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Avoiding class warfare: managing continuous queries with differentiated classes of service


Data stream management systems (DSMSs) offer the most effective solution for processing data streams by efficiently executing continuous queries (CQs) over the incoming data. CQs inherently have different levels of criticality and hence different levels of expected quality of service (QoS) and quality of data (QoD). Adhering to such expected QoS/QoD metrics is even more important in cases of multi-tenant data stream management services. In this work, we propose DILoS, a framework that, through priority-based scheduling and load shedding, supports differentiated QoS and QoD for multiple classes of CQs. Unlike existing works that consider scheduling and load shedding separately, DILoS is a novel unified framework that exploits the synergy between scheduling and load shedding. We also propose ALoMa, a general, adaptive load manager that DILoS is built upon. By its design, ALoMa performs better than the state-of-the-art alternatives in three dimensions: (1) it automatically tunes the headroom factor, (2) it honors the delay target, (3) it is applicable to complex query networks with shared operators. We implemented DILoS and ALoMa in our real DSMS prototype system (AQSIOS) and evaluate their performance for a variety of real and synthetic workloads. Our experimental evaluation of ALoMa verified its clear superiority over the state-of-the-art approaches. Our experimental evaluation of the DILoS framework showed that it (a) allows the scheduler and load shedder to consistently honor CQs’ priorities, (b) significantly increases system capacity utilization by exploiting batch processing, and (c) enables operator sharing among query classes of different priorities while avoiding priority inversion, i.e., a lower-priority class never blocks a higher-priority one.

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  1. In fact, the CTRL paper does not even use real operators: It used only delay operators to simulate an operator with a certain processing cost and selectivity. The Aurora paper uses only a simulation for its experiment, not a real DSMS.

  2. Note that because STREAM (inherited by AQSIOS) does not support everything in the CQL syntax, we had to split the query into several virtual queries in the actual script.

  3. Dataset LBL-PKT-4/lbl-pkt-n.tcp is publicly available at the following URL:

  4. We have observed in some experiments (not shown in this paper), that the reduction in data loss under DILoS can reach up to 100 %, i.e., completely eliminating the need for shedding.

  5. Since the three classes have the same amount of data, total data loss of the three classes is calculated by \(\frac{\sum _{1\le i \le 3}[\mathrm{dataloss}_i])}{3}\)

  6. Note that in this case, the estimated headroom factor of class 1 is not adjusted and still remains at the initial value because the load manager does not have the necessary signals to decrease it.


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Our thanks to the anonymous reviewers for their insightful comments and Mark Silvis and Eric Gratta for their help with copyediting. This work was supported in part by NSF awards IIS-0534531, IIS-0746696, OIA-1028162, an Andrew Mellon Predoctoral Fellowship and EMC/ Greenplum.

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Correspondence to Thao N. Pham.

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This article enhances and extends preliminary work [37] that was presented in the SMDB’11 Workshop.

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Pham, T.N., Chrysanthis, P.K. & Labrinidis, A. Avoiding class warfare: managing continuous queries with differentiated classes of service. The VLDB Journal 25, 197–221 (2016).

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  • Data stream management system
  • Continuous query
  • Multi-tenant
  • Load shedding
  • Scheduling