Effective distributed scheduling of parallel workloads
Proceedings of the 1996 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Matchmaking: Distributed Resource Management for High Throughput Computing
HPDC '98 Proceedings of the 7th IEEE International Symposium on High Performance Distributed Computing
Ivy: a read/write peer-to-peer file system
ACM SIGOPS Operating Systems Review - OSDI '02: Proceedings of the 5th symposium on Operating systems design and implementation
Don't settle for less than the best: use optimization to make decisions
HOTOS'07 Proceedings of the 11th USENIX workshop on Hot topics in operating systems
Pig latin: a not-so-foreign language for data processing
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Grid resource allocation: allocation mechanisms and utilisation patterns
AusGrid '08 Proceedings of the sixth Australasian workshop on Grid computing and e-research - Volume 82
Cutting the electric bill for internet-scale systems
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
Quincy: fair scheduling for distributed computing clusters
Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles
Rhizoma: a runtime for self-deploying, self-managing overlays
Proceedings of the 10th ACM/IFIP/USENIX International Conference on Middleware
Brief announcement: modelling MapReduce for optimal execution in the cloud
Proceedings of the 29th ACM SIGACT-SIGOPS symposium on Principles of distributed computing
On availability of intermediate data in cloud computations
HotOS'09 Proceedings of the 12th conference on Hot topics in operating systems
See spot run: using spot instances for mapreduce workflows
HotCloud'10 Proceedings of the 2nd USENIX conference on Hot topics in cloud computing
Orchestrating the deployment of computations in the cloud with conductor
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
On modelling and prediction of total CPU usage for applications in mapreduce environments
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
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Cloud computing enables customers to access virtually unlimited resources on demand and without any fixed upfront cost. However, the commoditization of computing resources imposes new challenges in how to manage them: customers of cloud services are no longer restricted to the resources they own, but instead choose from a variety of different services offered by different providers, and the impact of these choices on price and overall performance is not always clear. Furthermore, having to take into account new cloud products and services, the cost of recovering from faults, or price fluctuations due to spot markets makes the picture even more unclear. This position paper highlights a series of challenges that must be overcome in order to allow customers to better lever-age cloud resources. We also make the case for a system called Conductor that automatically manages resources in cloud computing to meet user-specifiable optimization goals, such as minimizing monetary cost or completion time. Finally, we discuss some of the challenges we will face in building such a system.