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CCGRID '01 Proceedings of the 1st International Symposium on Cluster Computing and the Grid
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ACM '82 Proceedings of the ACM '82 conference
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Obfuscation of executable code to improve resistance to static disassembly
Proceedings of the 10th ACM conference on Computer and communications security
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
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GRID '04 Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing
Trust but verify: monitoring remotely executing programs for progress and correctness
Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming
Java, peer-to-peer, and accountability: building blocks for distributed cycle sharing
VM'04 Proceedings of the 3rd conference on Virtual Machine Research And Technology Symposium - Volume 3
Intrusion detection using sequences of system calls
Journal of Computer Security
Cluster computing on the fly: P2P scheduling of idle cycles in the internet
IPTPS'04 Proceedings of the Third international conference on Peer-to-Peer Systems
CycleMeter: detecting fraudulent peers in internet cycle sharing
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
Monitoring remotely executing shared memory programs in software DSMs
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Secure remote execution of sequential computations
ICICS'09 Proceedings of the 11th international conference on Information and Communications Security
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The increased popularity of grid systems and cycle sharing across organizations requires scalable systems that provide facilities to locate resources, to be fair in the use of those resources, and to monitor jobs executing on remote systems. This paper presents a novel and lightweight approach to monitoring the progress and correctness of a parallel computation on a remote, and potentially fraudulent, host system. We describe a monitoring system that uses a sequence of program counter values to monitor program progress, and compiler techniques that automatically generate the monitoring code. This approach improves on earlier work by omitting the need to duplicate computation, which both simplifies and reduces the overhead of monitoring. Our approach allows dynamic and accountable cycle-sharing across the Internet. Experimental results show that the overhead of our system is negligible and our monitoring approach is scalable.