Name
Abstract | ||
|---|---|---|
A large class of applications require real-time processing of continuous stream data resulting in the development of data stream management systems (DSMS). Since many of these applications are distributed, distributed DSMSs are starting to receive attention. In this paper, we focus on an important issue in distributed DSMS operation, namely load distribution to minimize end-to-end latency. We identify the often conflicting requirements of load distribution, and propose a "potential-driven" load distribution approach to mimic the movements of objects in the physical world. Our approach also takes into account heterogeneous machines, different network conditions, and resource constraints. We present experimental results that investigate our algorithms from various aspects, and show that they outperform existing techniques in terms of end-to-end latency. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1379272.1379277 | SSPS |
Keywords | Field | DocType |
data stream management system,different network condition,conflicting requirement,continuous stream data,dsms operation,load distribution,data stream processing,load distribution approach,end-to-end latency,potential-driven load distribution,account heterogeneous machine,data streams,real time processing,load balancing,load balance,stream processing,distributed system,distributed systems | Data stream processing,Data stream mining,Computer science,Load balancing (computing),Data stream,Latency (engineering),Stream data,Real-time computing,Management system,Distributed computing,Network conditions | Conference |
Citations | PageRank | References |
1 | 0.35 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weihan Wang | 1 | 19 | 6.08 |
| Mohamed Sharaf | 2 | 659 | 43.07 |
| Shimin Guo | 3 | 51 | 6.95 |
| M. Tamer Özsu | 4 | 4504 | 582.63 |