1. Introduction
Content delivery networks (CDNs) have become indispensable for data dissemination on the Internet. By employing caches that are proximal to end users, CDNs provide low-latency and better data availability, increasing the quality of experience (QoE). According to the Cisco Visual Networking Index, CDNs will carry 72% of Internet traffic by 2022, up from 56% in 2017 [
1]. With the continuous growing requirements of bandwidth-hungry and latency-sensitive applications such as 8K video and automatic driving, more and more CDN companies are deploying their CDN sites closer to end users, i.e., in access networks. In this trend, telecommunication companies/mobile network operators (MNOs) have more incentives and requirements than ever to deploy more CDN sites in their hierarchical cloudified networks [
2,
3,
4]. On one hand, they are re-designing the network architecture to support better flexibility and higher efficiency by introducing software-defined networking (SDN) and network function virtualization (NFV) techniques. The re-designed architecture will help provide a better content delivery service at a lower cost (e.g., by saving bandwidth). On the other hand, the representative scenarios in the 5G era require rigorous latency and bandwidth guarantees, which cannot be satisfied by existing remote CDN sites alone. Thus, sites in access networks are needed. In addition, MNOs have a cost advantage over traditional CDN companies when providing CDN services in access networks. The reason is that MNOs are the owners of the access networks, thus they do not have to pay for transit costs or leasing of servers. China Unicom has published its network cloudification plan in the 5G era [
5] and proposed the cloud-edge-fog collaborative content-oriented CDN architecture called CUBE-CDN [
6].
Compared with traditional CDNs, MNOs’ CDN (called telco CDNs in the rest of the paper) in the 5G era faces two main forms of challenge.
First, telco CDN should be operated in cooperation with the underlying network which evolves towards a content-oriented and information-centric architecture [
7,
8]. This is because telco is the owner of both the overlay and the underlay, whose performance should be taken into account simultaneously. In the context of underlay architecture evolution, Information-centric networking (ICN) has attracted much attention. In ICN, every router is cache-enabled and protocols are designed to be in content-centric abstraction in contrast to today’s host-centric abstraction. Communication is based on content look-up and content caching on the path of content delivery (i.e., on-path caching), enabling content-awareness at the network layer. Once a content is cached in a router, the requests of nearby users for this content can all be satisfied by this router directly without being directed to remote servers, thus the response latency is reduced. However, the cache space in routers is limited in comparison to the powerful caching capability of CDNs. In addition, routers that execute content look-up locally can hardly support line-speed forwarding [
9]. On the other hand, CDNs accelerate content delivery by directing content requests to the most suitable CDN site, which is always outside (or at least some distance) from the network that the content requests come from (i.e., off-path caching). Telco CDNs enable edge caching in the access network by deploying a large number of edge sites consisting of caching servers with a high capacity. In this context, we argue that the benefits of both ICN and telco CDN can be utilized in an integrated design fashion, which has rarely been discussed before.
Second, the distributed and cloudified infrastructure brings both flexibility and challenges in key CDN operational aspects. Existing works have focused on some of the challenges. In [
10,
11], they raise the concept of CDN-as-a-service (CDNaaS), which enables on-demand customization of various CDN types with different service level agreements (SLAs). On the basis of the virtualized CDNaaS architecture, important resource allocation issues are studied, e.g., QoE-aware virtual computation/storage allocation [
12,
13,
14], SLA-based virtual CDN (vCDN) embedding [
15,
16], on-demand vCDN migration [
17,
18]. In addition, content caching and user request mapping should be re-designed in the hierarchical and resource-constrained cloud architecture with traffic engineering purposes taken into account [
19,
20,
21,
22,
23,
24,
25,
26,
27,
28]. Besides the aspects mentioned above, efficient pricing strategies and precise profitability analysis are also essential for telco CDNs to ensure sustainable income, i.e., matching revenue to cost. The profitability of introducing CDN service in an MNO is studied in [
29]. The economic impact of telco CDNs’ federation is studied in [
30]. In addition, pricing for cache has attracted much attention [
31,
32,
33,
34].
Despite this, there is still no literature that focuses on bandwidth pricing in the context of telco CDN. Traditional CDNs adopt a unified percentile-based bandwidth pricing strategy among different CDN sites. Take Ali CDN as an example, the bandwidth pricing strategies across different CDN sites are unified on the Chinese mainland [
35]. Nevertheless, the traditional bandwidth pricing strategy is not flexible and efficient enough for telco CDNs in the 5G era. The reasons are as follows. First, sites of the 5G era telco CDNs are deployed in access networks, thus a considerable amount of user requests can be satisfied within the access networks. Nevertheless, it has been shown that traffic in different access networks reveals spatial and temporal heterogeneity, which should be taken into account in the design of pricing strategies to reduce the peak-to-average ratio of the sites’ bandwidth usage [
36]. Second, telcos have to manage their own bandwidth cost while ensuring service quality. When the average congestion level of CDN sites reaches a certain level, telcos have to invest more to expand the sites’ bandwidth capacity. Under this circumstance, a fixed pricing strategy does not provide enough economic incentives for capacity expanding.
To address the above mentioned challenges, in this paper, we first propose a framework that integrates information-centric forwarding with a powerful caching service provided by telco CDNs, enabling more cost-efficient ICN-based telco CDNs. Then, we design a location-dependent pricing strategy (LDP) that considers the spatially heterogeneous features of traffic in geo-distributed telco CDN sites. If the variation of the sites’ congestion level reaches a threshold, then the prices of the sites are set differently according to their relative congestion levels; whereas, if all sites are at a similar congestion level, then the prices are set the same according to the average congestion level. In addition, our design ensures that in both cases, the higher a site’s congestion level is, the higher its bandwidth price is. This design of joint location dependent pricing and request mapping helps telco CDNs manage sites’ bandwidth more efficiently and to better match revenue to cost. Specifically, the location-dependent design encourages more usage in under-utilized sites through setting lower prices. In contrast, for congested sites, it sets higher prices to let users pay more, thus its increasing bandwidth cost can be compensated.
In particular, we make four contributions.
First, we propose an integrated framework for cost-efficient and effective deployment of ICN-based telco CDNs. Specifically, we propose to employ powerful off-path caching provided by telco CDNs to complement on-path caching in ICN. In our design, once a cache miss occurs at an ICN router, the content request is directed to the most suitable telco CDN site by the border router of the access network. Therefore, the integrated design has the features of both content-centric forwarding and powerful off-path caching, giving a cost-efficient and realistic choice for the deployment of ICN-based telco CDNs in the 5G era;
Second, we propose a framework that consists of a location-dependent pricing (LDP) module, price-aware request mapping (PARM) module and status measurement (SM) modules. The LDP module dynamically sets prices of different CDN sites according to the sites’ bandwidth usage measured by the SM modules. The PARM module calculates the request mapping rules according to the prices and the application access statistics measured by the SM modules;
Third, we carefully design the LDP module and the PARM module. Specifically, an algorithm is presented to describe the LDP procedure, considering the spatial and temporal heterogeneity of different telco CDN sites. Then, an optimization problem that minimizes user perceived latency and the total payment is formulated to make the PARM decisions;
Fourth, we conduct extensive simulations to evaluate the proposed design. The simulation results show that our design helps ICN-based telco CDNs flexibly price different CDN sites according to their congestion levels. When the congestion level of a CDN site increases to a certain level, LDP sets the price higher, helping the telco keep pace with its increasing bandwidth cost. Moreover, we observe the impact of some key parameter settings on the telco’s revenue and cost, helping a telco to set proper parameters when adopting our design.
The rest of the paper is organized as follows. In
Section 2, we review the related work. In
Section 3, we illustrate the integrated design of the ICN-based telco CDN architecture. Then, we demonstrate the framework that integrates the location dependent pricing and the price-aware request mapping. In
Section 4, we present the details of the LDP algorithm design followed by the problem formulation of the optimization running in the PARM module. Then, in
Section 5, we conduct extensive simulations, analyze the results in different traffic patterns and different parameter settings. In
Section 6, we discuss parameter setting issues in an implementation point of view. Finally, we conclude the paper in
Section 7.
2. Related Work
As one of the key algorithmic functionalities in CDNs, request mapping/load balancing has always been a hot topic for its direct impact on the end users’ QoE and the CDN providers’ costs [
37]. Specifically, different types of CDNs have different design goals in terms of request mapping.
In the context of traditional CDNs, most CDN sites are deployed in the data centers that are owned by CDN companies and the data centers are always in core networks. Therefore, a key principle in request mapping design is to take users’ location into consideration. Researchers from Bell Labs propose to use user location and content type as match fields of the routing table in the request mapping server [
38]. Stability of the designed mechanism is guaranteed by the Lyapunov optimization technique while the transmission cost is minimized. Different from current request mapping mechanisms that are usually based on DNS/HTTP redirection, the proposed mechanism provides more flexibility and finer mapping granularity, which help reduce average delivery time and promote cache hit ratio. Similarly, researchers from Akamai also propose to take user location into account and realize their design in commercial deployment [
39].They make it possible to specify the client’s IP prefix by using an EDNS0 extension of the existing DNS protocol. Specifically, the IP prefix of a user is included when a DNS look-up request is forwarded from a local DNS (LDNS) to an up-level authoritative DNS server. The EDNS0-based request mapping design significantly reduces round-trip-time (RTT), the time to first byte, and content downloading time.
In the context of cloud CDNs [
40], CDN sites are deployed in cloud data centers which are highly distributed and always have to transit a number of Internet Service Providers (ISPs). Therefore, a key principle of request mapping is to optimize the cost efficiency. For example, the authors of [
41] argue that geographical diversity of bandwidth cost brings a highly challenging problem of how to minimize total cost. In this context, bandwidth cost means the bandwidth prices that are paid by a cloud CDN provider to different transit ISPs. They formulate a problem that minimizes the summation of bandwidth cost and energy cost. In addition, they propose a hybrid particle swarm algorithm (HPSO) to solve the problem. Simulations show that the proposed design can minimize the total cost and promote throughput of the system. The authors of [
42] also take into account the bandwidth prices charged by transit ISPs when solving request mapping and routing problems. In order to reduce complexity and promote robustness in the implementation, they design a distributed tide algorithm and evaluate the effectiveness. Simulations show that the proposed design can guarantee minimized average latency and average cost.
In the context of telco CDN (ISP-operated CDN), request mapping is often designed in collaboration with congestion management of the underlying network infrastructure. The reason is that a telco CDN has to optimize both the service performance that its overlay (CDN) provides and the efficiency of its underlying network infrastructure. The authors of [
25] argue that in Mobile CDNs that enhance Base Stations with storage capability, blind redirection of user requests upon content placement can cause traffic congestion. They investigate a joint optimization problem of content placement and request mapping to achieve both congestion avoidance and load balancing. By using the stochastic optimization model and Lyapunov optimization technique, they design an algorithm that efficiently achieves a low transmission cost. An evaluation confirms that the design goals were achieved. The authors of [
43] propose a flexible content-based and network-status aware request mapping mechanism, taking the content prefix and traffic statistics as input parameters. Simulations show that average traffic volume and average access latency are both reduced.
3. System Model
In this section, we propose the overall design of the ICN-based telco CDN architecture. Then, we present the framework that consists of an LDP module, PARM module, and SM modules. Finally, we illustrate the functions and relationship between the listed modules.
Figure 1 shows the ICN-based telco CDN service scenario we consider in this paper in comparison with the traditional CDN service scenario. Traditional CDNs, e.g., Alibaba CDN and Tencent CDN, always deploy their sites in core networks or connect their sites with telcos on Point-of-Presence (PoP) [
22] and serve traditional IP access networks. In comparison, telcos in the 5G era can deploy amounts of sites in their cloudified hierarchical access networks. We propose to use these sites to provide a caching service for both ICN-based and IP-based underlay network regions. In IP network regions, content requests are directed to CDN sites after DNS look-up and data flows are forwarded based on IP address. In ICN network regions, content requests are looked up based on content name at every router; if the requested content is cached locally, then the content chunks are directly sent back. Once a cache miss occurs, the request is forwarded to the border router, which is employed to direct the unsatisfied requests to the CDN sites. Take ICN-based region 1 as an example, if a cache miss for content A occurs at R1, border router R2 will direct the request to CDN site 1 or site 2 according to the forwarding information table (FIB) rules (as shown by R2’s FIB in
Figure 1). In this paper, we assume there are
M telco CDN sites, serving
L user access regions. The regions may be partitioned in base station level or aggregation-level. Let
denote the bandwidth capacity of site
m, and
denote the historical traffic usage of site
m (
). Let
denote the latency between region
l (
) and site
m (
). Assume that
N applications adopt the telco CDN service, paying their bandwidth and cache expenses to the telco. Let
denote the request arrival rate of application
n (
) in region
l (
). Let
denote the expected response time of application
n (
).
Given the input parameters above, the telco should set the bandwidth price and calculate the rules of user request mapping. Let
denote the unit bandwidth price of site
m (
). Let
denote the portion of application
n requests from region
l directed to site
m. For clarity, we summarize the notations mentioned above in
Table 1.
In our design, the bandwidth prices and the request mapping rules are periodically calculated by the LDP module and PARM module, respectively. As shown in
Figure 2, the SM module measures the bandwidth usage, the application access statistics at different sites, and updates the latest state to the LDP module and the PARM module. The LDP module calculates bandwidth prices according to the congestion level and its variation. Then, on the basis of the prices and other system parameters as listed in
Table 1, the PARM module runs an optimization to obtain the latest request mapping rules and install the rules on the relevant sites. Given
,
,
as the update period of LDP, PARM, SM, respectively, then they should be set according to the following inequality: