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Most data generated by modern applications is stored in the cloud, and thereis an exponential growth in the volume of jobs to access these data and performcomputations using them. The volume of data access or computing jobs can beheterogeneous across different job types and can unpredictably change overtime. Cloud service providers cope with this demand heterogeneity andunpredictability by over-provisioning the number of servers hosting each jobtype. In this paper, we propose the addition of erasure-coded servers that canflexibly serve multiple job types without additional storage cost. We analyzethe service capacity region and the response time of such erasure-coded systemsand compare them with standard uncoded replication-based systems currently usedin the cloud. We show that coding expands the service capacity region, thusenabling the system to handle variability in demand for different data types.Moreover, we characterize the response time of the coded system in variousarrival rate regimes. This analysis reveals that adding even a small number ofcoded servers can significantly reduce the mean response time, with a drasticreduction in regimes where the demand is skewed across different job types.

Tackling Heterogeneous Traffic in Multi-access Systems via Erasure Coded Servers