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.

Today BigData systems commonly use resource management systems such as TORQUE, Mesos, and Google Borg to share the physical resources among users or applications. Enabled by virtualization, users can run their applications on the same node with low mutual interference. Container-based virtualizations (e.g., Docker and Linux Containers) offer a lightweight virtualization layer, which promises a near-native performance and is adopted by some Big-Data resource sharing platforms such as Mesos. Nevertheless, using containers to consolidate the I/O resources of shared storage systems is still at an early stage, especially in a distributed file system (DFS) such as Hadoop File System (HDFS). To overcome this issue, we propose a distributed middleware system, DFS-Container, by further containerizing DFS. We also evaluate and analyze the unfairness of using containers to proportionally allocate the I/O resource of DFS. Based on these analyses and evaluations, we propose and implement a new mechanism, IOPS-Regulator, which improve the fairness of proportional allocation by 74.4% on average.

For the sake of higher cell density while achieving near-zero standby power, recent research progress in Magnetic Tunneling Junction (MTJ) devices has leveraged Multi-Level Cell (MLC) configurations of Spin-Transfer Torque Random Access Memory (STT-RAM). However, in orderto mitigate the write disturbance in an MLC strategy, data stored in the soft bit must be restored back immediately after the hard bit switching is completed. Furthermore, as the result of MTJ feature size scaling, the soft bit can be expected to become disturbed by the read sensing current, thus requiring an immediate restore operation to ensure the data reliability. In this paper, we design and analyze a novel Adaptive Restore Scheme for Write Disturbance (ARS-WD) and Read Disturbance (ARS-RD), respectively. ARS-WD alleviates restoration overhead by intentionally overwriting soft bit lines which are less likely to be read. ARS-RD, on the other hand, aggregates the potential writes and restore the soft bit line at the time of its eviction from higher level cache. Both of these two schemes are based on a lightweight forecasting approach for the future read behavior of the cache block. Our experimental results show substantial reduction in soft bit line restore operations, delivering 17.9 percent decrease in overall energy consumption and 9.4 percent increase in IPC, while incurring negligible capacity overhead. Moreover, ARS promotes advantages of MLC to provide a preferable L2 design alternative in terms of energy, area and latency product compared to SLC STT-RAM alternatives.

Spin-Transfer Torque Random Access Memory (STT-RAM) has been identified as an advantageous candidate for on-chip memory technology due to its high density and ultra low leakage power. Recent research progress in Magnetic Tunneling Junction (MTJ) devices has developed Multi-Level Cell (MLC) STT-RAM to further enhance cell density. To avoid the write disturbance in MLC strategy, data stored in the soft bit must be restored back immediately after the hard bit switching is completed. However, frequent restores are not only unnecessary, but also introduce a significant energy consumption overhead. In this paper, we propose an Adaptive Overwrite Scheme (AOS) which alleviates restoration overhead by intentionally overwriting selected soft bits based on RRD (Read Reuse Distance). Our experimental results show 54.6% reduction in soft bit restoration, delivering 10.8% decrease in overall energy consumption. Moreover, AOS promotes MLC to be a preferable L2 design alternative in terms of energy, area and latency product.

The Bulk Synchronous Parallel (BSP) model, which divides a graphing algorithm into multiple supersteps, has become extremely popular in distributed graph processing systems. However, the high number of network messages exchanged in each superstep of the graph algorithm will create a long period of time. We refer to this as a communication delay. Furthermore, the BSP's global synchronization barrier does not allow computation in the next superstrep to be scheduled during this communication delay. This communication delay makes up a large percentage of the overall processing time of a superstep. While most recent research has focused on reducing number of network messages, but communication delay is still a deterministic factor for overall performance. In this paper, we add a runtime communication and computation scheduler into current graph BSP implementations. This scheduler will move some computation from the next superstep to the communication phase in the current superstep to mitigate the communication delay. Finally, we prototyped our system, Zebra, on Apache Hama, which is an open source clone of the classic Google Pregel. By running a set of graph algorithms on an in-house cluster, our evaluation shows that our system could completely eliminate the communication delay in the best case and can achieve average 2X speedup over Hama.