Abstract-High-performance clusters have been widely deployed to solve challenging and rigorous scientific and engineering tasks. On one hand, high performance is certainly an important consideration in designing clusters to run parallel applications. On the other hand, the ever increasing energy cost requires us to effectively conserve energy in clusters. To achieve the goal of optimizing both performance and energy efficiency in clusters, in this paper, we propose two energy-efficient duplication-based scheduling algorithms-Energy-Aware Duplication (EAD) scheduling and Performance-Energy Balanced Duplication (PEBD) scheduling. Existing duplication-based scheduling algorithms replicate all possible tasks to shorten schedule length without reducing energy consumption caused by duplication. Our algorithms, in contrast, strive to balance schedule lengths and energy savings by judiciously replicating predecessors of a task if the duplication can aid in performance without degrading energy efficiency. To illustrate the effectiveness of EAD and PEBD, we compare them with a nonduplication algorithm, a traditional duplication-based algorithm, and the dynamic voltage scaling (DVS) algorithm. Extensive experimental results using both synthetic benchmarks and real-world applications demonstrate that our algorithms can effectively save energy with marginal performance degradation.

Abstract-MapReduce has become an important distributed processing model for large-scale data-intensive applications like data mining and web indexing. Hadoop-an open-source implementation of MapReduce is widely used for short jobs requiring low response time. The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature.

There yet exist no truly parallel file systems. Those that make the claim fall short when it comes to providing adequate concurrent write performance at large scale. This limitation causes large usability headaches in HPC.Users need two major capabilities missing from current parallel file systems. One, they need low latency interactivity. Two, they need high bandwidth for large parallel IO; this capability must be resistant to IO patterns and should not require tuning. There are no existing parallel file systems which provide these features. Frighteningly, exascale renders these features even less attainable from currently available parallel file systems. Fortunately, there is a path forward.

Optimizing energy consumption has become a major concern in designing economical clusters.

Abstract-Many energy conservation techniques have been proposed to achieve high energy efficiency in disk systems. Unfortunately, growing evidence shows that energy-saving schemes in disk drives usually have negative impacts on storage systems. Existing reliability models are inadequate to estimate reliability of parallel disk systems equipped with energy conservation techniques. To solve this problem, we propose a mathematical model -called MINT -to evaluate the reliability of a parallel disk system where energy-saving mechanisms are implemented. In this paper, we focus on modeling the reliability impacts of two well-known energysaving techniques -the Popular Disk Concentration technique (PDC) and the Massive Array of Idle Disks (MAID). We started this research by investigating how PDC and MAID affect the utilization and power-state transition frequency of each disk in a parallel disk system. We then model the annual failure rate of each disk as a function of the disk's utilization, powerstate transition frequency as well as operating temperature, because these parameters are key reliability-affecting factors in addition to disk ages. Next, the reliability of a parallel disk system can be derived from the annual failure rate of each disk in the parallel disk system. Finally, we used MINT to study the reliability of a parallel disk system equipped with the PDC and MAID techniques. Experimental results show that PDC is more reliable than MAID when disk workload is low. In contrast, the reliability of MAID is higher than that of PDC under relatively high I/O load.