With the growing demand for openness, scalability, and granularity, mobile network function virtualization (NFV) has emerged as a key enabler for the most of mobile network operators. NFV decouples network functions from hardware devices. This decoupling allows network services, called Virtualized Network Functions (VNFs), to be hosted on commodity hardware which simplifies and enhances service deployment and management for providers, improves flexibility, and leads to efficient and scalable resource usage, and lower costs. The proper placement of VNFs in the hosting infrastructures is one of the main technical challenges. This placement significantly influences the network’s performance, reliability, and operating costs. The VNF placement is NP-Hard. Therefore, there is a need for placement methods that can cope with the complexity of the problem and find appropriate solutions in a reasonable duration. The primary purpose of this study is to provide a taxonomy of optimization techniques used to tackle the VNF placement problems. We classify the studied papers based on performance metrics, methods, algorithms, and environment. Virtualization is not limited to simply replacing physical machines with virtual machines or VNFs, but may also include micro-services, containers, and cloud-native systems. In this context, the second part of our article focuses on the placement of Containers Network Functions (CNFs) in edge/fog computing. Many issues have been considered as traffic congestion, resource utilization, energy consumption, performance degradation, etc. For each matter, various solutions are proposed through different surveys and research papers in which each one addresses the placement problem in a specific manner by suggesting single objective or multi-objective methods based on different types of algorithms such as heuristic, meta-heuristic, and machine learning algorithms.

Content delivery networks (CDNs) have gained immense popularity over the years. Replica server placement is a key design issue in CDNs. It entails placing replica servers at meticulous locations, such that cost is minimized and quality of service of end-users is satisfied. Many replica server placement models have been proposed in the literature of traditional CDN. As the CDN architecture is evolving through the adoption of emerging paradigms, such as, cloud computing and network functions virtualization, new algorithms are being proposed. In this paper, we present a comprehensive survey of replica server placement algorithms in traditional and emerging paradigm-based CDNs. We categorize the algorithms and provide a summary of their characteristics. Besides, we identify requirements for an efficient replica server placement algorithm and perform a comparison in the light of the requirements. Finally, we discuss potential avenues for further research in replica server placement in CDNs.

Value-added services (e.g., overlaid video advertisements) have become an integral part of today's content delivery networks (CDNs). To offer cost-efficient, scalable, and more agile provisioning of new value-added services in CDNs, network functions virtualization paradigm may be leveraged to allow implementation of fine-grained services as a chain of virtual network functions (VNFs) to be placed in CDN. The manner in which these chains are placed is critical as it both affects the quality of service (QoS) and provider cost. The problem is however, very challenging due to the specifics of the chains (e.g., one of their end-points is not known prior to the placement). We formulate it as an integer linear program and propose a cost efficient proactive VNF placement and chaining algorithm. The objective is to find the optimal number of VNFs along with their locations in such a manner that the cost is minimized while QoS is met. Apart from cost minimization, the support for large-scale CDNs with a large number of servers and end-users is an important feature of the proposed algorithm. Through simulations, the algorithm's behavior for small-scale to large-scale CDN networks is analyzed.

Fog computing has emerged as a promising technology that can bring cloud applications closer to the devices at the network edge. The fog infrastructure contains mainly distributed and heterogeneous fog devices such as in the context of the Internet of Things. Unlike traditional data centers, those devices are characterized by sporadic resources availability, mobility, and increased flexibility. However, resource allocation mechanisms proposed currently for fog computing still lack the support of dynamic behavior. In this paper, we propose novel resource management algorithms capable of flexible service provisioning in a dynamic fog computing environment. Specifically, the joint problem of container placement and task provisioning is formulated with Integer Linear Programming. Due to its NP-hardness, we propose a low-complex Particle-Swarm-Optimization based meta-heuristic and a Greedy heuristic. Our solutions aim to optimize the number of served end-users with a predefined delay-threshold while considering dynamic fog nodes behavior / mobility and resources availability of fog nodes. Using real-world mobility data sets and different resources’ availability models, conducted simulations demonstrate that the PSO-based Algorithm achieves near-optimal results. Whereas, the Greedy Algorithm realizes only 10%-30% less success ratio than the optimal solution with negligible execution time.

The IPv6 routing protocol for low power and lossy networks (RPL) was accepted as the standard routing protocol for Internet of Things (IoT) by Internet engineering task force on March 2012. Since then, it has been used in different IoT applications. Though RPL considerably deals with IoT network requirements, there are still some open problems to solve, since it was not initially designed for IoT applications. In this paper, we address the problem of packet loss and power depletion in an RPL-based network under heavy and highly dynamic load. We address this problem in three steps: First, we present a context-aware objective function (CAOF), which computes the rank, considering the context of the node. CAOF also avoids thundering herd phenomenon by gradual shifting from a high rank value toward the real rank value. Second, we present a new routing metric, known as context-aware routing metric (CARF) which considers the status of remaining power and queue utilization of parent chain toward the root in a recursive manner while lessening the effect of upstream parents as it gets farther down the path. Through comprehensive evaluations, we show that this metric leads to a better decision about the proper parent in a network with high traffic dynamicity, rather than deciding merely based on the parent rank. Third, we present a new parent selection mechanism, which selects the best parent based on CARF and some other metrics while avoiding routing loops by a simple yet effective countermeasure. Evaluation results reveal improvements in network lifetime while decreasing packet loss in comparison with standard specification of RPL.

This paper provides a comprehensive survey of content placement (CP) algorithms for cloud-based content delivery networks (CCDNs). CP algorithms are essential for content delivery for their major role in selecting content to be stored in the geographically distributed surrogate servers in the cloud to meet end-user demands with quality of service (QoS). Evidently, the key objectives of CP, i.e., cost and QoS, are competing. Cost is determined by the underlying cost model of the CCDN infrastructure while the delivered QoS is determined by where the content is placed in the CCDN. Therefore, we provide an overview of the content and the CCDN infrastructure. The overview of the content includes content characteristics and the influence of Online Social Networking on CP. The overview of the CCDN infrastructure includes elasticity and cost model, which affect CP. Our goal is to provide a holistic perspective of the aspects that impact CP algorithms and their efficiency. From the influential factors, we derive a set of design criteria for CP algorithms in CCDNs. We discuss the state-of-the-art CP algorithms for CCDNs and evaluate them against the well-motivated design criteria. We also delineate practical implications and uncover future research challenges.