Since Non-Volatile Memories (NVMs) started entering the mainstream memory/storage market, we must consider how to secure NVM-equipped computing systems. Recent Meltdown and Spectre attacks are a strong evidence that security must be intrinsic to computing systems instead of being added as an afterthought. Processor vendors are taking the first steps and are beginning to build security primitives into commodity processors. One security primitive that is associated with the use of emerging NVMs is memory encryption. Memory encryption, while necessary, is very challenging when used with NVMs because it exacerbates the write endurance problem. Secure architectures use cryptographic metadata that must be persisted and restored to allow secure recovery of data in the event of power-loss. Specifically, encryption counters must be persistent to enable secure and functional recovery of an interrupted system. However, the cost of ensuring and maintaining persistence for these counters can be significant. In this paper, we propose a novel scheme to maintain encryption counters without the need for frequent updates. Our new memory controller design, Osiris, repurposes memory Error-Correction Codes (ECCs) to enable fast restoration and recovery of encryption counters. Since different counter-mode encryption schemes are used in industry and research, we provide a versatile Osiris implementation that improves the performance and write-endurance in different memory encryption schemes. To evaluate our design, we use Gem5 to run eight memory-intensive workloads selected from SPEC2006 and U.S. Department of Energy (DoE) proxy applications, and three computation-intensive graph algorithms from CRONO. Compared to a write-through counter-cache scheme, on average, Osiris can reduce 45.8 percent of the memory writes (increase lifetime by 1.86x), and reduce the performance overhead from 44.7 percent(for write-through) to only 4.49 percent. Furthermore, without the need for backup battery or extra power-supply hold-up time, Osiris performs better than a battery-backed write-back (4.4 versus 5.7 percent overhead) and has less write-traffic (1.8 versus 5.4 percent overhead).

Non-Volatile Memory is here and provides an attractive fabric for main memory. Unlike DRAM, non-volatile main memory (NVMM) retains data after power loss. This allows memory to host data persistently across crashes and reboots, but opens up opportunities for attackers to snoop and/or tamper with data between boot episodes. While memory encryption and integrity verification have been well studied for DRAM systems, new challenges surface for NVMM if we want to simultaneously preserve security guarantees, data recovery across crashes/reboots, good persistence performance, and fast recovery.

With Non-Volatile Memories (NVMs) beginning to enter the mainstream computing market, it is time to consider how to secure NVM-equipped computing systems. Recent Meltdown and Spectre attacks are evidence that security must be intrinsic to computing systems and not added as an afterthought. Processor vendors are taking the first steps and are beginning to build security primitives into commodity processors. One security primitive that is associated with the use of emerging NVMs is memory encryption. Memory encryption, while necessary, is very challenging when used with NVMs because it exacerbates the write endurance problem. Secure architectures use cryptographic metadata that must be persisted and restored to allow secure recovery of data in the event of power-loss. Specifically, encryption counters must be persistent to enable secure and functional recovery of an interrupted system. However, the cost of ensuring and maintaining persistence for these counters can be significant. In this paper, we propose a novel scheme to maintain encryption counters without the need for frequent updates. Our new memory controller design, Osiris, repurposes memory Error-Correction Codes (ECCs) to enable fast restoration and recovery of encryption counters. To evaluate our design, we use Gem5 to run eight memory-intensive workloads selected from SPEC2006 and U.S. Department of Energy (DoE) proxy applications. Compared to a write-through counter-cache scheme, on average, Osiris can reduce 48.7% of the memory writes (increase lifetime by 1.95x), and reduce the performance overhead from 51.5% (for write-through) to only 5.8%. Furthermore, without the need for backup battery or extra power-supply hold-up time, Osiris performs better than a battery-backed write-back (5.8% vs. 6.6% overhead) and has less write-traffic (2.6% vs. 5.9% overhead).

The distributed file system, HDFS, is widely deployed as the bedrock for many parallel big data analysis. However, when running multiple parallel applications over the shared file system, the data requests from different processes/executors will unfortunately be served in a surprisingly imbalanced fashion on the distributed storage servers. These imbalanced access patterns among storage nodes are caused because a). unlike conventional parallel file system using striping policies to evenly distribute data among storage nodes, data-intensive file system such as HDFS store each data unit, referred to as chunk file, with several copies based on a relative random policy, which can result in an uneven data distribution among storage nodes; b). based on the data retrieval policy in HDFS, the more data a storage node contains, the higher probability the storage node could be selected to serve the data. Therefore, on the nodes serving multiple chunk files, the data requests from different processes/executors will compete for shared resources such as hard disk head and networkbandwidth, resulting in a degraded I/O performance. In this paper, we first conduct a complete analysis on how remote and imbalanced read/write patterns occur and how they are affected by the size of the cluster. We then propose novel methods, referred to as Opass, to optimize parallel data reads, as well as to reduce the imbalance of parallel writes on distributed file systems. Our proposed methods can benefit parallel data-intensive analysis with various parallel data access strategies. Opass adopts new matching-based algorithms to match processes to data so as to compute the maximum degree of data locality and balanced data access. Furthermore, to reduce the imbalance of parallel writes, Opass employs a heatmap for monitoring the I/O statuses of storage nodes and performs HM-LRU policy to select a local optimal storage node for serving write requests. Experiments are conducted on PRObE's Marmot 128-node cluster testbed and the results from both benchmark and well-known parallel applications show the performance benefits and scalability of Opass.

Hadoop distributed file system (HDFS) is a major distributed file system for commodity clusters and cloud computing. Its extensive scalability and replica fault tolerance scheme makes it well suited for data-intensive application. Due to the tremendous growth of data, many computation-centric applications also become data-intensive. However, they are not optimal on HDFS, which leaves plenty of space for performance optimization. In this paper we ported an MPI-SVM solver, originally developed for HPC environment to the HDFS. We specifically improved the data pre-processing part that requires large amount of I/O operations by a deterministic scheduling method. Our improvement showed a balanced read pattern on each node. The time ratio between the longest process and the shortest process has been reduced by 60%. Also the average read time has significantly reduced by 78%. The data served on each node also showed a small variance in comparison with the originally ported SVM algorithm. We believe that our design avoids the overhead introduced by remote I/O operations, which will be beneficial to many algorithms when coping with large scale of data.