Minimum-Cost Cloud Storage Service Across Multiple Cloud Providers - 2017


Many cloud service suppliers (CSPs) provide knowledge storage services with datacenters distributed worldwide. These datacenters provide completely different get/put latencies and unit prices for resource utilization and reservation. Thus, when selecting completely different CSPs' datacenters, cloud customers of globally distributed applications (e.g., online social networks) face 2 challenges: one) how to allocate information to worldwide datacenters to satisfy application service level objective (SLO) requirements, including both information retrieval latency and availability and2) how to allocate knowledge and reserve resources in datacenters belonging to completely different CSPs to attenuate the payment value. To handle these challenges, we tend to first model the value minimization drawback below SLO constraints using the integer programming. Thanks to its NP-hardness, we tend to then introduce our heuristic solution, together with a dominant-price-based mostly information allocation algorithm and an optimal resource reservation algorithm. We have a tendency to any propose three enhancement strategies to reduce the payment cost and service latency: 1) coefficient-based knowledge reallocation; two) multicast-based knowledge transferring; and 3) request redirection-primarily based congestion control. We tend to finally introduce an infrastructure to enable the conduction of the algorithms. Our trace-driven experiments on a supercomputing cluster and on real clouds (i.e., Amazon S3, Windows Azure Storage, and Google Cloud Storage) show the effectiveness of our algorithms for SLO guaranteed services and client price minimization.

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