On the effectiveness of an optimization method for the traffic of TCP-based multiplayer online games

This paper studies the feasibility of using an optimization method, based on multiplexing and header compression, for the traffic of Massively Multiplayer Online Role Playing Games (MMORPGs) using TCP at the Transport Layer. Different scenarios where a number of flows share a common network path are identified. The adaptation of the multiplexing method is explained, and a formula of the savings is devised. The header compression ratio is obtained using real traces of a popular game and a statistical model of its traffic is used to obtain the bandwidth saving as a function of the number of players and the multiplexing period. The obtained savings can be up to 60 % for IPv4 and 70 % for IPv6. A Mean Opinion Score model from the literature is employed to calculate the limits of the multiplexing period that can be used without harming the user experience. The interactions between multiplexed and non-multiplexed flows, sharing a bottleneck with different kinds of background traffic, are studied through simulations. As a result of the tests, some limits for the multiplexing period are recommended: the unfairness between players can be low if the value of the multiplexing period is kept under 10 or 20 ms. TCP background flows using SACK (Selective Acknowledgment) and Reno yield better results, in terms of fairness, than Tahoe and New Reno. When UDP is used for background traffic, high values of the multiplexing period may stress the unfairness between flows if network congestion is severe

Modelling TCP congestion control dynamics in drop-tail environments

In this paper we study communication networks that employ drop-tail queueing and additive-increase multiplicative-decrease (AIMD) congestion control algorithms. We show that the theory of non-negative matrices may be employed to model such networks and to derive basic theorems concerning their behaviour

Service Quality Assessment for Cloud-based Distributed Data Services

The issue of less-than-100% reliability and trust-worthiness of third-party controlled cloud components (e.g., IaaS and SaaS components from different vendors) may lead to laxity in the QoS guarantees offered by a service-support system S to various applications. An example of S is a replicated data service to handle customer queries with fault-tolerance and performance goals. QoS laxity (i.e., SLA violations) may be inadvertent: say, due to the inability of system designers to model the impact of sub-system behaviors onto a deliverable QoS. Sometimes, QoS laxity may even be intentional: say, to reap revenue-oriented benefits by cheating on resource allocations and/or excessive statistical-sharing of system resources (e.g., VM cycles, number of servers). Our goal is to assess how well the internal mechanisms of S are geared to offer a required level of service to the applications. We use computational models of S to determine the optimal feasible resource schedules and verify how close is the actual system behavior to a model-computed \u27gold-standard\u27. Our QoS assessment methods allow comparing different service vendors (possibly with different business policies) in terms of canonical properties: such as elasticity, linearity, isolation, and fairness (analogical to a comparative rating of restaurants). Case studies of cloud-based distributed applications are described to illustrate our QoS assessment methods. Specific systems studied in the thesis are: i) replicated data services where the servers may be hosted on multiple data-centers for fault-tolerance and performance reasons; and ii) content delivery networks to geographically distributed clients where the content data caches may reside on different data-centers. The methods studied in the thesis are useful in various contexts of QoS management and self-configurations in large-scale cloud-based distributed systems that are inherently complex due to size, diversity, and environment dynamicity

Telecommunications Networks

This book guides readers through the basics of rapidly emerging networks to more advanced concepts and future expectations of Telecommunications Networks. It identifies and examines the most pressing research issues in Telecommunications and it contains chapters written by leading researchers, academics and industry professionals. Telecommunications Networks - Current Status and Future Trends covers surveys of recent publications that investigate key areas of interest such as: IMS, eTOM, 3G/4G, optimization problems, modeling, simulation, quality of service, etc. This book, that is suitable for both PhD and master students, is organized into six sections: New Generation Networks, Quality of Services, Sensor Networks, Telecommunications, Traffic Engineering and Routing

WizHaul: On the Centralization Degree of Cloud RAN Next Generation Fronthaul

Cloud Radio Access Network (C-RAN) will become a main building block for 5G. However, the stringent requirements of current fronthaul solutions hinder its large-scale deployment. In order to introduce C-RAN widely in 5G, the next generation fronthaul \agsrev{interface} (NGFI) will be based on a cost-efficient packet-based network with higher path diversity. In addition, NGFI shall support a flexible functional split of the RAN to adapt the amount of centralization to the capabilities of the transport network. In this paper we question the ability of standard techniques to route NGFI traffic while maximizing the centralization degree---the goal of C-RAN. We propose two solutions jointly addressing both challenges: (i) a nearly-optimal backtracking scheme, and (ii) a low-complex greedy approach. We first validate the feasibility of our approach in an experimental proof-of-concept, and then evaluate both algorithms via simulations in large-scale (real and synthetic) topologies. Our results show that state-of-the-art techniques fail at maximizing the centralization degree and that the achievable C-RAN centralization highly depends on the underlying topology structure.This work has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 671598 (5G-Crosshaul project) and 761536 (5G-Transformer project)