Mobile agent fault tolerance in autonomous decentralized database systems

Author(s):  
W. Dake ◽  
C.P. Leguizamo ◽  
K. Mori

Author(s):  
Chenggang Wu ◽  
Shaohui Liu ◽  
Bo Wang ◽  
Zhongzhi Shi ◽  
Hua Gu


10.28945/3033 ◽  
2006 ◽  
Author(s):  
G. Adesola Aderounmu ◽  
Bosede Oyatokun ◽  
Matthew Adigun

This paper presents a comparative analysis of Remote Method Invocation (RMI) and Mobile Agent (MA) paradigm used to implement the information storage and retrieval system in a distributed computing environment. Simulation program was developed to measure the performance of MA and RMI using object oriented programming language (the following parameters: search time, fault tolerance and invocation cost. We used search time, fault tolerance and invocation cost as performance parameters in this research work. Experimental results showed that Mobile Agent paradigm offers a superior performance compared to RMI paradigm, offers fast computational speed; procure lower invocation cost by making local invocations instead of remote invocations over the network, thereby reducing network bandwidth. Finally MA has a better fault tolerance than the RMI. With a probability of failure pr = 0.1, mobile agent degrades gracefully.



2019 ◽  
Vol 5 (1) ◽  
pp. 65-79
Author(s):  
Yunhong Ji ◽  
Yunpeng Chai ◽  
Xuan Zhou ◽  
Lipeng Ren ◽  
Yajie Qin

AbstractIntra-query fault tolerance has increasingly been a concern for online analytical processing, as more and more enterprises migrate data analytical systems from mainframes to commodity computers. Most massive parallel processing (MPP) databases do not support intra-query fault tolerance. They may suffer from prolonged query latency when running on unreliable commodity clusters. While SQL-on-Hadoop systems can utilize the fault tolerance support of low-level frameworks, such as MapReduce and Spark, their cost-effectiveness is not always acceptable. In this paper, we propose a smart intra-query fault tolerance (SIFT) mechanism for MPP databases. SIFT achieves fault tolerance by performing checkpointing, i.e., materializing intermediate results of selected operators. Different from existing approaches, SIFT aims at promoting query success rate within a given time. To achieve its goal, it needs to: (1) minimize query rerunning time after encountering failures and (2) introduce as less checkpointing overhead as possible. To evaluate SIFT in real-world MPP database systems, we implemented it in Greenplum. The experimental results indicate that it can improve success rate of query processing effectively, especially when working with unreliable hardware.



Author(s):  
Flávio M. Assis Silva ◽  
Radu Popescu-Zeletin
Keyword(s):  




Author(s):  
Rajwinder Singh ◽  
A. K. Sarje ◽  
Navdeep Kaur ◽  
Ramandeep Kaur


Sign in / Sign up

Export Citation Format

Share Document