This paper introduces the Wave Transactional Filesystem (WTF), a novel, transactional, POSIX-compatible filesystem based on a new file slicing API that enables efficient file transformations. WTF provides transactional access to a distributed filesystem, eliminating the possibility of inconsistencies across multiple files. Further, the file slicing API enables applications to construct files from the contents of other files without having to rewrite or relocate data. Combined, these enable a new class of high-performance applications. Experiments show that WTF can qualitatively outperform the industry-standard HDFS distributed filesystem, up to a factor of four in a sorting benchmark, by reducing I O costs. Microbenchmarks indicate that the new features of WTF impose only a modest overhead on top of the POSIX-compatible API.
Power-proportional filesystems are elastic, in that they dynamically change the power consumption of a cluster to scale resource usage with demand and decrease power consumption in the cluster @cite_13 @cite_24 @cite_4 . WTF's design does not consider power-proportionality, but could possibly incorporate allocation techniques from other systems to make it more elastic.
@cite_24 @cite_13 @cite_4
2099204199 2144553551 896827445 2027177485
Power-proportional cluster-based storage is an important component of an overall cloud computing infrastructure. With it, substantial subsets of nodes in the storage cluster can be turned off to save power during periods of low utilization. Rabbit is a distributed file system that arranges its data-layout to provide ideal power-proportionality down to very low minimum number of powered-up nodes (enough to store a primary replica of available datasets). Rabbit addresses the node failure rates of large-scale clusters with data layouts that minimize the number of nodes that must be powered-up if a primary fails. Rabbit also allows different datasets to use different subsets of nodes as a building block for interference avoidance when the infrastructure is shared by multiple tenants. Experiments with a Rabbit prototype demonstrate its power-proportionality, and simulation experiments demonstrate its properties at scale. Reducing the energy footprint of warehouse-scale computer (WSC) systems is key to their affordability, yet difficult to achieve in practice. The lack of energy proportionality of typical WSC hardware and the fact that important workloads (such as search) require all servers to remain up regardless of traffic intensity renders existing power management techniques ineffective at reducing WSC energy use. We present PEGASUS, a feedback-based controller that significantly improves the energy proportionality of WSC systems, as demonstrated by a real implementation in a Google search cluster. PEGASUS uses request latency statistics to dynamically adjust server power management limits in a fine-grain manner, running each server just fast enough to meet global service-level latency objectives. In large cluster experiments, PEGASUS reduces power consumption by up to 20 . We also estimate that a distributed version of PEGASUS can nearly double these savings Abstract In this paper, we introduce a novel approach for power allocation in cellular networks. In our model, we use sigmoidal-like utility functions to represent different users’ modulation schemes. Each utility function is a representation of the probability of successfully transmitted packets per unit of power consumed by a user, when using a certain modulation scheme. We consider power allocation with utility proportional fairness policy, where the fairness among users is in utility percentage i.e. percentage of successfully transmitted packets of the corresponding modulation scheme. We formulate our power allocation optimization problem as a product of utilities of all users and prove that it is convex and therefore the optimal solution is tractable. We present a distributed algorithm to allocate base station powers optimally with priority given to users running lower modulation schemes while ensuring non-zero power allocation to users running higher modulation schemes. Our algorithm prevents fluctuation in the power allocation process and is capable of traffic and modulation dependent pricing policy. This can be used to flatten traffic and decrease the service price for users. We also compare our results with a benchmark algorithm and show that our algorithm performs better in allocating powers fairly to all users without dropping any user in order to maximize performance. The ability to cap peak power consumption is a desirable feature in modern data centers for energy budgeting, cost management, and efficient power delivery. Dynamic voltage and frequency scaling (DVFS) is a traditional control knob in the tradeoff between server power and performance. Multi-core processors and the parallel applications that take advantage of them introduce new possibilities for control, wherein workload threads are packed onto a variable number of cores and idle cores enter low-power sleep states. This paper proposes Pack & Cap, a control technique designed to make optimal DVFS and thread packing control decisions in order to maximize performance within a power budget. In order to capture the workload dependence of the performance-power Pareto frontier, a multinomial logistic regression (MLR) classifier is built using a large volume of performance counter, temperature, and power characterization data. When queried during runtime, the classifier is capable of accurately selecting the optimal operating point. We implement and validate this method on a real quad-core system running the PARSEC parallel benchmark suite. When varying the power budget during runtime, Pack & Cap meets power constraints 82 of the time even in the absence of a power measuring device. The addition of thread packing to DVFS as a control knob increases the range of feasible power constraints by an average of 21 when compared to DVFS alone and reduces workload energy consumption by an average of 51.6 compared to existing control techniques that achieve the same power range.