Following successful adoption of cloud computing, many service providers (SPs) are now using high-performance Virtual Machines (VMs) located in large datacenters owned by public cloud infrastructure providers to deploy their virtual network functions (VNFs). Since using these VMs has a cost depending on utilization time, a complex problem of VNF placement and scheduling (VPS) must be addressed to achieve satisfactory network performance (e.g., latency) while minimizing the cost paid to lease VMs. In this study, a cost-efficient VPS scheme (CE-VPS) is proposed to address the VPS problem in public cloud networks considering dynamic requests of ordered sequences of VNFs. Our CE-VPS scheme goes beyond existing solutions as it models some important practical aspects such as an additional latency incurred by booting a VM and installing a VNF instance. Also, CE-VPS considers that VNFs can be multi-threaded or single-threaded, and that their throughput as a function of allocated computing resources must be modeled differently. CE-VPS is formulated as a mixed inter linear program (MILP) and also as an efficient heuristic algorithm. CE-VPS achieves lower cost and latency than conventional Best-Availability and Cost-Efficient Proactive VNF Placement schemes, and a better trade-off between resource consumption and latency performance than a conventional Low-Latency scheme.

Mobile Fronthaul (MF) is defined as the connection between Remote Radio Head (RRH) and Baseband Processing Unit (BBU) in a Cloud Radio Access Network (C-RAN). Dedicated MF connections between RRH and BBU would be very costly. Thus, Time Division Multiplexing Ethernet Passive Optical Network (TDM EPON) is a promising solution to reduce cost as it can enable multiplexing gain. Note that, even though mobile users transmit intermittently, in the upstream channel RRH is sampling radio signal all the time, limiting the achievable multiplexing gain. Our study enhances the conventional TDM EPON architecture by introducing traffic classification, sifting of useless data to avoid the transmission of unnecessary EPON frames, and hence increasing the multiplexing gain in the upstream channel. We also propose a Hybrid Bandwidth Allocation (HBA) scheme to exploit the traffic usefulness classification information. Simulation results show significant improvements in terms of load and number of connected RRHs that can be supported by same EPON, while keeping the end-to-end delay under 100 μs.

With the rapid growth of content-based network services, there is increasing interest in reducing the emissions associated with brown-energy consumption in Content Delivery Networks (CDNs). At the same time, content needs to be placed in safe Data Center (DC) locations, which are unlikely to be hit by disasters. Further risk reduction is achieved using content replication which provides inter-DC content redundancy. Unfortunately, there is contention between the objectives of brown-energy minimization and risk reduction since replicating content increases brown-energy consumption. To address these contradictory issues, we leverage the concept of content fragmentation used inside DCs and propose an inter-DC Content Fragmentation (CF) scheme which aims to achieve brown-energy saving by reducing storage overhead while maintaining low risk compared to basic replication schemes. We also propose a Green and Low-Risk Content Placement approach (GR-CP) to address the tradeoff between brown-energy consumption and disaster risk. Both CF and replication schemes are implemented in GR-CP and evaluated over a range of content popularity and redundancy levels. Our results show that CF outperforms replication scheme except when content popularity is high and the risk constraint is stringent. When popularity and risk are both low, CF can save more brown energy by using low-redundancy fragmentation techniques.