Emerging non-volatile memories (NVMs) like battery-backed DRAM and phase-change memory offer durability at higher speeds than traditional storage. Each technology\u00a0represents a different point in the cost, performance and endurance space. In this paper, we propose that storage intensive\u00a0applications use more than one kind of NVM in configurable\u00a0proportions to strike a balance between cost, performance and\u00a0endurance.
\nFurthermore, in virtualized environments, we show that a fixed ratio of different NVMs across co-located applications\u00a0may not be ideal. We propose a new resource allocation and\u00a0scheduling algorithm to enable cloud providers to help co-located applications use different proportions of NVM while\u00a0meeting latency and capacity requirements. We evaluate the\u00a0proposed design by simulating several storage intensive workloads and demonstrate average cost savings of 33% for cloud\u00a0providers while degrading performance by only 4% and not\u00a0violating any service-level agreements. Compared to conventional scheduling algorithms, the proposed scheduling algorithm reduces a cloud provider\u2019s penalty cost and operation\u00a0cost by more than 20%, 50%, and 50% at low, medium, and
\nhigh server loads.<\/p>\n","protected":false},"excerpt":{"rendered":"
Emerging non-volatile memories (NVMs) like battery-backed DRAM and phase-change memory offer durability at higher speeds than traditional storage. Each technology\u00a0represents a different point in the cost, performance and endurance space. In this paper, we propose that storage intensive\u00a0applications use more than one kind of NVM in configurable\u00a0proportions to strike a balance between cost, performance and\u00a0endurance. 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