accommodating bursts in distributed stream processing systems n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Accommodating Bursts in Distributed Stream Processing Systems PowerPoint Presentation
Download Presentation
Accommodating Bursts in Distributed Stream Processing Systems

play fullscreen
1 / 32

Accommodating Bursts in Distributed Stream Processing Systems

139 Views Download Presentation
Download Presentation

Accommodating Bursts in Distributed Stream Processing Systems

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Accommodating Bursts in Distributed Stream Processing Systems • Yannis Drougas, ESRI • drougas@esri.com • Vana Kalogeraki, AUEB • vana@aueb.gr

  2. Stream Processing Applications • Large class of emerging applications in which data streams must be processed online • Example applications include: • Traffic Monitoring • Sensor network data processing • Stock Exchange data filtering • Surveillance • Network monitoring

  3. High-volume, continuous input streams Processed result streams Distributed Stream Processing Systems On-line processing functions / continuous query operators implemented on each node: Clustering Correlation Filtering Aggregation ...

  4. Stream Processing Applications Characteristics • Data is produced continuously, in large volumes and at high rates • Data has to be processed in a timely manner, e.g. within a deadline • Application input rates fluctuate notably and abruptly E.g.: • increasing traffic volume due to a DoS attack • large number of messages generated when there is a time-critical event in the sensor network field • It is important that no data is lost and that time-critical events are processed and delivered in a timely manner Clustering Join Filtering

  5. Previous Work • The majority of previous work [Tatbul@VLDB 2003, VLDB2007, Amini@ICDCS 2006, Gu@ICDCS 2005] has focused on optimizing a given utility function • Some solutions employ data admission • Others consider the optimal placement of tasks on nodes • The case where load patterns can be predicted has also been studied [Borealis @ ICDE 2005] • QoS management [Wei@RTSS2006] to predict query workload • Common ways to deal with an overload: Admission control or resource reservation techniques [Abeni & Buttazzo @ RTSS’04] can result in under-utilization • QoS degradation is a reactive technique and occurs only after burst has occurred [Chen @ IPDPS’05]

  6. Our Problem • We focus on the problem of addressing bursts of input data rate • Can we accommodate a burst without having to reserve resources a priori? • How should we react to a burst? • Benefits: • Lost data units due to bursts are minimized • No QoS degradation or data admission • No under-utilization, dynamic reservation used • Challenges: • Highly dynamic / unpredictable environment • Multiple limiting resource types • Plan must be applied in real-time for the burst

  7. Roadmap • Motivation and Background • System Architecture • Burst Handling Mechanism • Feasible Region • Pareto Points • Online burst management through rate assignment • Experimental Evaluation • Conclusion

  8. System Architecture Application layer: Execution of stream processing applications. Overlay network consisted of processing nodes. Built over a DHT (currently, Pastry). The physical (IP) network.

  9. Synergy Node Architecture • Replica Placement places components • Load Balancing and Load Prediction detect hot-spots • Migration Engine alleviates hot-spots • Application Composition and QoS Projection instantiate applications • Execution Scheduler provides real-time execution • Node Monitor measures processor and bandwidth • Discovery locates streams and components • Routing transfers streaming data

  10. Distributed Stream Processing Application • A stream processing application is represented as a graph. The application is executed collaboratively by peers of the system that invoke the appropriate services. • Multiple applications can run simultaneously sharing the system resources. • A service can be instantiated on more than one nodes. • A service instantiation on a node is a component.

  11. System Model • Each component ciis characterized by its resource requirements: • The selectivity of the component ciis given by: • Each node n is characterized by the availability of its resources:

  12. Optimization Problem • Capacity Constraints: • Flow Conservation Constraints: • We need to come up with a plan that satisfies the above constraints and minimizes the likelihood of missing data due to bursts. The plan should determine the rate assignment of each component rci

  13. Feasible Region • Assume we have Q applications • The state of the system at any given time can be described by a point in the Q-dimensional space • The feasible region is the set of all points (application input rates combinations) that nodes in the given distributed stream processing system can accommodate without any data unit being dropped. • The form of linear constraints suggest that in the general case of Q applications, the feasible region is a convex polytope.

  14. Feasible Region - Example

  15. Dominance & Pareto Points • A point p1 dominates a point p2 when for each application q, • If the current system state is p2, one can apply the rate allocations calculated for p1 • A Pareto point is not dominated by any other point in the feasible region • Pareto points represent optimal solutions: There is no point that is “better” than a pareto point

  16. Burst Handling Initial resource requirements Additional resource requirements due to single burst • If the input rate of application q increases by , the input rate of a component of q will increase by • In order for a stream processing system to be able to sustain such an increase, the following must hold for each node:

  17. Optimization Objective • Assuming simultaneous bursts, the capacity constraints for each node are: • We assume each application has equal probability for a burst to appear. • To minimize the amount of dropped data, we wish to maximize all : • If the current input rates are represented by p, we need to configure the system for:

  18. Optimization Objective - Example • To be able to accommodate the burst, the new point must be inside the feasible region

  19. Our System: BARRE • Our objective is to accommodate bursts in real-time through rate reconfiguration • This problem is difficult to solve online • Instead, our solution: • Offline: Pre-calculates a small number of component rate assignment plans during an offline phase (using the Feasible Region & Pareto Point machinery that we described) • Online: • Monitors application incoming rates and resource availability during runtime • When bursts occur, component rates are re-assigned, based on the pre-calculated plans

  20. Feasible Region Determination • We don’t compute the entire feasible region • Instead, we find a set of Pareto points and use them to construct the appropriate component rate assignment on the event of a burst • For those Pareto points we pre-calculate their optimal rate assignment • These component rate assignments will be used to construct the appropriate component rate allocation on the event of a burst.

  21. Identifying Index Points - Example • Step 1: Find the maximum possible rate for each application • Solve a max-flow problem, with the rates of all but one applications set to 0.

  22. Identifying Index Points - Example • Step 2: Find the mid-point of the resulting plane and see how far it can go • Solve max-flow by also constraining direction

  23. Identifying Index Points - Example • Step 3: For each of the resulting sides, find each mid-point and repeat previous step • The upper left and lower right points are now dominated by the newly found index points

  24. Identifying Index Points - Example • Step 4: This is the resulting approximation to the feasible region • The mid-points of the sides cannot improve without breaking the capacity constraints

  25. Online Phase • Upon the onset of a burst, we use the pre-calculated Pareto Points to determine the appropriate component input rate allocation plan • We may need to adjust the entire plan as a result of the burst p1 p2 p3 p’ p’ = p +δ p2 dominates p, however, when there is a burst we may have to change plans to p3 p

  26. Experimental Setup • Implementation over our Synergy middleware • Used FreePastry for service discovery and collection of statistics • 10 unique services in the system, 5 services on a single node • Each application included 4 to 6 services • Each result is an average of 5 runs • Comparison with: • A burst-unaware method • Static Reservation of 20% of node resources • Simple Dynamic Adaptation, from previous work • A combination of the above

  27. Index Point Pre-Calculation Index Point (Pareto Point) Database calculation time, this is why index points need to be pre-calculated.

  28. BARRE Operation BARRE avoids missing data units during the burst and minimizes lost data units. Also, resulting plan is “safer”, so it prevents future drops

  29. Missed Data Units BARRE results in fewer data units dropped. It can sustain up to about 80% (vs. about 20%) application rate increase without dropping any data unit.

  30. Data Units Delivered On Time BARRE achieves a high percentage of data unit delivery.

  31. Conclusions • We proposed BARRE, a dynamic reservation scheme to address bursts in a dynamic stream processing system. • Reservation is based on application needs and readjusted according to system dynamics • BARRE utilizes multiple nodes for each processing component, splitting the input rate among them whenever needed

  32. Accommodating Bursts in Distributed Stream Processing Systems • Yannis Drougas, ESRI • drougas@esri.com • Vana Kalogeraki, AUEB • vana@aueb.gr