Cache Replacement Based on Distance to Origin Servers

The Internet architecture, based on end-to-end connections, had difficulties to efficiently deliver the always increasing number of contents. Content Delivery Networks (CDN) have been deployed to improve the delivery. Recent research works propose a new networking architecture, much more adapted to the current Internet usage (end-users just care about the contents they want and not about the endpoints that provide them). The Content-Centric Networking (CCN) aims at replacing the IP paradigm. CCN has been investigated by the research community for few years, and some demonstrators have proved its feasibility. However, less works have addressed the CDN use-case so far. In this paper, we show that the current CCN design, which does not allow negative reply, is not suitable for interconnection with the CDN service. We then propose the use of two new tables in the CCN nodes that are interconnected to the CDN servers, in order to detect possible misses in the CDN servers and subsequently forward the requests for missing contents toward the original servers instead of the CDN surrogates. The evaluations we have performed highlight that the integration of both new tables does not incur an increase of the memory requirement and thus a cost in the node. The proposed solution is thus a viable solution to make CCN network work in close cooperation with CDN networks, each one keeping its specific business models and functions: transport for CCN, storage for CDN.

Deploying and testing content delivery networks (CDNs) effectively requires a large infrastructure that is difficult to afford and manage. This chapter presents a new and enhanced model for analyzing content delivery networks in a more realistic way based on a previous analytic model. CDNs typically consist of large networks and many servers; thus, the number of network parameters to model is significant. The chapter describes a basic scenario in order to test and evaluate the performance of a CDN. The new model provides a hierarchical configuration, which increases flexibility. The chapter also describes different scenarios involving communication among clients, surrogates, clusters, and origin server. Other scenarios are also interesting to investigate related to the applications that will be exploited in the CDN. Web and streaming applications could be analyzed separately, but with a similar object model for both.

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.

Over the last decades, Content Delivery Networks (CDNs) have been developed to overcome the limitation of user perceived latency by replicating contents from origin server to its content servers around the globe close to clients. As some contents occupy most of the storage capacity and processing power in traditional private content servers, cloud computing can provide a pool of storage and processing power resources for caching contents. By adopting cloud computing to CDN, the content provider can use the cloud infrastructure by distributing the contents to cloud servers which will then deliver to near clients. In this paper, we propose a cloud-based CDN framework designed by two schemes 1) UDP/TCP-based content distribution from origin server to cloud servers and 2) SDN-based cloud server coordination. In addition, we also formulate the optimal content placement problem using binary integer programming to minimize the total cost of renting resources including storage, processing power, and network bandwidth in cloud providers for hosting contents from origin server. Then, the optimal solution obtained from binary integer programming is evaluated by greedy algorithm and simulations. The proposed framework helps content provider to offer high quality of services to clients while minimizing the cost of rented cloud resources.

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.

To provide secure content delivery, Transport Layer Security (TLS) has become a de facto standard over a couple of decades. However, TLS has a long history of security weaknesses and drawbacks. Thus, the security of TLS has been enhanced by addressing security problems through continuous version upgrades. Meanwhile, to provide fast content delivery globally, websites (or origin web servers) need to deploy and administer many machines in globally distributed environments. They often delegate the management of machines to web hosting services or content delivery networks (CDNs), where the security configurations of distributed servers may vary spatially depending on the managing entities or locations. Based on these spatial differences in TLS security, we find that the security level of TLS connections (and their web services) can be lowered. After collecting the information of (web) domains that exhibit different TLS versions and cryptographic options depending on clients' locations, we show that it is possible to redirect TLS handshake messages to weak TLS servers, which both the origin server and the client may not be aware of. We investigate 7M domains with these spatial differences of security levels in the wild and conduct the analyses to better understand the root causes of this phenomenon. We also measure redirection delays at various locations in the world to see whether there are noticeable delays in redirections.

The efficient delivery of Web content has been identified as a key issue of research for some time. Forward (or reverse) proxies, which are positioned along the request route from the users' browsers to the origin content servers, maintain a cache with copies of content from their origin servers. The strategic placement of proxies across the backbone ISP or the content delivery network can drastically improve the performance of the system (in terms of network bandwidth savings, origin server load, and user-request latency). The ultimate goal of this work is to develop a tool that decides on the position and the number of proxies required in order to achieve given performance improvements (expressed in terms of network bandwidth, origin server load, and user-latency). We believe such a tool will be very helpful to ISPs/CDNs, content providers and end-users, and is thus very much lacking.

Hierarchical caching enables users to obtain content from one of (possibly) many caches between an edge router cache and an origin server, reducing latency/overall network traffic. This can be used in many network architectures, including P2P networks and Content Distribution Networks. Of particular interest are Information Centric Networks (ICNs), which decouple content identifiers from specific network hosts and explicitly consider universal caching (collaboration between network routers) as a desirable feature.Performance analysis of a large-scale hierarchy of caches requires accurate mathematical models for various cache replacement algorithms. There is no previous study that models LRU-k. This cache replacement algorithm is important since its principle is the basis of recent algorithms such as k-LRU that outperform LRU in many situations. We first model LRU-2 using Che's approximation, as a specific case of LRU-k for $k=2$. We also extend our model to a hierarchical network of LRU-2 caches. The model is validated analytically and with simulation. The experiments show that the proposed model approximates LRU-2 accurately. LRU-2 and 2-LRU are also compared analytically and with simulations. The comparison between the two algorithms illustrates that 2-LRU outperforms LRU-2.

A content distribution network (CDN) is a system to improve the delivery of content to the end users on the Internet, in which popular content may be cached or replicated at a number of servers, placed closer to some client populations. The design of a CDN consists of defining which content should be replicated at each server, the number of servers, where they should be placed in the network, which server a client’s requests should be sent to, and how server responses should be routed to the clients. CDNs were originally designed for traditional web files. However, given the increase in streaming media content on the Internet, the development of efficient CDN design methods that take into account the special characteristics of media objects, is of great interest. These characteristics include sustained high bandwidth requirements and faster response time by multicast delivery. The main goal of this paper is to propose an algorithm for designing a streaming media CDN with the efficient byte hit ratio and fast response time. We propose the segment-based dynamic caching mechanism with the pheromone that is defined a kind of lifetime of each segment. We evaluate this algorithm in simulations using CDNsim.KeywordsCDNsegment based cachingpheromone

CDN (Content Delivery Network) has become an important infrastructure of the Internet. However, building an anomaly detection system to monitor and guarantee CDN service quality is non-trivial. Current anomaly detection system usually suffers from undesirable performance in terms of high rate of false positive and false negative, which consequently impacts on its practical deployment. Identifying the root cause of a false detection is critical for diagnosing and improving the performance of anomaly detection. In this paper, we propose a novel feedback mechanism for prediction-based anomaly detection in CDN . Specifically, we introduce a carefully-designed metric named Fittingscore to diagnose whether the prediction model can fit the data well. Further, a threshold adjustment mechanism is proposed to dynamically adjust the thresholds of residual errors. Extensive experiments employing a three-month real CDN dataset collected from a top ISP-operated CDN in China show our proposed method can significantly improve the performance of anomaly detection.

The connotation of the cloud resources have been extended to be multi-scale resources, which includes central resources as presented by data center, edge resources as presented by Content Delivery Network (CDN) and end resources as presented by Peer-to-Peer (P2P). Under the development situation of the scale of the cloud services, it is difficult to provide services (e.g. streaming distribution) with guaranteed QoS only relying on single type of resource (e.g. central resources) to geo-distributed users. Therefore, making multi-resources cooperative to provide reliable services is necessary. However, it is a great challenge to realize Federated Management of Multi-scale Resources (FMMR). In this research, we propose the idea of prediction-based FMMR, and present the problem formulation introducing economic profit from the perspective of CDN operators. Then, we present the method of Time-series Prediction based on Wavelet Analysis (TPWA) to predict the resource requirements of streaming cloud services in CDN. Finally, the predictability of the resource requirement pattern of the streaming cloud service and the effectiveness of our proposed method have been verified, based on the traces collected from a real CDN entity.

In this paper, we address the problem of media content distribution through a Content Distribution Network (CDN). We propose a scheduled CDN solution based on the assumption that request for entertainment content ranges from on-demand to consumption within a few days. We present content distribution algorithms for content requests which can tolerate delays of consumption. For each content request, a distribution schedule is made based on the schedules of the admitted requests and network resource including link and cache capacities. Simulation shows that schedules over network links with cache constraints can significantly enhance the resource utilization thereby reducing the delivery failure. Thus, the network utilization is tailored to the consumption pattern. (Abstract)

A Content Delivery Network (CDN) is a dynamic and complex service system. It causes a huge amount of traffic on the network infrastructure of Internet Service Providers (ISPs). Oftentimes, CDN providers and ISPs struggle to find an efficient and appropriate way to cooperate for mutual benefits. This challenge is key to push the quality of service (QoS) for the end-user. We model, simulate, and optimize the behavior of a CDN to provide cooperative solutions and to improve the QoS. Therefor, we determine reasonable server locations, balance the amount of servers and improve the user assignments to the servers. These aspects influence run time effects like caching at the server, response time and network load at specific links. Especially, user request history and profiles are considered to improve the overall performance. Since we consider multiple objectives, we aim to provide a diverse set of pareto optimal solutions using simulation based optimization.

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.