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Fast Low-Cost Failure Recovery for Real-Time Communication in Multi-hop Networks Kang G. Shin Real-Time Computing Laboratory The University of Michigan.

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Presentation on theme: "Fast Low-Cost Failure Recovery for Real-Time Communication in Multi-hop Networks Kang G. Shin Real-Time Computing Laboratory The University of Michigan."— Presentation transcript:

1 Fast Low-Cost Failure Recovery for Real-Time Communication in Multi-hop Networks Kang G. Shin Real-Time Computing Laboratory The University of Michigan (This is joint work with S. Han)

2 Dependability in ISN Integrated service networks (ISNs): – Real-time and non-real-time applications will coexist in IP-based ISNs Emerging Internet-based real-time applications: –Life-/safety-critical : Emergency calls, remote medical services, military applications, remote control of plants, … –Financially-critical : Business multimedia conferences, real-time e-commerce, on-line auctions, … –Economic/social consequences of failures Motivation: –Conventional fault-tolerance techniques are inadequate to real- time communication in future Internet.

3 Research Objective Objective: Develop an efficient method for adding fault-tolerance to existing or emerging real-time communication protocols with –Guaranteed dependability –Low overhead –Good scalability –Inter-operability Environments: –Large-scale (IP-based) multi-hop networks –Real-time unicast/multicast communication –Dynamic connection setups/teardowns

4 Real-Time Communication End-to-end QoS-guarantee: –QoS: message delay, delay jitter, throughput, … –Semi-real-time communication : RTP, XTP, IP multicast,... Two approaches: –Connection-oriented, per-connection QoS control (e.g.,RSVP)  –Connection-less, per-class QoS control (e.g., Diff Serv) Typical procedure of connection-oriented approach: 1. Client’s input traffic specification & QoS requirement 2. Off-line route selection & admission test 3. Off-line resource reservation along the selected route 4. Run-time traffic policing/shaping & packet scheduling.

5 Target Failure Model Network failure model: –Transient failures (e.g., message omissions) –Persistent failures (e.g., component crashes) Real-time communication perspective: –Negligible bit-error rate with optical technology –Congestion-induced loss avoidance by resource reservation –Greater impact of a single component failure Reliability of data network paths: –Less than 25 days of MTTF –More than 60% of failures last 10 minutes ~ several hours Much lower reliability than that of PSTN paths

6 Persistent Failure Recovery Physical-layer techniques: –Protection switching –Self-healing approach Advantages: –Hit-less or fast recovery –Transparency Need of upper-layer techniques: –Inability of dealing with IP-router failures –Heterogeneity of underlying mediums –Inability of supporting application-specific fault-tolerance requirements (e.g., in multicast services)

7 Upper-Layer Techniques Failure masking approach: –For applications that can’t not tolerate any message loss, e.g., multi-copy transmission with error coding Failure detection & recovery approach: –For applications that can tolerate some message losses during failure recovery, e.g., on-the-fly channel rerouting Shortcomings of on-the-fly rerouting: –No guarantees on successful recovery –Long recovery delay –High control traffic overhead Our goal: –Fast and guaranteed failure recovery with low cost

8 Our Approach Ideas: –Advance resource reservation for failure recovery (called “ spare resources ”) –Advance (off-line) recovery-route selection –A dependable real-time connection = primary + backup channels backup paths should be disjoint with its primary path. Issues: –Negotiation on dependability QoS parameters –Backup path selection and spare resource allocation –Channel failure detection –Run-time failure recovery –Resource reconfiguration after recovery

9 Outline of Remaining Talk Dependability QoS parameters Backup channel establishment Failure detection Run-time failure recovery Other issues Summary and conclusions

10 Dependability QoS Parameters Probability of fast and guaranteed recovery, Pr –Markov modeling Time-varying Approximation by combinatorial reliability modeling –Negotiation between network and applications Service-disruption time bound, G –Not negotiable Implication: –The probability that a dependable connection will suffer from a disruption longer than G is at most Pr. Reference: [IEEE TOC’98]

11 Setting Up Backup Channels Overhead of backup channel: –No bandwidth/buffer consumption before activation Spare resource reservation: –Can be utilized by best-effort traffic in failure-free situations, but not by real-time traffic. Reduction of network capacity to accommodate more real- time connections. Techniques for overhead reduction: –Spare-resource sharing (backup multiplexing) –Adaptive resource control in failure-free situations

12 Deterministic Resource Sharing Failure hypothesis: –The type and max number of failures are predetermined (e.g., single link failure model). Basic procedure: – Calculate the exact amount of spare resources needed to handle all possible failures under the assumed failure model. Resource aggregation Route optimization: –Selecting primary and backup routes so as to minimize spare resources

13 Limitations of Deterministic Sharing Restricted failure hypothesis: –Same fault-tolerance capability to all connections Limited applicability: –Applicable when resources are exchangeable among connection e.g., when bandwidth is the only resource under consideration Centralized optimization: –High computational complexity –Adequate to static flow networks Unsuitable for large-scale, heterogeneous, dynamic networks.

14 Probabilistic Backup Multiplexing Failure hypothesis: – Each network component fails with a certain probability. Basic procedure: – If any two backup channels are not likely to be activated simultaneously, they are not accounted for in each other’s channel admission test. Channel admission by overbooking –Applicable to any real-time communication scheme –Distributed hop-by-hop spare resource calculation Per-connection fault-tolerance control: –Use a different multiplexing degree for each connection in determining if two backups will be multiplexed or not.

15 Performance Evaluation Simulation networks: –Random topologies, regular topologies (average degree  4) Efficiency of backup multiplexing: –The overhead of backup channel is 110~150% of primary channels without multiplexing vs. 30~50% with multiplexing, for single component failure tolerance. –Means that 20~35% network capacity are reserved for backups, or dedicated to best-effort services in a failure-free situation. Reference: [SIGCOMM’97]

16 Backup Route Selection Premise: –Separation of backup route selection from backup multiplexing mechanism, i.e., spare resources are computed from given routing results. –Use existing routing methods for primary channels. Goal: –Minimize the amount of spare resources while guaranteeing the fault-tolerance level required (NP-complete) Two-stage approach: 1. Quick initial routing with greedy heuristics 2. Periodic/triggered route reconfiguration

17 Two-Stage Routing Greedy routing: –Shortest-path routing with some link-cost metrics, for example, f1 = 1 ( minimum hop routing ) f2 = total bandwidth reserved at the link f3 = incremental spare bandwidth if the backup is routed over the link Route reconfiguration: –Addition/departure of connections makes already-routed backups inefficient in terms of spare resource requirements –Backup reconfiguration won’t cause actual service disruptions. Reference: [RTSS’97]

18 Overview of Failure Recovery Primary Channel Setup Failure Reporting & Channel Switching Backup Channel Setup Normal Operation Failure Detection

19 Origins of network failures: –Maintenance –Power outage –Fiber cut –Hardware errors –Software errors –Congestion –Malicious attacks Failure-diagnosis vs. fail-over

20 What Failures to Detect and How? Channel failure: –When a real-time channel experiences persistent message losses, it is said to suffer from “channel failure”. –Or, if the rate of correct message delivery within a certain time interval < a channel-specific threshold Physical-/Data link-layer support: –Hop-by-hop packet filtering Behavior-based channel failure detection: –Neighbor detection method –End-to-end detection method

21 Two Detection Methods Neighbor method: –Periodic exchange of node heartbeats between neighbor nodes – Neighbor nodes declare the failures of channels on a component, if they do not receive heartbeats from the component for a certain period. End-to-end method: –Channel source node injects channel heartbeats between data messages. – Channel destination node detects a channel failure by monitoring message reception.

22 Experimental Evaluation Strength & limitation of end-to-end detection –Perfect failure detection coverage –Long detection latency –Unable to locate the source of failure Strength & limitation of neighbor detection –Short detection latency –Potentially imperfect detection coverage Experimental goal –Evaluate the detection efficiency in terms of both failure detection coverage and latency by fault-injection experiments.

23 Failure Detection Latency fault injection latency (neighbor) latency (end-to-end) real-time message reception heartbeat reception heartbeat miss

24 Experimental Setup Hardware platform: –Three network nodes are connected by optical fiber point-to- point links. Software: –Real-time channel protocol suite developed in RTCL, U of M. Workload: –Two-hop real-time channels and background traffic Fault-injection tool: –DOCTOR

25 Testbed Configuration NI NPAP NPAP NPAP HMON Node 1 Node 2 Node 3 VME bus Data Network Host Ethernet

26 Fault Injection DOCTOR, an integrated fault-injection tool set: –Software-implemented fault injector –Hardware-aided data monitor (HMON) –Fault-selection tool Specifications of injected faults: –Transient faults into NP of Node 2 at OS task scheduler, clock service, network adapter driver, and real-time channel protocol. –Memory faults, CPU faults, communication faults. Reference: [IPDS’95]

27 Detection Scheme Implementation Heartbeat generation: –By a periodic task Heartbeat protocol: –Simple exchange of ‘I am alive’ messages Heartbeat transmission path: –In end-to-end detection, heartbeats are transmitted as real-time messages of the corresponding channel. –In neighbor detection, heartbeats can be (option 1) transmitted as best-effort messages, (option 2) transmitted as real-time messages.

28 Experimental Results Impacts of implementation : –Transmitting node heartbeats as real-time messages greatly enhances the detection coverage of the neighbor method. Nearly 100% detection coverage. Workload dependency: –The performance of detection schemes is insensitive to workloads (i.e., traffic load or # of channels) and is not prone to false alarms. Reference: [FTCS’97] [IEEE TPDS’99]

29 Handling of Detected Failures 1. Failure reporting: –Implicit reporting (e.g., by link-state maintenance) –Explicit reporting –What, where, and how (path) to report 2. Channel switching: –Backup activation –Traffic redirection –On-the-fly rerouting 3. Resource reconfiguration: –Closure or repair of faulty channels –Backup re-establishment or migration

30 Failure Reporting & Channel Switching Time-bounded/robust failure handling –Two-way signaling –Special-type real-time channels for time-critical control message transmission (e.g., failure reports and backup activation message) --- out-of-band signaling Source Destination Primary channel Backup channel Failure report Activation msg

31 Resource Reconfiguration Closure of failed channels: –Explicit or implicit closure (‘soft state’) Dependability maintenance: –Re-establishing failed or activated backups –Allocating more spare resources or re-routing some backups Dependability degradation: (in case of resource shortage) –Option 1: tearing down backups of some connections –Option 2: gracefully degrading dependability QoS –Option 3: degrading performance QoS of backups Back to normal: –When failed components are repaired

32 Other Issues Extension to multicast services: –Source-based tree case, shared tree case Support for elastic QoS control schemes: –Network-triggered QoS renegotiation (e.g., ABR) –Application-triggered QoS renegotiation (e.g., RCBR) On-going research: –Supporting hierarchical network architectures –Supporting differentiated services –Multi-layer fault-tolerance –Detection/tolerance of malicious attack

33 Conclusion Salient features of the proposed scheme: –Unified method for dependable unicast/multicast QoS communication –Per-connection (or per-class) dependability QoS control –Fast (time-bounded) failure recovery –Robust/distributed failure handling –Low fault-tolerance overhead Design philosophy: –Pre-planned failure recovery –Client-specific dependability support –Independence of the underlying technology Reference: [IEEE Network ‘98]


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