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Although disk storage densities are improving impressively (60% to 130% compounded annually), performance improvements have been occurring at only about 7% to 10% compounded annually over the last decade. As a result, disk system performance is fast becoming a dominant factor in overall svstem behavior. Naturally, researchers want to improve overall I/O(More)
Modern high-end disk arrays often have several gigabytes of cache RAM. Unfortunately, most array caches use management policies which duplicate the same data blocks at both the client and array levels of the cache hierarchy: they are inclusive. Thus, the aggregate cache behaves as if it was only as big as the larger of the client and array caches, instead(More)
Configuring redundant disk arrays is a black art. To configure an array properly, a system administrator must understand the details of both the array and the workload it will support. Incorrect understanding of either, or changes in the workload over time, can lead to poor performance. We present a solution to this problem: a two-level storage hierarchy(More)
The I/O gap between processor speed and dynamic disk performance has been growing as VLSI performance (improving at 40–60% per year) outstrips the rate at which disk access times improve (about 7% per year). Unless something is done, new processor technologies will not be able to deliver their full promise. Fixes to this problem have concentrated on(More)
Increasing scale and the need for rapid response to changing requirements are hard to meet with current monolithic cluster scheduler architectures. This restricts the rate at which new features can be deployed, decreases efficiency and utilization, and will eventually limit cluster growth. We present a novel approach to address these needs using(More)
Energy consumption has become an important issue in high-end data centers, and disk arrays are one of the largest energy consumers within them. Although several attempts have been made to improve disk array energy management, the existing solutions either provide little energy savings or significantly degrade performance for data center workloads.Our(More)
Google's Borg system is a cluster manager that runs hundreds of thousands of jobs, from many thousands of different applications, across a number of clusters each with up to tens of thousands of machines. It achieves high utilization by combining admission control, efficient task-packing, over-commitment, and machine sharing with process-level performance(More)
Cloud systems require elastic resource allocation to minimize resource provisioning costs while meeting service level objectives (SLOs). In this paper, we present a novel PRedictive Elastic reSource Scaling (PRESS) scheme for cloud systems. PRESS unobtrusively extracts fine-grained dynamic patterns in application resource demands and adjust their resource(More)
Elastic resource scaling lets cloud systems meet application service level objectives (SLOs) with minimum resource provisioning costs. In this paper, we present CloudScale, a system that automates fine-grained elastic resource scaling for multi-tenant cloud computing infrastructures. CloudScale employs online resource demand prediction and prediction error(More)
Enterprise-scale storage systems, which can contain hundreds of host computers and storage devices and up to tens of thousands of disks and logical volumes, are difficult to design. The volume of choices that need to be made is massive, and many choices have unforeseen interactions. Storage system design is tedious and complicated to do by hand, usually(More)