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Far-memory techniques that enable applications to use remote memory are increasingly appealing in modern data centers, supporting applications’ large memory footprint and improving machines’ resource utilization. Unfortunately, most far-memory techniques focus on OS-level optimizations and are agnostic to managed runtimes and garbage collections (GC) underneath applications written in high-level languages. With different object-access patterns from applications, GC can severely interfere with existing far-memory techniques, breaking remote memory prefetching algorithms and causing severe local-memory misses. We developed MemLiner, a runtime technique that improves the performance of far-memory systems by aligning memory accesses from application and GC threads so that they follow similar memory access paths, thereby (1) reducing the local-memory working set and (2) improving remote-memory prefetching through simplified memory access patterns. We implemented MemLiner in two widely used GCs in OpenJDK: G1 and Shenandoah. Our evaluation with a range of widely deployed cloud systems shows that MemLiner improves applications’ end-to-end performance by up to3.3×and reduces applications’ tail latency by up to220.0×.