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Supporting On-demand Elasticity in Distributed Graph Processing.

, , , , and . IC2E, page 12-21. IEEE Computer Society, (2016)

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Supporting On-demand Elasticity in Distributed Graph Processing., , , , and . IC2E, page 12-21. IEEE Computer Society, (2016)Idempotent distributed counters using a forgetful bloom filter., , , and . Clust. Comput., 19 (2): 879-892 (2016)Handling Uncertainty: Pareto-Efficient BoT Scheduling on Hybrid Clouds., , , , and . ICPP, page 419-428. IEEE Computer Society, (2013)Cross-Layer Scheduling in Cloud Systems., , and . IC2E, page 236-245. IEEE Computer Society, (2015)Idempotent Distributed Counters Using a Forgetful Bloom Filter., , , and . ICCAC, page 113-124. IEEE Computer Society, (2015)Optimizing Scientific Workflows in the Cloud: A Montage Example., , , , and . UCC, page 517-522. IEEE Computer Society, (2014)An Experimental Comparison of Partitioning Strategies in Distributed Graph Processing., , , and . Proc. VLDB Endow., 10 (5): 493-504 (2017)Zorro: zero-cost reactive failure recovery in distributed graph processing., , , and . SoCC, page 195-208. ACM, (2015)RAMP: reliability-aware elastic instance provisioning for profit maximization., , and . J. Supercomput., 71 (12): 4529-4554 (2015)Running Data-Intensive Scientific Workflows in the Cloud., , , , and . PDCAT, page 180-185. IEEE Computer Society, (2014)