Characterizing caching workload of a large commercial Content Delivery Network

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.

LofoSwitch: An online policy for concerted server and disk power control in content distribution networks

Content Distribution Networks (CDN) appeared a decade ago as a method for reducing latencies, improving performance experienced by Internet users, and limiting the effect of flash-crowds, so as balance load in servers. Content Distribution has evolved in different ways (e.g. cloud computing structures and video streaming distribution infrastructures). The solution proposed in early CDN was the location of several controlled caching servers close to clients, organized and managed by a central control system. Many companies deployed their own CDN infrastructure– and so demonstrating the resulting effectiveness. However, the business model of these networks has evolved from the distribution of static web objects to video streaming. Many aspects of deployment and implementation remain proprietary, evidencing the lack of a general CDN model, although the main design concepts are widely known. In this work, the authors represent the structure of a CDN and the performance of some of its parameters, using queuing theory, simplifying the redirection schema and studying the elements that could determine the improvement in performance. The main contribution of the work is a general expression for a CDN environment and the relationship between different variables like caching hit ratios, network latency, number of surrogates, and server capacity; this proves that the use of CDN outperform the typical client/server architecture.

The development of Network Function Virtualization (NFV) and Software Defined Networks (SDN) standards is an opportunity for Mobile Network Operators (MNOs) to deploy Content Delivery Network (CDN) functionalities into the mobile network edge, such as Base Stations (BSs). In this paper, we investigated the content placement problem for the BS-based wireless CDN system. We call the storage resources implemented on BSs as storage helpers. Due to the limited helper storage capacity and the limited user population served per BS, helpers exhibit low hit ratio comparing to traditional CDN edge servers serving a wide area. Then, cooperation is a suitable means to enhance the performance of the wireless CDN system. We propose that BSs close to each other cooperate in replicating content and replying user requests. We formulate the optimum content placement problem to minimize the traffic pressure on mobile network gateways, and show the problem complexity is NP-Hard. We then transform the problem into a multiple-Maximum Weighted Independent Set problem, and propose a heuristic algorithm. The evaluation shows that the hit ratio is improved by our algorithm comparing to the traditional Least Frequently Used (LFU) policy without cooperation.

Content delivery network (CDN) with a high quality-of-service (QoS) requirement deals with three challenging problems: 1) a cache server deployment problem to determine where to locate cache servers, 2) a request routing problem to settle which cache server serves a user request, and 3) a content replication problem to decide which content is stored in a cache server. Cooperation among cache servers significantly improves the user QoS by increasing the cached content diversity of the network. Since the aforementioned problems are tightly coupled, we formulate a new CDN model which jointly considers all the three problems under the cooperative content replication with optimal solutions using integer linear programming. As the joint optimization problem is an NP-hard problem, we propose a greedy heuristic algorithm which is close to the optimal solution. We consider two scenarios for application of the algorithm, where the popularities of contents are identical and are different among nodes. The performance of the algorithm is investigated on the actual topology sampled from the Level 3 CDN network. By increasing the cached content diversity in the network, the joint optimization problem under the cooperative content replication shows higher performance than that of the non-cooperation case.

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

Web caching and replication tune capacity with performance and they have become essential components of the Web. In practice, caching and replication techniques have been applied in proxy servers and Content Distribution Networks (CDNs) respectively. In this paper, we investigate the benefits of integrating caching policies on a CDN’ s infrastructure. Using a simulation testbed, our results indicate that there is much room for performance improvement in terms of perceived latency, hit ratio and byte hit ratio. Moreover, we show that the combination of caching with replication fortifies CDNs against flash crowd events.

In this paper, we propose a method to maintain a Peer-to-Peer (P2P) overlay in live streaming systems based on a hybrid of Content Delivery Network (CDN) and P2P Technology. The key idea of the proposed method is to explicitly take into account the upload capacity of each peer in selecting peers which can directly receive the live stream from a cache server in the CDN. In addition, it prepares several backup peers to support the task of upload in such a way that a stream is fed to a P2P client if the local buffer at the client becomes scarce. The performance of the proposed scheme is evaluated by simulation.

Along with the continuing evolution of the Internet and its applications, Content Delivery Networks (CDNs) have become a hot topic with both opportunities and challenges. CDNs were mainly proposed to solve content availability and download time issues by delivering content through edge cache servers deployed around the world. In our previous work, we presented a novel CDN architecture based on a Fog computing environment as a promising solution for real-time applications. In such architecture, we proposed to use a name-based routing protocol following the Information Centric Networking (ICN) approach, with a popularity-based caching strategy to guarantee overall delivery performance. To validate our design principle, we have implemented the proposed Fog-based CDN architecture with its major protocol components and evaluated its performance, as shown through this article. On the one hand, we have extended the Optimized Link-State Routing (OLSR) protocol to be content aware (CA-OLSR), i.e., so that it uses content names as routing labels. Then, we have integrated CA-OLSR with the popularity-based caching strategy, which caches only the most popular content (MPC). On the other hand, we have considered two similar architectures for conducting performance comparative studies. The first is pure Fog-based CDN implemented by the original OLSR (IP-based routing) protocol along with the default caching strategy. The second is a classical cloud-based CDN implemented by the original OLSR. Through extensive simulation experiments, we have shown that our Fog-based CDN architecture outperforms the other compared architectures. CA-OLSR achieves the highest packet delivery ratio (PDR) and the lowest delay for all simulated numbers of connected users. Furthermore, the MPC caching strategy shows higher cache hit rates with fewer numbers of caching operations compared to the existing default caching strategy, which caches all the pass-by content.

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.

Social Welfare Implications of Different Cache Deployment Scenarios

This article addresses the architectural challenges involved in building global-scale distributed cloud systems for mobile customers. It examines key components necessary for delivering consistent, low-latency experiences to users worldwide while maintaining high availability, security, and cost efficiency. The article explores foundational architecture elements, including multi-region deployment models, load-balancing strategies, content delivery networks, and API gateway architectures. It further investigates data management strategies covering distributed database architectures, consistency models, caching approaches, and data sovereignty requirements. Additional sections detail scalability optimization techniques, including autoscaling, microservices, asynchronous processing, and deployment automation, followed by an in-depth look at reliability practices and operational excellence. Throughout, the article presents real-world performance metrics and implementation strategies to provide a comprehensive framework for organizations building or expanding mobile cloud infrastructure.

The last few years have witnessed an explosion of content-rich services over the Internet, and thereby content distribution itself grows into one of the most important Internet applications. Content delivery networks or content distribution networks (CDNs) first emerged to address the efficiency of content distribution over the Web for the end users, since content delivery has become important for improvement of Web performance [53]. In order to improve accessibility, decrease access delay, maximize bandwidth utilization, and maintain correctness for the users, CDN distributes content to a group of geographically dispersed cache servers. Note that these servers are located as close as possible to the users.

Data is growing quickly due to a significant increase in social media applications. Today, billions of people use an enormous amount of data to access the Internet. The backbone network experiences a substantial load as a result of an increase in users. Users in the same region or company frequently ask for similar material, especially on social media platforms. The subsequent request for the same content can be satisfied from the edge if stored in proximity to the user. Applications that require relatively low latency can use Content Delivery Network (CDN) technology to meet their requirements. An edge and the data center constitute the CDN architecture. To fulfill requests from the edge and minimize the impact on the network, the requested content can be buffered closer to the user device. Which content should be kept on the edge is the primary concern. The cache policy has been optimized using various conventional and unconventional methods, but they have yet to include the timestamp beside a video request. The 24-h content request pattern was obtained from publicly available datasets. The popularity of a video is influenced by the time of day, as shown by a time-based video profile. We present a cache optimization method based on a time-based pattern of requests. The problem is described as a cache hit ratio maximization problem emphasizing a relevance score and machine learning model accuracy. A model predicts the video to be cached in the next time stamp, and the relevance score identifies the video to be removed from the cache. Afterwards, we gather the logs and generate the content requests using an extracted video request pattern. These logs are pre-processed to create a dataset divided into three-time slots per day. A Long short-term memory (LSTM) model is trained on this dataset to forecast the video at the next time interval. The proposed optimized caching policy is evaluated on our CDN architecture deployed on the Korean Advanced Research Network (KOREN) infrastructure. Our findings demonstrate how adding time-based request patterns impacts the system by increasing the cache hit rate. To show the effectiveness of the proposed model, we compare the results with state-of-the-art techniques.

The number of users downloading video content on the Internet has dramatically increased recently, and obtaining huge rich content items, e.g., movie files, is becoming a popular application of the Internet. The transmission bandwidth consumed by delivering rich content is enormous. Therefore, it is urgent for Internet service providers to design an efficient delivery system that minimizes the amount of network resources consumed. Content delivery networks are widely used to reduce the flow hop length and the response time when obtaining content items. To improve the cache efficiency, various methods of replacing caches have been proposed, with the target of improving the cache hit ratio. The effect of reducing the traffic load on network links by delivering content items from caches depends on the distance from the origin server to the users. However, many existing methods do not consider the origin server distance. To maximize the effect of reducing the link load obtained by caches, we propose here a cache-replacement policy based on the hop distance to the origin servers. To simplify the caching algorithm, we divide the storage capacity of cache severs into multiple virtual caches and manage content items separately on the basis of the hop distance to origin servers. We also propose an optimal method for designing the capacities of virtual caches that maximizes the total expected reduction of flow hop length. Through numerical evaluation, we show that the proposed method can decrease the average link load by about 10% compared with the normal least recently used.