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Memory is increasingly becoming a bottleneck for big data and latency-sensitive applications in virtualized systems. Memory efficiency is critical for high-performance execution of virtual machines (VMs). Mechanisms proposed for improving memory utilization often rely on an accurate estimation of VM working set size at runtime, which is difficult under changing workloads. This paper explores opportunities for improving memory efficiency and their impacts on the performance of VM executions. First, we show that if each VM is initialized with an application-specified lower bound memory, then by maintaining a shared memory region across VMs in the presence of temporal memory usage variations on the host, those VMs under high memory pressure can minimize their performance loss by opportunistically and transparently harvesting idle memory on other VMs. Second, we show that by enabling on-demand VM …