Dynamic file migration in distributed computer systems
Communications of the ACM
Competitive distributed file allocation
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Genetic algorithm based approach for file allocation on distributed systems
Computers and Operations Research - Special issue on genetic algorithms
Optimal allocation of resources in distributed information networks
ACM Transactions on Database Systems (TODS) - Special issue: papers from the international conference on very large data bases: September 22–24, 1975, Framingham, MA
Comparative Models of the File Assignment Problem
ACM Computing Surveys (CSUR)
Optimal program and data locations in computer networks
Communications of the ACM
Some theorems to aid in solving the file allocation problem
Communications of the ACM
An approximation algorithm for a file-allocation problem in a hierarchical distributed system
SIGMOD '80 Proceedings of the 1980 ACM SIGMOD international conference on Management of data
The Quest for Efficient Boolean Satisfiability Solvers
CAV '02 Proceedings of the 14th International Conference on Computer Aided Verification
Optimal File Allocation in a Multiple Computer System
IEEE Transactions on Computers
Allocation of copies of a file in an information network
AFIPS '72 (Spring) Proceedings of the May 16-18, 1972, spring joint computer conference
Early observations on the performance of Windows Azure
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
An automated approach to cloud storage service selection
Proceedings of the 2nd international workshop on Scientific cloud computing
Assessing the Value of Cloudbursting: A Case Study of Satellite Image Processing on Windows Azure
ESCIENCE '11 Proceedings of the 2011 IEEE Seventh International Conference on eScience
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Cloud computing offers many possibilities for prospective users, there are however many different storage and compute services to choose from between all the cloud providers and their multiple datacenters. In this paper we focus on the problem of selecting the best storage services according to the application's requirements and the user's priorities. In previous work we described a capability based matching process that filters out any service that does not meet the requirements specified by the user. In this paper we introduce a mathematical model that takes this output lists of compatible storage services and constructs an integer linear programming problem. This ILP problem takes into account storage and compute cost as well as performance characteristics like latency, bandwidth, and job turnaround time, a solution to the problem yields an optimal assignment of datasets to storage services and of application runs to compute services. We show that with modern ILP solvers a reasonably sized problem can be solved in one second, even with an order of magnitude increase in cloud providers, number of datacenters, or storage services the problem instances can be solved under a minute. We finish our paper with two use cases, BLAST and MODIS. For MODIS our recommended data allocation leverages both cloud and local resources, it incurs in half the cost of a pure cloud solution and the job turnaround time is 52% faster compared to a pure local solution.