1. E VALUATION OF F AIRNESS IN ICN X. De Foy, JC. Zuniga, B. Balazinski InterDigital (first.last@interdigital.com)

2. Motivation As part of the ongoing discussion on the Evaluation Methodology draft, should we measure how “fair” is the usage of network resources in ICN? Is this a real issue? If so, what should we measure?

3. Potential Fairness Issues in ICN Since ICNs share resources… – Network resources: bandwidth, in-network storage, NRS, Topology Managers, etc. – Individual nodes resources: CPU, RAM, NIC, Storage, etc. … a network entity can trick the system to get a larger amount of resources – Receivers, publishers, even routers … an ICN system design may have a bias – E.g. a too large a bias towards popular content may starve unpopular content objects

4. Fairness Measures in ICN Fairness between end users – Throughputs between several competing flows on bottleneck links are compared for interest shaping evaluation in [4] Fairness between classes of content (e.g. popular vs. unpopular) – Number of replicas depending on popularity in [2] – In these scenarios it is assumed that all users will get similar service for a same class of content: Response time depending on popularity in [1] Ratio of throughput gain with/without cache depending on popularity in [3] … any other examples?

5. Fairness in a Generic Context Strategies/Criteria: between fairness and efficiency – Basic strategy is max-min fairness – the goal is to maximize the minimum allocation among all agents – Proportional fairness – compromise between efficiency and a minimal level of fairness between users – Other efficiency-fairness tradeoffs (e.g. α-fairness used in [9] ranging from utilitarian α=0, proportional α=1, max-min α→∞) Examples of Qualities used to Evaluate Fairness – Share Guarantee: every arriving agent gets at least an equal split of the resources if it wants it – Strategy Proof-ness: no agent may misreport its demand and be better off regardless of demands from other agents – Envy Freeness: an agent would never prefer the allocation of another agent – Pareto Efficiency: changing allocation to make one agent better off would result in making another agent worse

6. Fairness in Relevant Domains Sharing of resources in data centers between tenants – Run jobs with resource usage vector (CPU, Memory, etc.), measure number of jobs per tenant – Example: Dominant Resource Fairness [5] Middle box resource allocation – Multiple resources within a single node, more dynamic allocation (queue scheduling) [6] Sharing of Resources in P2P Systems – Some peers can use strategies to optimize their gain and minimize their own resource usage [7] Scheduling in Wireless Ad Hoc networks – Single resource (bandwidth/time slots) allocated at each node in a distributed manner [8]

7. Evaluation of Fairness in ICN 1/2 ICN should be fair to end users/receivers. Should it be fair to other entities like publishers and individual content objects as well? Some resources may be “traded” for others, especially Bandwidth vs. Storage Multiple content sources/replicas can lead to a distribution of bottlenecks (e.g. network links, routers storage, routers CPU, etc.)

8. Evaluation of Fairness in ICN 2/2 Is there an acceptable set of reference setups… – n r receivers, n p publishers, n o content objects – A network topology with caches/routers/links and placement of receivers/publishers Some of the topologies used in evaluations are bus, ring, caching tree, topologies with a single congested link, mesh with certain probability of inter-connections. – A usage pattern … and associated fairness criteria? – General criteria, qualities – Specific agents (receivers, etc.)

9. Questions to the RG Is there a common, agreeable definition of fairness for ICN? – Is there a content-oriented aspect of fairness? Do ICN particularities actually make a difference on how to evaluate fairness (multiple sources, caching vs. bandwidth equivalence)? Should we include a section on this aspect in the Evaluation Methodology draft?

10. References [1] Carofiglio et al. “Bandwidth and Storage Sharing Performance in Information Centric Networking”, 2010, LinkLink [2] Tortelli et al. “A Fairness Analysis of Content Centric Networks”, 2012, LinkLink [3] Saucez et al. “Congestion control and in-network caching” (ICNRG IETF84 presentation), 2012, LinkLink [4] Wang et al. “An Improved Hop-by-hop Interest Shaper for Congestion Control in Named Data Networking”, 2013, LinkLink [5] Ghodsi et al. “Dominant Resource Fairness: Fair Allocation of Heterogeneous Resources in Datacenters”, 2010, LinkLink [6] Ghodsi et al. “Multi-resource fair queueing for packet processing”, 2012, LinkLink [7] Piatek et al. “Do incentives build robustness in BitTorrent?”, 2007, LinkLink [8] Tassiulas & Sarkar “Maxmin Fair Scheduling in Wireless Ad Hoc Networks”, 2005, LinkLink [10] Bertsimas et al., “A Characterization of the Efficiency-Fairness Tradeoff”, 2010, LinkLink