DocumentCode :
643488
Title :
Detecting Unstable Conjunctive Locality-Aware Predicates in Large-Scale Systems
Author :
Min Shen ; Kshemkalyani, Ajay D. ; Khokhar, Ashfaq
Author_Institution :
Dept. of Comput. Sci., Univ. of Illinois at Chicago, Chicago, IL, USA
fYear :
2013
fDate :
27-30 June 2013
Firstpage :
127
Lastpage :
134
Abstract :
Recently, the concept of locality-aware predicates (LAP) has been proposed. A LAP models a predicate within a local region of the whole network in a large-scale locality driven system, such as WSNs and modular robotics. In such systems, the cost of doing a global predicate detection is high, besides which, a predicate over the state of a local region better captures properties of local interactions. Thus, LAP detection becomes a relevant and interesting problem. In this paper, we explore the problem of detecting unstable conjunctive LAP, and develop a scale-free algorithm by running an interval-based detection algorithm using a vector clock built on-the-fly for processes in the local region. More importantly, we develop the encoded vector clock (EVC) technique. EVC makes detecting unstable conjunctive LAP more practical in large-scale systems by reducing the storage cost.
Keywords :
cost reduction; graph theory; mobile computing; storage management; EVC; LAP models; conjunctive locality-aware predicate unstability detection; encoded vector clock technique; global predicate detection; interval-based detection algorithm; large-scale locality driven system; scale-free algorithm; storage cost reduction; undirected graph; Clocks; Complexity theory; Detection algorithms; Large-scale systems; Synchronization; Vectors; Wireless sensor networks; distributed system; encoding; large-scale network; locality-aware; predicate detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Parallel and Distributed Computing (ISPDC), 2013 IEEE 12th International Symposium on
Conference_Location :
Bucharest
Print_ISBN :
978-1-4799-2967-2
Type :
conf
DOI :
10.1109/ISPDC.2013.25
Filename :
6663573
Link To Document :
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