A Survey on Content Placement Algorithms for Cloud-Based Content Delivery Networks <s> E. CONSTRAINTS <s> In recent years, technical challenges are emerging on how to efficiently distribute the rapid growing user-generated contents (UGCs) with long-tailed nature. To address this issue, we have previously proposed cloud centric media network (CCMN) for cost-efficient UGCs delivery. In this paper, we further study the content placement problem in CCMN. Our objective is to minimize the monetary cost incurred by using cloud resources to orchestrate an elastic and global content delivery network (CDN) service. In particular, this objective is achieved via a two-step method. First, for a single content, we map it into a k-center problem, and find a logarithmic relationship between the mean hop distance from users to contents, and the reciprocal of replica number. Second, for multiple contents, we formulate a convex optimization with storage and bandwidth capacity constraints, which can be solved by our proposed algorithm. Finally, we verify the algorithm based on real-world traces collected from a popular video website in China. Our numerical results suggest that, the optimal number of replica for each content follows a power law in respect to its popularity, under feasible storage and bandwidth constraints, in a set of deployed backbone networks. <s> BIB001 </s> A Survey on Content Placement Algorithms for Cloud-Based Content Delivery Networks <s> E. CONSTRAINTS <s> We propose a novel roadside unit (RSU) cloud, a vehicular cloud, as the operational backbone of the vehicle grid in the Internet of Vehicles (IoV). The architecture of the proposed RSU cloud consists of traditional and specialized RSUs employing software-defined networking (SDN) to dynamically instantiate, replicate, and/or migrate services. We leverage the deep programmability of SDN to dynamically reconfigure the services hosted in the network and their data forwarding information to efficiently serve the underlying demand from the vehicle grid. We then present a detailed reconfiguration overhead analysis to reduce reconfigurations, which are costly for service providers. We use the reconfiguration cost analysis to design and formulate an integer linear programming (ILP) problem to model our novel RSU cloud resource management (CRM). We begin by solving for the Pareto optimal frontier (POF) of nondominated solutions, such that each solution is a configuration that minimizes either the number of service instances or the RSU cloud infrastructure delay, for a given average demand. Then, we design an efficient heuristic to minimize the reconfiguration costs. A fundamental contribution of our heuristic approach is the use of reinforcement learning to select configurations that minimize reconfiguration costs in the network over the long term. We perform reconfiguration cost analysis and compare the results of our CRM formulation and heuristic. We also show the reduction in reconfiguration costs when using reinforcement learning in comparison to a myopic approach. We show significant improvement in the reconfigurations costs and infrastructure delay when compared to purist service installations. <s> BIB002
As illustrated in Fig. 4 , a CP model must be subject to various constraints to handle real world scenarios. The hierarchical structure inherently induces heterogeneity into the surrogate servers. Therefore, surrogate servers have different storage and processing capacities and utilization costs. Often, implicit storage constraints are modeled into CP, such as the k-replica constraint BIB001 , which limits the number of replicas of content to k. Similarly, the underlying network link layer will have different bandwidth capacities and utility cost functions. In CCDNs, the surrogate servers are generally in datacenters in the storage clouds that are divided into regions and zones. The links within zones in a region are low latency and high capacity, in contrast to inter-region bandwidth links. Often, CP models are required to guarantee either hard or soft QoS metrics, i.e. either meets all or some of the end-user requests for content or meets some predefined latency requirements, such as end-to-end delay. Alternatively, QoS can be modeled as a maximization of the allowable violations in the SLA BIB002 . For instance, if SLA requires 99% uptime, this implies that up to 1% QoS violations are acceptable without penalty. Therefore, heuristics can be designed to leverage this slack. It should be noted that various constraints can be modeled as objectives. Therefore, objectives are essentially relaxed constraints and it is not uncommon to find CP models where objectives and constraints are interchanged. A CP model with all utility functions as constraints is very strict as it does not allow the violations of any of the utility functions. However, utility functions in the objective allow violations that are penalized, which is the cost of the model. CP models often include only a few of the constraints or objectives while others are simplified or assumed to hold true.