the cache is full. This paper instead proposes a novel framework that can simultaneously learn both content admission and content eviction for caching in CDNs. To attain this goal, we first put forward a lightweight architecture for content next request time prediction. We then leverage reinforcement learning (RL) along with the prediction to learn the time-varying content popularities for content admission, and develop a simple threshold-based model for content eviction. We call this new algorithm RL-Belady (RLB). In addition, we address several key challenges to design learning-based caching algorithms, including how to guarantee lightweight training and prediction with both content eviction and admission in consideration, limit memory overhead, reduce randomness and improve robustness in RL stochastic optimization. Our evaluation results using $3$ production CDN datasets show that RLB can consistently outperform state-of-the-art methods with dramatically reduced running time and modest overhead.

Chapter 14 - Proxy Functions of Microsoft Internet Security and Acceleration Server 2006

Energy-efficient content delivery networks using cluster shutdown

Content distribution networks (CDNs) are increasingly being used to disseminate data in today's Internet. The growing interest in CDNs is motivated by a common problem across disciplines: how does one reduce the load on the origin server and the traffic on the Internet, and ultimately improve response time to users? In this direction, crucial data management issues should be addressed. A very important issue is the optimal placement of the outsourced content to CDN's servers. Taking into account that this problem is NP complete, a heuristic method should be developed. All the approaches developed so far assume the existence of adequate popularity statistics. Such information though, is not always available, or it is extremely volatile, turning such methods problematic. This paper develops a network-adaptive, non-parameterized technique to place the outsourced content to CDN's servers, which requires no a-priori knowledge of request statistics. We place the outsourced objects to these servers with respect to the network latency that each object produces. Through a detailed simulation environment, using both real and synthetic data, we show that the proposed technique can yield up to 25% reduction in user-perceived latency, compared with other heuristic schemes which have knowledge of the content popularity.

The increasing prevalence of Distributed Denial of Service (DDoS) attacks on the Internet has led to the wide adoption of DDoS Protection Service (DPS), which is typically provided by Content Delivery Networks (CDNs) and is integrated with CDN's security extensions. The effectiveness of DPS mainly relies on hiding the IP address of an origin server and rerouting the traffic to the DPS provider's distributed infrastructure, where malicious traffic can be blocked. In this paper, we perform a measurement study on the usage dynamics of DPS customers and reveal a new vulnerability in DPS platforms, called residual resolution, by which a DPS provider may leak origin IP addresses when its customers terminate the service or switch to other platforms, resulting in the failure of protection from future DPS providers as adversaries are able to discover the origin IP addresses and launch the DDoS attack directly to the origin servers. We identify that two major DPS/CDN providers, Cloudflare and Incapsula, are vulnerable to such residual resolution exposure, and we then assess the magnitude of the problem in the wild. Finally, we discuss the root causes of residual resolution and the practical countermeasures to address this security vulnerability.

With the increasing popularity of the Internet video streaming services (e.g. YouTube and Netflix), content delivery networks (CDNs) are heavily used to stream video contents to users, and consume more and more power and bandwidths in recent years. In this paper, we investigate the problem of saving a video streaming CDN’s operating expense, including both its energy cost and the traffic cost. From our measurement study on the CDN infrastructure of Youku, which is the largest video site in China, we find that there exists an inherent conflict between improving a video streaming CDN’s energy efficiency for power saving, and maintaining the CDN’s ISP-friendly server selection policy. To address this problem, we propose a cost-aware capacity provisioning algorithm, which dynamically plans the service capacities of a CDN’s server clusters in numerous ISPs, and optimizes its overall operating cost regarding both the energy consumptions and the cross-ISP traffics. By using the workload derived from real-world measurement and applying actual power and bandwidth price parameters, we show with experiments that our approach can significantly reduce a video streaming CDN’s overall operating cost, and avoid frequent server switches effectively. To our best knowledge, this work is the first one that identifies and resolves the inherent conflict between a CDN’s energy efficiency and its ISP-friendly policy.

Planet-scale video Content Delivery Networks (CDNs) deliver a significant fraction of the entire Internet traffic. Effective caching at the edge is vital for the feasibility of these CDNs, which can otherwise incur significant monetary costs and resource overloads in the Internet.We analyze the challenges and requirements for video caching on these CDNs which cannot be addressed by standard solutions. We develop multiple algorithms for caching in these CDNs: (i) An LRU-based baseline solution to address the requirements, (ii) an intelligent ingress-efficient algorithm, (iii) an offline cache aware of future requests (greedy) to estimate the maximum caching efficiency we can expect from any online algorithm, and (iv) an optimal offline cache (for limited scales). We use anonymized actual data from a large-scale, global CDN to evaluate the algorithms and draw conclusions on their suitability for different settings.

A common approach when setting up a website is to utilize third party Web hosting and content delivery networks. Without taking this trend into account, any measurement study inspecting the deployment and operation of websites can be heavily skewed. Unfortunately, the research community lacks generalizable tools that can be used to identify how and where a given website is hosted. Instead, a number of ad hoc techniques have emerged, e.g., using Autonomous System databases, domain prefixes for CNAME records. In this work we propose Pythia, a novel lightweight approach for identifying Web content hosted on third-party infrastructures, including both traditional Web hosts and content delivery networks. Our framework identifies the organization to which a given Web page belongs, and it detects which Web servers are self-hosted and which ones leverage third-party services to provide contents. To test our framework we run it on 40,000 URLs and evaluate its accuracy, both by comparing the results with similar services and with a manually validated groundtruth. Our tool achieves an accuracy of 90% and detects that under 11% of popular domains are self-hosted. We publicly release our tool to allow other researchers to reproduce our findings, and to apply it to their own studies.

Cloud computing becomes a popular trend in recent years. In the meanwhile, content delivery network has been used for many years to distribute content all over the world. The relation between cloud computing and content delivery network is very interesting. In this paper, we proposed a cloud based content delivery network. This architecture integrates cloud infrastructure and content delivery network system together. It provides a low cost and elastic web serving cloud platform. Besides, it also provides easy way to manage data for web content developers, and accelerate the speed for users to access web sites. This system uses Hadoop Distributed File System as a cloud infrastructure service, and creates many data clusters around the world. Therefore, content providers could place content to closest node from end users. The result shows a high performance and flexible solution to build a personal content delivery network over cloud.

This paper introduces the concept of the Reverse Content Distribution Network (rCDN) infrastructure synergized with Fog Computing. In many IoT use cases, video content and other streaming data flow from downstream devices (cameras) to upstream devices (e.g., IoT Gateway, Road Side Unit, network edge node) that can stream the content directly to the Cloud or can aggregate, store and process data in a distributed fashion. This builds a Content Distribution Network (CDN) that changes the traditional CDN model. This gives rise to rCDN, a collection of related video streams that flow from multiple content sources in a many-to-1 fashion, and where highly dynamic video data is likely aggregated, processed, analyzed, transformed, cached and migrated potentially multiple times en route to its final storage destination. In this paper, we present a solution that synergize rCDN and Fog Computing to serve connected and autonomous vehicles, where video content from vehicles cameras and street cameras are distributed across rCDN nodes. We also discuss the role of rCDN for data re-usability within the Smart City, and highlight existing challenges.

This paper proposes an architecture of content delivery network (CDN) based on big data for power saving. There are two types of video content: hot content and cold content. When video content is accessed frequently, it is called hot content. Conversely, when video content is accessed infrequently, it is called cold content. In CDN, there is an origin server and a CDN cache server. A CDN cache server has a replicated content and provides its content to the end users nearby. Therefore, the user can receive the requested content from the closest proximity for fast content. The proposed architecture in this paper powers off the cold content server in CDN cache server when the number of cold content requests decreases. Hence, the proposed architecture for content delivery services based on power saving is expected to be useful for providing multimedia streaming services with low power consumption for content providers.

While the DNS protocol encompasses both UDP and TCP as its underlying transport, UDP is commonly used in practice. At the same time, increasingly large DNS responses and concerns over amplification denial of service attacks have heightened interest in conducting DNS interactions over TCP. This paper surveys the support for DNS-over-TCP in the deployed DNS infrastructure from several angles. First, we assess resolvers responsible for over 66.2% of the external DNS queries that arrive at a major content delivery network (CDN). We find that 2.7% to 4.8% of the resolvers, contributing around 1.1% to 4.4% of all queries arriving at the CDN from the resolvers we study, do not properly fallback to TCP when instructed by authoritative DNS servers. Should a content provider decide to employ TCP-fallback as the means of switching to DNS-over-TCP, it faces the corresponding loss of its customers. Second, we assess authoritative DNS servers (ADNS) for over 10M domains and many CDNs and find some ADNS, serving some popular websites and a number of CDNs, that do not support DNS-over-TCP. These ADNS would deny service to (RFC-compliant) resolvers that choose to switch to TCP-only interactions. Third, we study the TCP connection reuse behavior of DNS actors and describe a race condition in TCP connection reuse by DNS actors that may become a significant issue should DNS-over-TCP and other TCP-based DNS protocols, such as DNS-over-TLS, become widely used.

Content delivery over Internet is optimized for low use perceived latency by designing specialized Content Distribution Network (CDN). Content servers are connected through a content switch the distributes client requests among them to achieve load balancing across the servers. The content switch is located one-hop away from the servers. Server load balancing is one factor in achieveing low user perceived latency and improving operational efficiency. Traffic engineering is another factor that is generally and integrated with serverload balancing for CDN optimization. In this thesis we propose a request routing algorithm for a CDN that was designed to integrate server selection and traffic engineering functions in the request routing system. The CDN employs MPLS in the network for traffic engineering. The proposed algorithm optimizes content delivery for user perceived latency by achieving server load balancing and network traffic loan manaement among alternative paths. It also improves operational efficiency of the CDN by eliminating bottleneck paths and increasing utilization of underutilized servers and paths.