Connecting the Dots: Using Runtime Paths for Macro Analysis

Transcription

1 Connecting the Dots: Using Runtime Paths for Macro Analysis Mike Chen

2 Motivation Divide and conquer, layering, and replication are fundamental design principles e.g. Internet systems, P2P systems, and sensor networks Execution context is dispersed throughout the system => difficult to monitor and debug Lots of existing low-level tools that help with debugging individual components, but not a collection of them Much of the system is in how the components are put together Observation: a widening gap between the systems we are building and the tools we have

3 Current Approach Apache Sensor Peer Apache Sensor Peer Java Sensor Peer Bean Java Sensor Peer Bean Java Sensor Peer Bean Database Sensor Peer Database Sensor Peer

4 Current Approach Micro analysis tools, like code-level debuggers (e.g. gdb) and application logs, offers details of each individual component Scenario A user reports request A1 failed You try the same request, A2, but it works fine What to do next? gdb Apache A1 X Java = 1 X Java = 3 Y Bean = 2 Y Bean = 1 Database A2 Apache X Java = 3 Y Bean = 2 Database

5 Macro Analysis Macro analysis exploits non-local context to improve reliability and performance Performance examples: Scout, ILP, Magpie Statistical view is essential for large, complex systems Analogy: micro analysis allows you to understand the details of individual honeybee; macro analysis is needed to understand how the bees interact to keep the beehive functioning

6 Observation Systems have a single system-wide execution paths associated with each request E.g. request/response, one-way messages Scout, SEDA, Ninja use paths to specify how to service requests Our philosophy Use only dynamic, observed behavior Application-independent techniques

7 Our Approach Use runtime paths to connect the dots! dynamically captures the interactions and dependency between components look across many requests to get the overall system behavior more robust to noise Components are only partially known ( gray boxes ) paths Java Bean Apache Database Java Bean Apache Database Java Bean path

8 Our Approach Applicable to a wide range of systems. Apache Sensor Peer A Apache Sensor Peer B paths Java Sensor Peer C Bean Java Sensor Peer D Bean Java Sensor Peer E Bean Java Database Sensor Peer F Bean Java Database Sensor Peer G Bean

9 Open Challenges in Systems Today 1. Deducing system structure manual approach is error-prone static analysis doesn t consider resources 2. Detecting application-level failures often don t exhibit lower-level symptoms 3. Diagnosing failures failures may manifest far from the actual faults multi-component faults Goal: reduce time to detection, recovery, diagnosis, and repair

10 Talk Outline Motivation Model and architecture Applying macro analysis Future directions

11 Runtime Paths Instrument code to dynamically trace requests through a system at the component level record call path + the runtime properties e.g. components, latency, success/failure, and resources used to service each request Use statistical analysis detect and diagnose problems e.g. data mining, machine learning, etc. Runtime analysis tells you how the system is actually being used, not how it may be used Complements existing micro analysis tools

12 Architecture Tracer Tags each request with a unique ID, and carries it with the request throughout the system Report observations (component name + resource + performance properties) for each component Aggregator + Repository Reconstructs paths and stores them Declarative Query Engine Supports statistical queries on paths Data mining and machine learning routines Visualization request Tracer A Tracer C Tracer E Tracer B Tracer D Tracer F Reusable Analysis Framework Aggregator Developers/ Operators Visualization Query Engine Path Repository

13 Request Tracing Challenge: maintaining an ID with each request throughout the system Tracing is platform-specific but can be applicationgeneric and reusable across applications 2 classes of techniques Intra-thread tracing Use per-thread context to store request ID (e.g. ThreadLocal in Java) ID is preserved if the same thread is used to service the request Inter-thread tracing For extensible protocols like HTTP, inject new headers that will be preserved (e.g. REQ_ID: xx) Modify RPC to pass request ID under the cover Piggyback onto messages

14 Talk Outline Motivation Model and architecture Applying macro analysis Inferring system structure Detection application-level failures Diagnosing failures Future directions

15 Inferring System Structure Key idea: paths directly capture application structure 2 requests

16 Indirect Coupling of Requests Key idea: paths associate requests with internal state Trace requests from web server to database Parse client-side SQL queries to get sharing of db tables Straightforward to extend to more fine-grained state (e.g. rows) Request types Database tables

17 Failure Detection and Diagnose Detecting application-level failures Key idea: paths change under failures => detect failures via path changes. Diagnosing failures Key idea: bad paths touch root cause(s). Find common features.

18 Future Directions Key idea: violation of macro invariants are signs of buggy implementation or intrusion Message paths in P2P and sensor networks a general mechanism to provide visibility into the collective behavior of multiple nodes micro or static approaches by themselves don t work well in dynamic, distributed settings e.g. algorithms have upper bounds on the # of hops Although hop count violation can be detected locally, paths help identify nodes that route messages incorrectly e.g. detecting nodes that are slow or corrupt msgs

19 Conclusion Macro analysis fills the need when monitoring and debugging systems where local context is of insufficient use Runtime path-based approach dynamically traces request paths and statistically infer macro properties A shared analysis framework that is reusable across many systems Simplifies the construction of effective tools for other systems and the integration with recovery techniques like RR Paper includes a commercial example from Tellme! (thanks to Anthony Accardi and Mark Verber)

20 Backup Slides

21 Backup Slides

22 Current Approach Micro analysis tools, like code-level debuggers (e.g. gdb) and application logs, offers details of each individual component gdb Java Apache X = 1 Y Bean = 2 Java Apache X = 2 Y Bean = 4 X Java = 1 Y Bean = 2 X Java = 5 Y Bean = 2 X Java = 3 Y Bean = 2 Java Database X = 2 Y Bean = 3 Java Database X = 7 Y Bean = 1

23 Related Work Commercial request tracing systems Announced in 2002, a few months after Pinpoint was developed PerformaSure and AppAssure focus on performance problems. IntegriTea captures and playback failure conditions. Focus on individual requests rather than overall behavior, and on recreating the failure condition. Extensive work in event/alarm correlation, mostly in the context of network management (i.e. IP) Don t directly capture relationship between events Rely on human knowledge or use machine learning to suppress alarms. Distributed debuggers PDT, P2D2, TotalView, PRISM, pdbx Aggregates views from multiple components, but do not capture relationship and interaction between components Comparative debuggers: Wizard, GUARD Dependency models Most are statically generated and are likely to be inconsistent. Brown et al. takes an active, black box approach but is invasive. Candea et al. dynamically trace failures propagation.

24 1. Detecting Failures using Anomaly Detection Key idea: paths change under failures => detect failures via path changes Anomalies Unusual paths Changes in distribution Changes in latency/response time Examples: Error paths are shorter. User behavior changes under failures Retries a few times then give up Implement as long running queries (i.e. diff) Challenges: detecting application-level failures comparing sets of paths

25 2. Root-cause Analysis Key idea: all bad paths touch root cause, find common features Challenge: a small set of known bad paths and a large set of maybes Ideally want to correlate and rank all combinations of feature sets E.g. association rules mining May get false alarms because the root cause may not be one of the features Automatic generation of dynamic functional and state dependency graphs Helps developers and operators understand inter-component dependency and inter-request dependency Input to recovery algorithms that use dependency graphs

26 3. Verifying Macro Invariants Key idea: violations of high-level invariants are signs of intrusion or bugs Example: Peer auditing Problem: A small number of faulty or malicious nodes can bring down the system Corruption should be statistically visible in your behavior look for nodes that delay or corrupt messages or route messages incorrectly Apply root-cause analysis to locate the misbehaving peers Some distributed auditing is necessary Example: P2P implementation verification Problem: are messages delivered as specified by the algorithms? Detect extra hops, loops, and verify that the paths are correct Can implement as a query: select length from paths where (length > log2(n))

27 4. Detecting Single Point of Failure Key idea: paths converge on a single-point of failure Useful for finding out what to replicate to improve availability P2P example: Many P2P systems rely on overlay networks, which typically are networks built on top of the IP infrastructure. It s common for several overlay links to fail together if they depend on a shared physical IP link that failed Implement as a query: intersect edge.ip_links from paths Sensor Peer A Sensor Peer C Sensor Peer D Sensor Peer F Sensor Peer B Sensor Peer E Sensor Peer G

28 5. Monitoring of Sensor Networks An emerging area with primitive tools Key idea: use paths to reconstruct topology and membership Example: Membership select unique node from paths Network topology for directed information dissemination Challenge: limited bandwidth Can record a (random) subset of the nodes for each path, then statistically reconstruct the paths

29 Macro Analysis Look across many requests to get the overall system behavior more robust to noise Macro Analysis Request 1 Request 2 Request 3 Request 4 Component A X X X Component B X Component C X X

30 Properties of Network Systems Web services, P2P systems, and sensor networks can have tens of thousands of nodes each running many application components Continuous adaptation provides high availability, but also makes it difficult to reproduce and debug errors Constant evolution of software and hardware

31 Motivation Difficult to understand and debug network systems e.g. Clustered Internet systems, P2P systems and sensor networks Composed of many components Systems are becoming larger, more dynamic, and more distributed Workload is unpredictable and impractical to simulate Unit testing is necessary but insufficient. Components break when used together under real workload Don t have tools that capture the interactions between components and the overall behavior Existing debugging tools and application-level logs only do micro analysis

32 Macro vs Micro Analysis Resolution Overhead Macro Analysis Component. Complements micro analysis tools. Low. Can use it in actual deployment. Micro Analysis Line or variable High. Typically not used in deployment other than application logs.

33 What s a dynamic path? A dynamic path is the (control flow + runtime properties) of a request Think of it as a stack trace across process/machine boundaries with runtime properties Dynamically constructed by tracing requests through a system Runtime properties Resources (e.g. host, version) Performance properties (e.g. latency) Arguments (e.g. URL, args, SQL statement) Success/failure request A C E B D F Path A D E RequestID: 1 Seq Num: 1 Name: A Host: xx Latency: 10ms Success: true..

34 Related Work Micro debugging tools RootCause provides extensible logging of method calls and arguments. Diduce look for inconsistencies in variable usage. Complements macro analysis tools. Languages for monitoring InfoSpect looks for inconsistencies in system state using a logic language Network flow-based monitoring RTFM and Cisco NetFlow classify and record network flows Statistical and data mining languages S, DMQL, WebML

35 Visualization Techniques Tainted paths: mark all flows that have a certain property (e.g. failed or slow) with a distinct color and overlay it on the graph Detecting performance bottlenecks: look for replicated nodes that have different colors Detecting anomaly: look for missing edges and unknown paths

36 Pinpoint Framework Requests #1 #2 #3 External F/D Components A B C Communications Layer (Tracing & Internal F/D) Logs 1, success 2, fail 3,... 1,A 1,C 2,B.. Statistical Analysis Detected Faults

37 Experimental Setup Demo app: J2EE Pet Store e-commerce site w/~30 components Load generator replay trace of browsing Approx. TPCW WIPSo load (~50% ordering) Fault injection parameters Trigger faults based on combinations of used components Inject exceptions, infinite loops, null calls 55 tests with single-components faults and interaction faults 5-min runs of a single client (J2EE server limitation)

38 Application Observations # of components used in a dynamic web page request: median 14, min 6, max 23 large number of tightly coupled components that are always used together # of tightly coupled components cart productitemlist InventoryEJB SignOnEJB ClientControllerEJB ShoppingCartEJB 100% 80% 60% 40% 20% 0% # of components Cumulative % of total requests

39 Metrics Predicted Faults (P) Correctly Identified Faults (C) Actual Faults (A) Precision: C/P Recall: C/A Accuracy: whether all actual faults are correctly identified (recall == 100%) boolean measure

40 4 Analysis Techniques Pinpoint: clusters of components that statistically correlate with failures Detection: components where Java exceptions were detected union across all failed requests similar to what an event monitoring system outputs Intersection: intersection of components used in failed requests Union: union of all components used in failed requests

41 Results 100% 80% 60% 40% 20% average accuracy average precision 76% 77% 50% 33% 83% 15% 100% 11% Average Accuracy 100% 80% 60% 40% 20% Pinpoint Detection Intersection Union 0% Pinpoint Detection Intersection Union 0% # of Interacting Components Pinpoint has high accuracy with relatively high precision

42 Pinpoint Prototype Limitations Assumptions client requests provide good coverage over components and combinations requests are autonomous (don t corrupt state and cause later requests to fail) Currently can t detect the following: faults that only degrade performance faults due to pathological inputs Single-node only

43 Current Status Simple graph visualization

44 Proposed Research 3 classes of large network systems Clustered Internet systems Tiered architecture, high bandwidth, many replicas Peer-to-peer (P2P) systems, including sensor networks Widely distributed nodes, dynamic membership Sensor networks Limited storage, processing, and bandwidth.

45 P2P Systems: Tracing Trace messages by piggybacking the current node name to the messages Tracing overhead Assume 32-bit per node name and a very conservative log2(n) hops for each msg and Data overhead is 40% for a 1500-byte message in a node system

46 P2P Systems: Implementation Verification Current debugging techniques: lots of printf() s on each node and manually correlate the paths taken by messages How do you know the messages are delivered as specified by the algorithms? Use message paths to check for routing invariants detect extra hops, loops, and verify that the paths are correct Can implement as a query: select length from paths where (length > log2(n))