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Social-aware Utility-based Radio Resource Management. Timotheos Kastrinogiannis School of Electrical and Computer Engineering National Technical University of Athens (NTUA). At a glance…. Content Management Networks User’s social behavior;
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Social-aware Utility-based Radio Resource Management Timotheos Kastrinogiannis School of Electrical and Computer Engineering National Technical University of Athens (NTUA) THALES - SoComNets
At a glance… • Content Management Networks • User’s social behavior; • Content attributes, content management goals and new users’ satisfaction criteria. • Physical Networks (i.e., underlay networking paradigms) • Network-centric resource allocation and QoS provisioning mechanisms; • Network Utility Maximization (NUM) theory. * Top-down Cross Layering….
Network Centric RRM (The Traditional View) The Need….. The Evolution…..
A Mathematical Theory of Network Architectures… • The first unifying view and systematic approach • Network: Generalized NUM • Layering architecture: Decomposition scheme • Layers: Decomposed subproblems • Interfaces: Functions of primal or dual variables (KellyMaulloTan98):
Designing a Utility…. • A utility function is based on three key components: • Indicators correlated to data transmission (π.χ. BER). • Indicators correlated to QoS prerequisites fulfillment • Indicators correlated to preemption (e.g. real time aplications)
Fundamental Utility Functions Non-real-time Services Real-time Services Concave Sigmoidal Convex
User’s Behavioral Attributes in Content Management(Case I) • Motivation: “Minimizing Node Churn in Peer-to-Peer Streaming”[1] • In P2P networks suffer from highly transient peers who join and leave the system (churn) at their own will. • In P2P streaming servicescontent managementshould be performed in short time scales. C. Vassilakis, I. Stavrakakis, “Minimizing Node Churn in Peer-to-Peer Streaming Computer Communications”, Computer Communications Journal, Vol.33, Iss. 14, pp. 1598-1614, Sept. 2010.
Thread of Analysis Implicit Calculation Short-term Goodput QoE Utility(video) Churn Probability Can we improve Node’s Churn Probability via RRM ? Explicit Calculation
Corresponding Optimization QoS-Aware RRM QoE-aware (Content Network-aware) RRM Goal: Minimize Node’s Churn Probability
Networking Results Correlation with overlay network’s benefits…..
Beginning with a Simple Question…. “By what criteria do we evaluate a particular network architecture?”Shenker 1995 VS “The internet was designed to meet the needs of users, and so any evaluative criteria must reduce to the following question: how happy does this architecture make the users?”.
The Evolutionary Path of QoS • Initially, • correlation of services or service classes(application layer) with several network metrics (e.g. threshold-based values for latency, jitter, packet loss, e.t.c) • design of dynamic resource allocation algorithms that aim at maintaining these metrics at acceptable levels. • Then, • utility functions define a formal mathematical vehicle towards expressing and quantifying user’s degree of satisfaction with respect to their multi-criteria service performance. • the goal of network design can be restated as being, quite simply, to maximize the sum of utilities, leading to the establishment of Network Utility Maximization (NUM) “a concrete theoretic framework”
Quality of Experience (QoE) (Case II) • Finally, • Quality of Experience (QoE), defined as “a measure of the overall acceptability of an application or service, as perceived subjectively by the end-user”ITU How can we correlate QoS and QoE? • “E2E QoS” (enabled via monitoring and proper network reacting mechanisms); • QoE is enabled via the mapping of users’ opinions for the quality of a service (in a normalized way e.g. MOS)) to specific networking metrics (leading to proactive approaches); • Dynamic adaptation of a network’s operation and performance, in line with users’ dynamic requests (e.g. adaptive video resolution on demand).
In Reality … Experience is Subjective & Context-dependent …. Personal Experiences Mood Cultural Background Background Noise Socioeconomic Status Multimedia Content Content Management Social Distance Social Behavior
A Practical Idea • We envision the role of QoE as the vehicle that interconnects users/humans, applications and QoS-aware resource management mechanisms. • We propose a QoE framework that allows users to dynamically and asynchronously express their (dis)satisfaction with respect to the instantaneous experience of their service quality at the overall network QoS-aware resource allocation process.
QoE-aware Resource Allocation Users’ 16, 17, 18 Achieved Goodput Cells’ Overall Goodput
Social-aware Utility-based RRM (Case III) • Motivation: “Scalable Distribution of Content Updates over a Mobile Social Networks” • Service: “Dynamic Content Distribution” • Co-operation: subscribers to this service share their updates in an opportunistic fashion • Problem: “how the service provider can allocate its bandwidth optimally to make the content at users as “fresh” as possible.”
System’s Behavior (Under Optimal Content Management) μ : total injection rate allocated among different users Social Users’ Normalized Priority Indicator: Users are indexed according to their contact rates, in decreasing order. Observations: For low values of μ the “most social” user accumulates all the injected rate, thus acting as a global hub of all incoming information; Under certain conditions, it is actually optimal to allocate no bandwidth to the most social users in the system. Idea: Social-aware Utility Functions that express A. User’s QoS Prerequisites B. User’s Priority on Available Radio Resources with Respect to his Role/Importance in the Mechanism Of the Underlay Content Management Network. Non-Social Users’ Normalized Priority Indicator:
