Green Resource Management for Over-the-Top Services in 5G Networks Using Matching Theory

Abstract

Nowadays, over-the-top (OTT) applications can be accessed via Internet connections over cellular networks. Mobile network operators (MNOs) strive to accommodate mobile traffic through energy efficient shared networks. The OTT service providers (OSPs) need to interact with MNOs and require resources for offering desired quality-of-service (QoS) levels to users of different categories, with different QoS requirements, which imply prioritization of certain OTT flows. Resource scheduling should respect network neutrality, which forbids OSP prioritization. Furthermore, OSPs request resources periodically, according to their performance goals, causing delay in flows' accommodation due to i) the time required for information exchange between OSPs and MNOs, affected by network congestion, and ii) the time required for flows to receive resources, affected by the number of active flows. The intervention of OSPs may induce additional energy cost for the MNOs and affect the mobile network energy efficiency. Acknowledging the lack of OSP-oriented resource management approaches, we introduce a matching theoretic flow prioritization (MTFP) algorithm and investigate delay and energy efficiency through extensive simulations. Our study shows that MTFP improves both metrics comparing to the best effort approach, whereas its performance is affected by the OTT traffic level and the frequency of resource allocation process.

The Green Blockchains of Circular Economy

“…The goal is to offer a common view of the assets' state, with smart contracts providing share data with integrity, while enhancing the interoperability and transparency between the interacting partners. A CE can also involve small-medium enterprises (SMEs) that might not possess the skills or the resources [11] to perform the computationally heavy machine learning (ML) procedures that optimize the green operation of the installed services/equipment, such as the example in [12] where ML is utilized to improve the energy consumption of a data center and other relevant cases such as [13][14][15]. Thereupon, blockchaining is also promoted as an enabler for federated learning.…”