A Novel Scheduling Index Rule Proposal for QoE Maximization in Wireless Networks

Taboada, Ianire; Liberal, Fidel

doi:10.1155/2014/647157

Cited by 2 publications

(4 citation statements)

References 24 publications

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“…Therefore, resulting approaches have put the focus particularly in the reward (in terms of users' satisfaction) to be achieved by efficiently using the available network resources (c.f. [75]).…”

Section: Energy-aware Network and Service Controlmentioning

confidence: 99%

State of the Art and Research Challenges in the Area of Autonomous Control for a Reliable Internet of Services

Mei

Berg²,

Ганчев

et al. 2018

Lecture Notes in Computer Science

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The explosive growth of the Internet has fundamentally changed the global society. The emergence of concepts like service-oriented architecture (SOA), Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS), Network as a Service (NaaS) and Cloud Computing in general has catalyzed the migration from the information-oriented Internet into an Internet of Services (IoS). This has opened up virtually unbounded possibilities for the creation of new and innovative services that facilitate business processes and improve the quality of life. However, this also calls for new approaches to ensuring quality and reliability of these services. The goal of this book chapter is to first analyze the state-of-the-art in the area of autonomous control for a reliable IoS and then to identify the main research challenges within it. A general background and high-level description of the current state of knowledge is presented. Then, for each of the three subareas, namely the autonomous management and real-time control, methods and tools for monitoring and service prediction, and smart pricing and competition in multi-domain systems, a brief general introduction and background are presented, and a list of key research challenges is formulated. Keywords: Internet of Services (IoS) Á Autonomous control Autonomous management Á Service monitoring Á Service prediction Smart pricing 3 Autonomous Management and Real-Time Control On a fundamental level, the working group WG1, associated with this research sub-area, was primarily concerned with the management and control of networks, services, applications, and compositions of services or applications. Of particular interest were management and control techniques that span multiple levels, e.g., the network and service level.

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Section: Energy-aware Network and Service Controlmentioning

confidence: 99%

State of the Art and Research Challenges in the Area of Autonomous Control for a Reliable Internet of Services

Mei

Berg²,

Ганчев

et al. 2018

Lecture Notes in Computer Science

Self Cite

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“…Subjective quality maximization when channel capacity is constant [44], 2. Subjective quality maximization in channels with time-varying capacity [43], 3. Mean delay minimization for general size distributions in channels with timevarying capacity [41,45].…”

Section: Incorporating Energy and Cost To Opportunistic Qoe-aware Schmentioning

confidence: 99%

“…For this aim, our research is focused on the Markovian Decision Processes (MDP) framework and on Gittins and Whittle methods [41,[43][44][45] to obtain scheduling index rule solutions. In this way, first of all, the above scheduling problems are modeled in the framework of MDPs.…”

Section: Incorporating Energy and Cost To Opportunistic Qoe-aware Schmentioning

confidence: 99%

“…In such a way, the Whittle index represents the rate between marginal reward and marginal work, where marginal reward (work) is the difference between the expected total reward earned (work done) by serving and not serving at an initial state and then employing a certain optimal policy. As a first step towards ACROSS targeted multi-criteria optimization, it is worth mentioning the utility-based MDP employed in [43,44] for QoE maximization. This function depended on delay only but we plan to extend it to a generic problem aimed at maximizing a multivariate objective function.…”

Section: Incorporating Energy and Cost To Opportunistic Qoe-aware Schmentioning

confidence: 99%

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Energy vs. QoX Network- and Cloud Services Management

Blanco

Liberal

Lassila

et al. 2018

Lecture Notes in Computer Science

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Network Performance (NP)-and more recently Quality of Service/Experience/anything (QoS/QoE/QoX)-based network management techniques focus on the maximization of associated Key Performance Indicators (KPIs). Such mechanisms are usually constrained by certain thresholds of other system design parameters. e.g., typically, cost. When applied to the current competitive heterogeneous Cloud Services scenario, this approach may have become obsolete due to its static nature. In fact, energy awareness and the capability of modern technologies to deliver multimedia content at different possible combinations of quality (and prize) demand a complex optimization framework. It is therefore necessary to define more flexible paradigms that make it possible to consider cost, energy and even other currently unforeseen design parameters not as simple constraints, but as tunable variables that play a role in the adaptation mechanisms. In this chapter we will briefly introduce most commonly used frameworks for multi-criteria optimization and evaluate them in different Energy vs. QoX sample scenarios. Finally, the current status of related network management tools will be described, so as to identify possible application areas.

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A Novel Scheduling Index Rule Proposal for QoE Maximization in Wireless Networks

Cited by 2 publications

References 24 publications

State of the Art and Research Challenges in the Area of Autonomous Control for a Reliable Internet of Services

State of the Art and Research Challenges in the Area of Autonomous Control for a Reliable Internet of Services

Energy vs. QoX Network- and Cloud Services Management

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