Problem Statement
Traditional pricing strategies employed by sellers often lack flexibility and fail to adapt to rapidly changing market dynamics. Sellers face challenges in optimizing pricing for their products in real-time, considering factors such as market trends, competitor pricing, and fluctuations in demand. This can lead to missed revenue opportunities, suboptimal pricing decisions, and decreased competitiveness.
Abstract
Develop dynamic pricing algorithms for sellers to optimize pricing strategies based on real-time market trends and demand fluctuations. Leveraging machine learning and data analytics, the project aims to create algorithms that continuously analyze market data, competitor pricing, and demand patterns. The dynamic pricing system will provide sellers with real-time insights and recommendations to adjust their prices dynamically, maximizing revenue and competitiveness in the market.
Outcome
The project’s outcome is a dynamic pricing system that enables sellers to make informed and adaptive pricing decisions. Sellers utilizing the developed algorithms can respond swiftly to changes in market conditions, ensuring that their pricing strategies align with current trends and demand levels. The system is expected to improve revenue optimization for sellers, enhance competitiveness in the market, and contribute to a more agile and data-driven approach to pricing in the ever-evolving business landscape.
Reference
A service chain is a combination of network services (e.g. network address translation (NAT), a firewall, etc.) that are interconnected to support an application (e.g. video-on-demand). Building a service chain requires a set of specialized hardware devices each of which need to be configured with their own command syntax. By moving management functions out of forwarding hardware into controller software, software-defined networking (SDN) simplifies provisioning and reconfiguration of service chains. By moving the network functions out of dedicated hardware devices into software running on standard x86 servers, network function virtualization (NFV) turns the deployment of a service chain into a more (cost)-efficient and flexible process. In an SDN/NFV-based architecture, those service chains are composed of virtual network functions (VNFs) that need to be mapped to physical network components. In literature, several algorithmic approaches exist to do so efficiently and cost-effectively.
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