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Recent literature including our past work provide analysis and solutions forusing (i) erasure coding, (ii) parallelism, or (iii) variable slicing/chunking(i.e., dividing an object of a specific size into a variable number of smallerchunks) in speeding the I/O performance of storage clouds. However, acomprehensive approach that considers all three dimensions together to achievethe best throughput-delay trade-off curve had been lacking. This paper presentsthe first set of solutions that can pick the best combination of coding rateand object chunking/slicing options as the load dynamically changes. Ourspecific contributions are as follows: (1) We establish via measurement thatcombining variable coding rate and chunking is mostly feasible over a popularpublic cloud. (2) We relate the delay optimal values for chunking level andcode rate to the queue backlogs via an approximate queueing analysis. (3) Basedon this analysis, we propose TOFEC that adapts the chunking level and codingrate against the queue backlogs. Our trace-driven simulation results show thatTOFEC's adaptation mechanism converges to an appropriate code that provides theoptimal throughput-delay trade-off without reducing system capacity. Comparedto a non-adaptive strategy optimized for throughput, TOFEC delivers $2.5\times$lower latency under light workloads; compared to a non-adaptive strategyoptimized for latency, TOFEC can scale to support over $3\times$ as manyrequests. (4) We propose a simpler greedy solution that performs on a par withTOFEC in average delay performance, but exhibits significantly more performancevariations.

On Throughput-Delay Optimal Access to Storage Clouds via Load Adaptive Coding and Chunking