Abstract

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Cloud computing offers high scalability, flexibility and cost-effectiveness to meet emerging computing requirements. Understanding the characteristics of real workloads on a large production cloud cluster benefits not only cloud service providers but also researchers and daily users. This paper studies a largescale Google cluster usage trace dataset and characterizes how the machines in the cluster are managed and the workloads submitted during a 29-day period behave. We focus on the frequency and pattern of machine maintenance events, joband task-level workload behavior, and how the overall cluster resources are utilized.

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This paper derives simple, yet fundamental formulas to describe the interplay between parallelism of an application, program performance, and energy consumption. Given the ratio of serial and parallel portions in an application and the number of processors, we derive optimal frequencies allocated to the serial and parallel regions in an application to either minimize the total energy consumption or minimize the energy-delay product. The impact of static power is revealed by considering the ratio between static and dynamic power and quantifying the advantages of adding to the architecture capability to turn off individual processors and save static energy. We further determine the conditions under which one can obtain both energy and speed improvement, as well as the amount of improvement. While the formulas we obtain use simplifying assumptions, they provide valuable theoretical insights into energy-aware processor resource management. Our results form a basis for several interesting research directions in the area of energy-aware multicore processor architectures.

The number of cores in a single chip multiprocessor is expected

With their potential for high scalability and density, resistive memories are foreseen as a promising technology that overcomes the physical limitations confronted by charge-based DRAM and flash memory. Yet, a main burden towards the successful adoption and commercialization of resistive memories is their low cell reliability caused by process variation and limited write endurance. Typically, faulty and worn-out cells are permanently stuck at either '0' or '1'. To overcome the challenge, a robust error correction scheme that can recover from many hard faults is required.In this paper, we propose and evaluate RDIS, a novel scheme to efficiently tolerate memory stuck-at faults. RDIS allows for the correct retrieval of data by recursively determining and efficiently keeping track of the positions of the bits that are stuck at a value different from the ones that are written, and then, at read time, by inverting the values read from those positions. RDIS is characterized by a very low probability of failure that increases slowly with the relative increase in the number of faults. Moreover, RDIS tolerates many more faults than the best existing scheme-by up to 95% on average at the same overhead level.

This paper proposes DCC (Dynamic Cache Clustering), a novel distributed cache management scheme for large-scale chip multiprocessors. Using DCC, a per-core cache cluster is comprised of a number of L2 cache banks and cache clusters are constructed, expanded, and contracted dynamically to match each core's cache demand. The basic trade-offs of varying the on-chip cache clusters are average L2 access latency and L2 miss rate. DCC uniquely and efficiently optimizes both metrics and continuously tracks a near-optimal cache organization from many possible configurations. Simulation results using a full-system simulator demonstrate that DCC outperforms alternative L2 cache designs.