Emerging data-intensive applications are creating non-uniform CPU and I/O workloads which impose the requirement to consider both CPU and I/O effects in the power management strategies. Current approaches focus on scaling down the CPU frequency based on CPU busy/idle ratio without taking I/O into consideration. Therefore, they do not fully exploit the opportunities in power conservation. In this paper, we propose a novel power management scheme called model-free, adaptive, rule-based (MAR) in multiprocessor systems to minimize the CPU power consumption subject to performance constraints. By introducing new I/O wait status, MAR is able to accurately describe the relationship between core frequencies, performance and power consumption. Moreover, we adopt a model-free control method to filter out the I/O wait status from the traditional CPU busy/idle model in order to achieve fast responsiveness to burst situations and take full advantage of power saving. Our extensive experiments on a physical testbed demonstrate that, for SPEC benchmarks and data-intensive (TPC-C) benchmarks, an MAR prototype system achieves 95.8-97.8 percent accuracy of the ideal power saving strategy calculated offline. Compared with baseline solutions, MAR is able to save 12.3-16.1 percent more power while maintain a comparable performance loss of about 0.78-1.08 percent. In addition, more simulation results indicate that our design achieved 3.35-14.2 percent more power saving efficiency and 4.2-10.7 percent less performance loss under various CMP configurations as compared with various baseline approaches such as LAST, Relax, PID and MPC.

Current High Performance Computing (HPC) applications have seen an explosive growth in the size of data in recent years. Many application scientists have initiated efforts to integrate data-intensive computing into computational-intensive HPC facilities, particularly for data analytics. We have observed several scientific applications which must migrate their data from an HPC storage system to a data-intensive one for analytics. There is a gap between the data semantics of HPC storage and data-intensive system, hence, once migrated, the data must be further refined and reorganized. This reorganization must be performed before existing data-intensive tools such as MapReduce can be used to analyze data. This reorganization requires at least two complete scans through the data set and then at least one MapReduce program to prepare the data before analyzing it. Running multiple MapReduce phases causes significant overhead for the application, in the form of excessive I/O operations. That is for every MapReduce phase, a distributed read and write operation on the file system must be performed. Our contribution is to develop a MapReduce-based framework for HPC analytics to eliminate the multiple scans and also reduce the number of data preprocessing MapReduce programs. We also implement a data-centric scheduler to further improve the performance of HPC analytics MapReduce programs by maintaining the data locality. We have added additional expressiveness to the MapReduce language to allow application scientists to specify the logical semantics of their data such that 1) the data can be analyzed without running multiple data preprocessing MapReduce programs, and 2) the data can be simultaneously reorganized as it is migrated to the data-intensive file system. Using our augmented Map-Reduce system, MapReduce with Access Patterns (MRAP), we have demonstrated up to 33 percent throughput improvement in one real application, and up to 70 percent in an I/O kernel of another application. Our results for scheduling show up to 49 percent improvement for an I/O kernel of a prevalent HPC analysis application.

With the rapid development of computation capability, the massive increase in data volume has outmoded compute-intensive clusters for HPC analysis of large-scale data sets due to a huge amount of data transfer over network. Co-located compute and storage has been introduced in data-intensive clusters to avoid network bottleneck by launching the computation on nodes in which most of the input data reside. Chunk-based storage systems are typical examples, splitting data into blocks and randomly storing them across nodes. Records as the input data for the analysis are read from blocks. This method implicitly assumes that a single record resides on a single node and then data transfer can be avoided. However, this assumption does not always hold because there is a gap between records and blocks. The current solution overlooks the relationship between the computation unit as a record and the storage unit as a block. For situations with records belonging to one block, there would be no data transfer. But in practice, one record could consist of several blocks. This is especially true for binary files, which introduce extra data transfer due to preparing the input data before conducting the analysis. Blocks belonging to a single record are scattered randomly across the data nodes regardless of to the semantics of the records. To address these problems, we develop two solutions in this paper, one is to develop a Record-Based Block Distribution (RBBD) framework and the other is a data-centric scheduling using a Weighted Set Cover Scheduling (WSCS) to schedule the tasks. The Record-Based Block Distribution (RBBD) framework for data-intensive analytics aims to eliminate the gap between records and blocks and accomplishes zero data transfer among nodes. The Weighted Set Cover Scheduling (WSCS) is proposed to further improve the performance by optimizing the combination of nodes. Our experiments show that overlooking the record and block relationship can cause severe performance problems when a record is comprised of several blocks scattered in different nodes. Our proposed novel data storage strategy, Record-Based Block Distribution (RBBD), optimizes the block distribution according to the record and block relationship. By being combined with our novel scheduling Weighted Set Cover Scheduling (WSCS), we efficiently reduces extra data transfers, and eventually improves the performance of the chunk-based storage system. Using our RBBD framework and WSCS in chunk-based storage system, our extensive experiments show that the data transfer decreases by 36.4% (average) and the scheduling algorithm outperforms the random algorithm by 51%-62%; the deviation from the ideal solutions is no more than 6.8%.

In the past few years, more storage system applications have employed transaction processing techniques to ensure data integrity and consistency. Logging is one of the key requirements to ensure transaction Atomicity, Consistency, Isolation, Durability (ACID) properties and data recoverability in transaction processing systems (TPS). Recently, emerging complex I/O bound transactions have resulted in substantially more log content and higher log flushing latency. The latency will delay transaction commit and decrease the overall throughput of the TPS. On the other hand, RAID is widely used as the underlying storage system for Databases to guarantee system reliability and availability with high I/O performance. In this paper, we observe the overlap between the redundancies in the underlying RAID storage system and database logging system, and propose a novel reliable storage architecture called Transactional RAID (TRAID). TRAID deduplicates this overlap by only logging one compact version (XOR results) of recovery references for the updating data. It minimizes the amount of log content and thereby boosts the overall transaction processing performance. At the same time, TRAID guarantees the same RAID reliability, as well as recovery correctness and ACID semantics as current TPS setups. We experiment on two open-source database systems: Berkeley DB and PostgreSQL, with three different workloads: standard OLTP benchmark TPC-C, customized TPC-C with strong access locality, and customized TPC-C with write-intensive property. Then we test TRAID performance with "Group Commit” enabled. Finally, we evaluate the recovery efficiency of TRAID. Our extensive results demonstrate that for throughput, TRAID outperforms RAID by 43.24-69.5 percent for various workloads; it also saves on log space by 28.57-35.48 percent, and outperforms RAID by about 20 percent in throughput with "Group Commit” enabled. At last, we show that TRAID outperforms RAID from 28.7 to 35.7 percent during the recovery.

The emerging data-intensive applications of today are comprised of non-uniform CPU and I/O intensive workloads, thus imposing a requirement to consider both CPU and I/O effects in the power management strategies. Only scaling down the processor's frequency based on its busy/idle ratio cannot fully exploit opportunities of saving power. Our experiments show that besides the busy and idle status, each processor may also have I/O wait phases waiting for I/O operations to complete. During this period, the completion time is decided by the I/O subsystem rather than the CPU thus scaling the processor to a lower frequency will not affect the performance but save more power. In addition, the CPU's reaction to the I/O operations may be significantly affected by several factors, such as I/O type (sync or unsync), instruction/job level parallelism, it cannot be accurately modeled via physics laws like mechanical or chemical systems. In this paper, we propose a novel power management scheme called MAR (modeless, adaptive, rule-based) in multiprocessor systems to minimize the CPU power consumption under performance constraints. By using richer feedback factors, e.g. the I/O wait, MAR is able to accurately describe the relationships among core frequencies, performance and power consumption. We adopt a modeless control model to reduce the complexity of system modeling. MAR is designed for CMP (Chip Multi Processor) systems by employing multi-input/multi-output (MIMO) theory and per core level DVFS (Dynamic Voltage and Frequency Scaling). Our extensive experiments on a physical test bed demonstrate that, for the SPEC benchmark and data-intensive (TPC-C) benchmark, the efficiency of MAR is 93.6-96.2\% accurate to the ideal power saving strategy calculated off-line. Compared with baseline solutions, MAR could save 22.5-32.5\% more power while keeping the comparable performance loss of about 1.8-2.9\%. In addition, simulation results show the efficiency of our design for various CMP configurations.

Recent years have witnessed an increasing demand for super data clusters. The super data clusters have reached the petabyte-scale that can consist of thousands or tens of thousands storage nodes at a single site. For this architecture, reliability is becoming a great concern. In order to achieve a high reliability, data recovery and node reconstruction is a must. Although extensive research works have investigated how to sustain high performance and high reliability in case of node failures at large scale, a reverse lookup problem, namely finding the objects list for the failed node remains open. This is especially true for storage systems with high requirement of data integrity and availability, such as scientific research data clusters and etc. Existing solutions are either time consuming or expensive. Meanwhile, replication based block placement can be used to realize fast reverse lookup. However, they are designed for centralized, small-scale storage architectures. In this paper, we propose a fast and efficient reverse lookup scheme named Group-based Shifted Declustering (G-SD) layout that is able to locate the whole content of the failed node. G-SD extends our previous shifted declustering layout and applies to large-scale file systems. Our mathematical proofs and real-life experiments show that G-SD is a scalable reverse lookup scheme that is up to one order of magnitude faster than existing schemes.

Technology trends are making sophisticated replication-based storage architectures become a standard commercial practice in today's computing. Existing solutions successfully developed optimal and near-optimal parallelism layouts such as declustered parity organizations at small-scale storage architectures. There are very few studies on multiway replication-based storage architectures that are significantly different from parity-based storage architectures. It is difficult to scale up to a large size because current placement-ideal solutions have a limited number of configurations. In this paper, we retrofit the desirable properties of optimal parallelism definitions in parity architectures for replication architectures, and propose a novel placement-ideal data layout-shifted declustering. Shifted declustering layout obtains optimal parallelism in a wide range of configurations, and obtains optimal high performance and load balancing in both fault-free and degraded mode. Our theoretical proofs and comprehensive simulation results show that shifted declustering is superior in performance, load balancing, and reliability to traditional layout schemes such as standard mirroring, chained declustering, group-rotational declustering, and existing parity layout schemes PRIME and RELPR.

The data generated by scientific simulation, sensor, monitor or optical telescope has increased with dramatic speed. In order to analyze the raw data fast and space efficiently, data pre-process operation is needed to achieve better performance in data analysis phase. Current research shows an increasing tread of adopting MapReduce framework for large scale data processing. However, the data access patterns which generally applied to scientific data set are not supported by current MapReduce framework directly. The gap between the requirement from analytics application and the property of MapReduce framework motivates us to provide support for these data access patterns in MapReduce framework. In our work, we studied the data access patterns in matrix files and proposed a new concentric data layout solution to facilitate matrix data access and analysis in MapReduce framework. Concentric data layout is a hierarchical data layout which maintains the dimensional property in large data sets. Contrary to the continuous data layout adopted in current Hadoop framework, concentric data layout stores the data from the same sub-matrix into one chunk, and then stores chunks symmetrically in a higher level. This matches well with the matrix like computation. The concentric data layout preprocesses the data beforehand, and optimizes the afterward run of MapReduce application. The experiments show that the concentric data layout improves the overall performance, reduces the execution time by about 38% when reading a 64 GB file. It also mitigates the unused data read overhead and increases the useful data efficiency by 32% on average.

Although data prefetching algorithms have been extensively studied for years, there is no counterpart research done for metadata access performance. Existing data prefetching algorithms, either lack of emphasis on group prefetching, or bearing a high level of computational complexity, do not work well with metadata prefetching cases. Therefore, an efficient, accurate, and distributed metadata-oriented prefetching scheme is critical to leverage the overall performance in large distributed storage systems. In this paper, we present a novel weighted-graph-based prefetching technique, built on both direct and indirect successor relationship, to reap performance benefit from prefetching specifically for clustered metadata servers, an arrangement envisioned necessary for petabyte-scale distributed storage systems. Extensive trace-driven simulations show that by adopting our new metadata prefetching algorithm, the miss rate for metadata accesses on the client site can be effectively reduced, while the average response time of metadata operations can be dramatically cut by up to 67 percent, compared with legacy LRU caching algorithm and existing state-of-the-art prefetching algorithms.