Pham, Thao
(2016)
EXPLOITING THE SYNERGY BETWEEN SCHEDULING AND LOAD SHEDDING TO FACILITATE DIFFERENTIATED LEVELS OF SERVICE FOR CONTINUOUS QUERIES.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
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 dissertation, we propose DILoS, a framework that supports differentiated QoS and QoD for multiple classes of CQs by tightly integrating priority-based scheduling and load shedding.
Unlike existing works that consider scheduling and load shedding separately, DILoS is a novel unified framework that exploits the synergy between them. For the realization of DILoS, we propose ALoMa and SEaMLeSS, two general, adaptive load managers. Our load managers can also be used standalone and outperform the state-of-the-art in three dimensions: (1)they automatically tune the headroom factor, (2) they honor the delay target, and (3) they are applicable to complex query networks with shared operators.
We implemented DILoS, ALoMa and SEaMLeSS in our real DSMS prototype system (AQSIOS) and systematically evaluate their performance using real and synthetic workloads.Our experimental evaluation of ALoMa and SEaMLeSS verified their advantages over the state-of-the-art approaches. Our evaluation of DILoS 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.
To further support differentiated QoS and QoD for CQs in distributed DSMSs, we propose ARMaDILoS, a conceptual framework for large scale adaptive resource management
using DILoS. A fundamental component in ARMaDILoS is CQ migration. For this reason, we propose and implement UniMiCo, a protocol to migrate CQs without interrupting the
execution of the queries. Our experiments showed that UniMiCo produced correct outputs and did not introduce any hiccup in the response time of the queries.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
13 June 2016 |
| Date Type: |
Publication |
| Defense Date: |
8 April 2016 |
| Approval Date: |
13 June 2016 |
| Submission Date: |
15 April 2016 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
132 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Dietrich School of Arts and Sciences > Computer Science |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Data stream, continuous query, scheduling, load shedding |
| Date Deposited: |
13 Jun 2016 14:46 |
| Last Modified: |
15 Nov 2016 14:32 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/27356 |
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