Dynamic tree embeddings in butterflies and hypercubes
SIAM Journal on Computing
SAC '98 Proceedings of the 1998 ACM symposium on Applied Computing
Efficient dynamic embeddings of binary trees into hypercubes
Journal of Algorithms
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Dryad: distributed data-parallel programs from sequential building blocks
Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems 2007
Load shedding and distributed resource control of stream processing networks
Performance Evaluation
Optimistic Synchronization of Parallel Simulations in Cloud Computing Environments
CLOUD '09 Proceedings of the 2009 IEEE International Conference on Cloud Computing
Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Proceedings of the 2nd ACM Symposium on Cloud Computing
Extreme Data-Intensive Scientific Computing
Computing in Science and Engineering
Parallel data processing with MapReduce: a survey
ACM SIGMOD Record
A tutorial on cross-layer optimization in wireless networks
IEEE Journal on Selected Areas in Communications
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With the rapidly growing challenges of big data analytics, the need for efficient and distributed algorithms to optimize cloud computing performances is unprecedentedly high. In this paper, we consider how to optimally deploy a cloud computing programming framework (e.g., MapReduce and Dryad) over a given underlying network hardware infrastructure to maximize the end-to-end computation rate and minimize the overall computation and communication costs. The main contributions in this paper are three-fold: i) we develop a new network flow model with a generalized flowconservation law to enable a systematic design of distributed algorithms for computation rate utility maximization problems (CRUM) in cloud computing; ii) based on the network flow model, we reveal key separable properties of the dual functions of Problem CRUM, which further lead to a distributed algorithm design; and iii) we offer important networking insights and meaningful economic interpretations for the proposed algorithm and point out their connections to and distinctions from distributed algorithms design in traditional data communications networks. This paper serves as an important first step towards the development of a theoretical foundation for distributed computation analytics in cloud computing.