As the memory capacity in a system has been growing, refresh operations consume increasing ratios of the total DRAM power. To reduce the power consumption of such refresh operations, this paper proposes a novel value-aware refresh reduction technique called ZERO - REFRESH which exploits zero values in memory contents. A DRAM cell can retain the discharged state without refresh operations, and ZERO - REFRESH skips refresh operations on rows with all discharged cells. For abundant unallocated memory pages in typical systems, the operating system fills them with zeros to clean the contents. For those idle pages, ZERO - REFRESH can eliminate refresh operations in an OS-transparent way without any new interface to DRAM. However, for allocated memory pages, memory contents may not have many consecutive zero values to match the refresh granularity of DRAM. To increase the frequency of zero values and to arrange them to match the refresh granularity, ZERO - REFRESH transforms the value of memory blocks to the base and delta values, inspired by the prior BDI (Base-Delta-Immediate) compression technique. Once values are converted, bits are transposed to be stored as consecutive discharged bits at the refresh granularity. Such value transformation and rearrangement can make the memory contents friendly to refresh reduction based on discharged cells. The experimental results based on simulation show that the DRAM refresh operations are reduced by 37% on average for a set of benchmark applications, if the entire memory is allocated for the applications. If the memory usage statistics collected from three data center traces are applied, the DRAM refresh operations can be reduced by 46%, 57%, and 83% respectively for the three scenarios.

The increasing memory requirements of big data applications have been driving the precipitous growth of memory capacity in server systems. To maximize the efficiency of external memory, HW-based memory compression techniques have been proposed to increase effective memory capacity. Although such memory compression techniques can improve the memory efficiency significantly, a critical trade-off exists in the HW-based compression techniques. As the memory blocks need to be decompressed as quickly as possible to serve cache misses, latency-optimized techniques apply compression at the cacheline granularity, achieving the decompression latency of less than a few cycles. However, such latency-optimized techniques can lose the potential high compression ratios of capacity-optimized techniques, which compress larger memory blocks with longer latency algorithms.Considering the fundamental trade-off in the memory compression, this paper proposes a transparent dual memory compression (DMC) architecture, which selectively uses two compression algorithms with distinct latency and compression characteristics. Exploiting the locality of memory accesses, the proposed architecture compresses less frequently accessed blocks with a capacity-optimized compression algorithm, while keeping recently accessed blocks compressed with a latency-optimized one. Furthermore, instead of relying on the support from the virtual memory system to locate compressed memory blocks, the study advocates a HW-based translation between the uncompressed address space and compressed physical space. This OS-transparent approach eliminates conflicts between compression efficiency and large page support adopted to reduce TLB misses. The proposed compression architecture is applied to the Hybrid Memory Cube (HMC) with a logic layer under the stacked DRAMs. The experimental results show that the proposed compression architecture provides 54% higher compression ratio than the state-of-the-art latency-optimized technique, with no performance degradation over the baseline system without compression.

In cloud computing environment, cloud clients may view media contents, such as document, music, and/or video, stored in the cloud server storage. To support access to the contents even in offline mode, a content cache scheme in client device is proposed in this paper. In the scheme, content in the cache has its own content properties, and the content is classified into some categories divided by the content properties. In eviction algorithm on content usage, priority of the classified contents is assigned by user preference. Meanwhile, the user preference data is updated according to the content usage history. From repetition of this process, it is possible to manage the content cache, reflecting clients' preference which is changeable over time.