Impact of Processing-Resource Sharing on the Placement of Chained Virtual Network Functions

Network Function Virtualization (NFV) provides higher flexibility for network operators and reduces the complexity in network service deployment. Using NFV, Virtual Network Functions (VNF) can be located in various network nodes and chained together in a Service Function Chain (SFC) to provide a specific service. Consolidating multiple VNFs in a smaller number of locations would allow decreasing capital expenditures. However, excessive consolidation of VNFs might cause additional latency penalties due to processing-resource sharing, and this is undesirable, as SFCs are bounded by service-specific latency requirements. In this paper, we identify two different types of penalties (referred as "costs") related to the processingresource sharing among multiple VNFs: the context switching costs and the upscaling costs. Context switching costs arise when multiple CPU processes (e.g., supporting different VNFs) share the same CPU and thus repeated loading/saving of their context is required. Upscaling costs are incurred by VNFs requiring multi-core implementations, since they suffer a penalty due to the load-balancing needs among CPU cores. These costs affect how the chained VNFs are placed in the network to meet the performance requirement of the SFCs. We evaluate their impact while considering SFCs with different bandwidth and latency requirements in a scenario of VNF consolidation.Comment: Accepted for publication in IEEE Transactions on Cloud Computin

Efficient Resource Management Mechanism for 802.16 Wireless Networks Based on Weighted Fair Queuing

Wireless Networking continues on its path of being one of the most commonly used means of communication. The evolution of this technology has taken place through the design of various protocols. Some common wireless protocols are the WLAN, 802.16 or WiMAX, and the emerging 802.20, which specializes in high speed vehicular networks, taking the concept from 802.16 to higher levels of performance. As with any large network, congestion becomes an important issue. Congestion gains importance as more hosts join a wireless network. In most cases, congestion is caused by the lack of an efficient mechanism to deal with exponential increases in host devices. This can effectively lead to very huge bottlenecks in the network causing slow sluggish performance, which may eventually reduce the speed of the network. With continuous advancement being the trend in this technology, the proposal of an efficient scheme for wireless resource allocation is an important solution to the problem of congestion. The primary area of focus will be the emerging standard for wireless networks, the 802.16 or “WiMAX”. This project, attempts to propose a mechanism for an effective resource management mechanism between subscriber stations and the corresponding base station

Deep Reinforcement Learning for Resource Management in Network Slicing

Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users' activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.Comment: The manuscript has been accepted by IEEE Access in Nov. 201