Metrics and KPIs Sample Clauses

Metrics and KPIs. ‌ The objectives of this experiment are to quantify how the overhead due to Protocol Control Information (PCI) - the network headers - can be reduced in the network when contraint link resources are encountered. Routing scalability will be investigated by dumping routing tables to storage at certain intervals and assessing the number of routing entries. These functionalities are already supported by the available RINA implementations. Applications such as netperf or rina-tgen can measure peak, instantaneous and average bit rates and the total time to complete a file transfer. By using tcpdump and RINA tools we can measure gaps in packet transfers to assess failure recovery times and the packet loss during recovery. By logging all traffic using tcpdump/wireshark, we can analyse the total bandwidth consumed on each network link to assess network resource usage efficiency. These KPIs are summarised in Table 6. KPI Metric Current state of the art ARCFIRE Objective PCI overhead bits RObust Header Compression (ROHC) comparable to ROHC Routing scalability entries in a routing table logarithmic in theory, superlinear in practice logarithmic in practice Failure in- terruption time ms sub 50 ms restoration sub 20 ms measured in the testbed Application goodput b/s application-dependent application-dependent Link resource utilisation % scenario-dependent scenario-dependent Packet loss during failures number of SDUs 0 0 Table 6: KPIs for experiment 2 These KPIs will be measured under different circumstances and traffic loads. The traffic will be generated by gradually scaling up the number of end user devices, the number of network nodes (routers), the number of deployed services and the total number of user connections. These results will show how each of these KPIs scales, and, when taken as a whole provide insights in overall network scalability.
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Metrics and KPIs. ‌ DRAFT The KPIs for experiment 3 are given in Table 9. KPI Metric Current state of the art ARCFIRE Objective Complexity of configuration Number of protocols and parameters that need to be configured Depends on specific scenario (will be cal- culated for experiment configuration) Simpler configura- tion for equivalent scenarios Protocol header overhead in data transfer PDUs Number of protocol header bytes 20 bits per MPLS label, at least 2 or 3 labels used in typical configurations Less protocol header overhead for equiva- lent configurations Network state: forwarding table size Size of forwarding tables as a function of number of flows and nodes Depends on specific scenario (will be cal- culated for experiment configuration) Equal or less network state for equivalent configurations QoS: Delays Delays measured per QoS class Hard to guarantee differential delay between QoS classes at high loads [13] Statistical bounds on delay per QoS class, even when offered load is equal to 100% QoS: Packet Loss Losses measured per QoS class Hard to guarantee differential packet loss between QoS classes at high loads [13] Statistical bounds on packet loss per QoS class, even when offered load is equal to 100% QoS: Capacity Utilisation of N-flows (Mbps) Hard guarantees using traffic-engineered LSPs Capacity isolation between QoS classes, even when offered load is equal to 100% Table 9: KPIs for guaranteed QoS levels experiment
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Metrics and KPIs. ‌ The objectives of the experiment are to quantify i) the degradation in the level of service perceived by applications using flows when renumbering takes place in a DIF and ii) the overhead and performance of the renumbering procedure. As later explained in section 4.3.5, experiments will take into account realistic scenarios but also more extreme scenarios to understand the limitations of dynamic renumbering (e.g. in which a network is continuously being renumbered). That is why the seamless renumbering KPI targets in table 12 refer to the realistic use cases. KPI Metric Current state of the art ARCFIRE Objective Seamless renumbering: Latency Increased latency while the network is being renum- bered Application flows break when renumbering Less than 5% in realistic use cases Seamless renumbering: Goodput Decreased goodput while the network is being renumbered Application flows break when renumbering Less than 5% in realistic use cases Seamless renumbering: packet loss Packet loss due to renum- bering events Application flows break when renumbering Zero in realistic use cases Renumbering overhead Average extra entries in IPCP forwarding tables due to renumbering events, as a function of renum- bering period and DIF size Renumbering cannot be fully automated and seamless Understanding the trade- offs between renumbering period, DIF size and for- warding table size Renumbering speed Time to complete a renum- bering event (until old address is deprecated) Renumbering cannot be fully automated and seamless Understanding the perfor- xxxxx of the renumbering procedure, and how it may impact seamless renumber- ing metrics Table 12: KPIs for renumbering experiments • IRATI RINA implementation. It will be used to implement multiple RINA-enabled systems and run a variety of DIFs according to the experiment configuration depicted in section 4.3.5. Policies already present in the IRATI repository will be used to carry out standard DIF procedures. A few new namespace management policies will be developed to trigger the renumbering events according to different strategies (periodically, whole DIF at once, only mobile hosts). • rina-echo-time. The rina-echo-time application will be used to measure the latency and packet loss perceived by a flow user while renumbering takes place. • rina-tgen. The rina-tgen application will be used to measure the goodput perceived by a flow user while renumbering takes place. •
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Metrics and KPIs. ‌ The objectives of this experiment are to evaluate the behaviour of several built-in RINA features facilitating OMEC use cases, by verifying their correct operation and measuring the performance of its current implementation. In particular we are interested in the DIF Allocator work to locate a destination application and configure DIFs to access it if needed; as well as in the impact of handovers in terms of performance (packet loss, delay, goodput) in a RINA over WiFi scenario (Table 15). KPI Metric Current state of the art ARCFIRE Objective DIF Allocator performance Extra delay incurred in flow allocation due to application discovery and joining relevant DIF This capability is not supported by the current Internet protocol suite Understanding the perfor- xxxxx of the DIF Allocator under load, identifying trade-offs in its design Impact of handover on packet loss Increased packet loss due to handover (WiFi AP to WiFi AP) To be measured on testbed Equivalent or less than in the IP case (understand the tradeoffs of RINA over WiFi) Impact of handover on delay Increased delay due to handover (WiFi AP to WiFi AP) To be measured on testbed Equivalent or less than in the IP case (understand the tradeoffs of RINA over WiFi) Impact of handover on goodput Loss of application good- put (Mbps) due to handover effects To be measured on testbed for TCP and UDP Equivalent or less than in the IP case (understand the tradeoffs of RINA over WiFi) Table 15: KPIs for OMEC experiments
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Related to Metrics and KPIs

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