Large physical memory modules are necessary to meet performance demands of today's ap- plications but can be a major bottleneck in terms of power consumption during idle periods or when systems are running with workloads which do not stress all the plugged memory resources. Contribution of physical memory in overall system power consumption becomes even more signi cant when CPU cores run on low power modes during idle periods with hardware support like Dynamic Voltage Frequency Scaling. Our experiments show that even 10% of memory allocations can make references to all the banks of physical memory on a long running system primarily due to the randomness in page allocation. We also show that memory hot-remove or memory migration for large blocks is often restricted, in a long running system, due to allocation policies of current Linux VM which mixes movable and unmovable pages. Hence it is crucial to improve page migration for large contiguous blocks for a practical realization of power management support provided by the hardware. Operating systems can play a decisive role in effectively utilizing the power management support of modern DIMMs like PASR(Partial Array Self Refresh) in these situations but have not been using them so far. We propose three different approaches for optimizing memory power consumption by in- ducing bank boundary awareness in the standard buddy allocator of Linux kernel as well as distinguishing user and kernel memory allocations at the same time to improve the movability of memory sections (and hence memory-hotplug) by page migration techniques. Through a set of minimal changes in the standard buddy system of Linux VM, we have been able to reduce the number of active memory banks significantly (upto 80%) as well as to improve performance of memory-hotplug framework (upto 85%).

Modern architectures support multiple size pages to facilitate applications that use large chunks of contiguous memory either for buffer allocation, application specific memory management, in-memory caching or garbage collection. Most general purpose processors support larger page sizes, for e.g. x86 architecture supports 2MB and 1GB pages while PowerPC architecture supports 64KB, 16MB, 16GB pages. Such larger size pages are also known as superpages or huge pages. With the help of huge pages TLB reach can be increased significantly. The Linux kernel can transparently use these huge pages to significantly bring down the cost of TLB translations. With Transparent Huge Pages (THP) support in Linux kernel the end users or the application developers need not make any change to their application. Memory fragmentation which has been one of the classical problems in computing systems for decades is a key problem for the allocation of huge pages. Ubiquitous huge page support across architectures makes effective fragmentation management even more critical for modern systems. Applications tend to stress system TLB in the absence of huge pages, for virtual to physical address translation, which adversely affects performance/energy characteristics in long running systems. Since most kernel pages tend to be unmovable, fragmentation created due to their misplacement is more problematic and nearly impossible to recover with memory compaction. In this work, we explore physical memory manager of Linux and the interaction of kernel page placement with fragmentation avoidance and recovery mechanisms. Our analysis reveals that not only a random kernel page layout thwarts the progress of memory compaction; it can actually induce more fragmentation in the system. To address this problem, we propose a new allocator which takes special care for the placement of kernel pages. We propose a new region which represents memory area having kernel as well as user pages. Using this new region we introduce a staged allocator which with change in fragmentation level adapts and optimizes the kernel page placement. Later we introduce Illuminator which with zero overhead outperforms default kernel in terms of huge page allocation success rate and compaction overhead with respect to each huge page. We also show that huge page allocation is not a one dimensional problem but a two fold concern with how the fragmentation recovery mechanism may potentially interfere with the page clustering policy of allocator and worsen the fragmentation. Our results show that with effective kernel page placements the mixed page block counts reduces upto 70%, which allows our system to allocate 3x-4x huge pages than the default Kernel. Using these additional huge pages we show up to 38% improvement in terms of energy consumed and reduction in execution time up to 39% on standard benchmarks.