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Integrated Computer-Aided Engineering
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Hedera: dynamic flow scheduling for data center networks
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ShadowNet: a platform for rapid and safe network evolution
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USENIXATC'10 Proceedings of the 2010 USENIX conference on USENIX annual technical conference
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EMSOFT '10 Proceedings of the tenth ACM international conference on Embedded software
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Server operational cost optimization for cloud computing service providers over a time horizon
Hot-ICE'11 Proceedings of the 11th USENIX conference on Hot topics in management of internet, cloud, and enterprise networks and services
Black-box and gray-box strategies for virtual machine migration
NSDI'07 Proceedings of the 4th USENIX conference on Networked systems design & implementation
Workload-aware database monitoring and consolidation
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Recent advances in declarative networking
PADL'12 Proceedings of the 14th international conference on Practical Aspects of Declarative Languages
Cologne: a declarative distributed constraint optimization platform
Proceedings of the VLDB Endowment
A declarative recommender system for cloud infrastructure services selection
GECON'12 Proceedings of the 9th international conference on Economics of Grids, Clouds, Systems, and Services
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Guide-copy: fast and silent migration of virtual machine for datacenters
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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As cloud computing becomes widely deployed, one of the challenges faced involves the ability to orchestrate a highly complex set of subsystems (compute, storage, network resources) that span large geographic areas serving diverse clients. To ease this process, we present COPE (Cloud Orchestration Policy Engine), a distributed platform that allows cloud providers to perform declarative automated cloud resource orchestration. In COPE, cloud providers specify system-wide constraints and goals using COPElog, a declarative policy language geared towards specifying distributed constraint optimizations. COPE takes policy specifications and cloud system states as input and then optimizes compute, storage and network resource allocations within the cloud such that provider operational objectives and customer SLAs can be better met. We describe our proposed integration with a cloud orchestration platform, and present initial evaluation results that demonstrate the viability of COPE using production traces from a large hosting company in the US. We further discuss an orchestration scenario that involves geographically distributed data centers, and conclude with an ongoing status of our work.