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Abstract

Resource provisioning in compute clouds often requires an estimate of the capacity needs of Virtual Machines (VMs). The estimated VM size is the basis for allocating resources commensurate with demand. In contrast to the traditional practice of estimating the size of VMs individually, we propose a joint-VM provisioning approach in which multiple VMs are consolidated and provisioned together, based on an estimate of their aggregate capacity needs. This new approach exploits statistical multiplexing among the workload patterns of multiple VMs, i.e., the peaks and valleys in one workload pattern do not necessarily coincide with the others. Thus, the unused resources of a low utilized VM can be borrowed by the other co-located VMs with high utilization. Compared to individual-VM based provisioning, joint-VM provisioning could lead to much higher resource utilization. This paper presents three design modules to enable such a concept in practice. Specifically, a performance constraint describing the capacity need of a VM for achieving a certain level of application performance; an algorithm for estimating the aggregate size of multiplexed VMs; a VM selection algorithm that seeks to find those VM combinations with complementary workload patterns. We showcase that the proposed three modules can be seamlessly plugged into applications such as resource provisioning, and providing resource guarantees for VMs. The proposed method and applications are evaluated by performance data collected from about 16 thousand VMs in commercial data centers. The results demonstrate more than 45% improvements in terms of the overall resource utilization.

Computer architecture has experienced a major paradigm shift from focusing only on raw performance to considering power-performance efficiency as the defining factor of the emerging systems. Along with this shift has come increased interest in workload characterization. This interest fuels two closely related areas of research. First, various studies explore the properties of workload variations and develop methods to identify and track different execution behavior, commonly referred to as "phase analysis". Second, a large complementary set of research studies dynamic, on-the-fly system management techniques that can adaptively respond to these differences in application behavior. Both of these lines of work have produced very interesting and widely useful results. Thus far, however, there exists only a weak link between these conceptually related areas, especially for real-system studies.Our work aims to strengthen this link by demonstrating a real-system implementation of a runtime phase predictor that works cooperatively with on-the-fly dynamic management. We describe a fully-functional deployed system that performs accurate phase predictions on running applications. The key insight of our approach is to draw from prior branch predictor designs to create a phase history table that guides predictions. To demonstrate the value of our approach, we implement a prototype system that uses it to guide dynamic voltage and frequency scaling. Our runtime phase prediction methodology achieves above 90% prediction accuracies for many of the experimented benchmarks. For highly variable applications, our approach can reduce mispredictions by more than 6X over commonly-used statistical approaches. Dynamic frequency and voltage scaling, when guided by our runtime phase predictor, achieves energy-delay product improvements as high as 34% for benchmarks with non-negligible variability, on average 7% better than previous methods and 18% better than a baseline unmanaged system.