Access the full text.
Sign up today, get DeepDyve free for 14 days.
HV Jagadish, J Gehrke, A Labrinidis, Y Papakonstantinou, JM Patel, R Ramakrishnan, C Shahabi (2014)
Big data and its technical challengesCACM, 57
B Gedik, K-L Wu, P Yu, L Liu (2007)
GrubJoin: An Adaptive, Multi-Way
B Gedik, K-L Wu, PS Yu, L Liu (2007)
CPU load shedding for binary stream joinsKnowl. Inf. Syst., 13
JH Chang, H-CM Kum (2009)
Frequency-based load shedding over a data stream of tuplesInf. Sci., 179
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.
The VLDB Journal – Springer Journals
Published: Apr 1, 2016
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.