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57
VirtualPower: Coordinated Power Management in Virtualized Enterprise Systems
- In Proceedings of International Symposium on Operating System Principles (SOSP
, 2007
"... Power management has become increasingly necessary in large-scale datacenters to address costs and limitations in cooling or power delivery. This paper explores how to integrate power management mechanisms and policies with the virtualization technologies being actively deployed in these environment ..."
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Cited by 161 (12 self)
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Power management has become increasingly necessary in large-scale datacenters to address costs and limitations in cooling or power delivery. This paper explores how to integrate power management mechanisms and policies with the virtualization technologies being actively deployed in these environments. The goals of the proposed VirtualPower approach to online power management are (i) to support the isolated and independent operation assumed by guest virtual machines (VMs) running on virtualized platforms and (ii) to make it possible to control and globally coordinate the effects of the diverse power management policies applied by these VMs to virtualized resources. To attain these goals, VirtualPower extends to guest VMs ‘soft ’ versions of the hardware power states for which their policies are designed. The resulting technical challenge is to appropriately map VM-level updates made to soft power states to actual changes in the states or in the allocation of underlying virtualized hardware. An implementation of VirtualPower Management (VPM) for the Xen hypervisor addresses this challenge by provision of multiple system-level abstractions including VPM states, channels, mechanisms, and rules. Experimental evaluations on modern multicore platforms highlight resulting improvements in online power management capabilities, including minimization of power consumption with little or no performance penalties and the ability to throttle power consumption while still meeting application requirements. Finally, coordination of online methods for server consolidation with VPM management techniques in heterogeneous server systems is shown to provide up to 34% improvements in power consumption.
Power and performance management of virtualized computing environments via lookahead control.
- In Proc. Fifth Int’l Conference on Autonomic Computing,
, 2008
"... Abstract There is growing incentive to reduce the power consumed by large-scale data centers that host online services such as banking, retail commerce, and gaming. Virtualization is a promising approach to consolidating multiple online services onto a smaller number of computing resources. A virtu ..."
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Cited by 138 (6 self)
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Abstract There is growing incentive to reduce the power consumed by large-scale data centers that host online services such as banking, retail commerce, and gaming. Virtualization is a promising approach to consolidating multiple online services onto a smaller number of computing resources. A virtualized server environment allows computing resources to be shared among multiple performance-isolated platforms called virtual machines. By dynamically provisioning virtual machines, consolidating the workload, and turning servers on and off as needed, data center operators can maintain the desired quality-of-service (QoS) while achieving higher server utilization and energy efficiency. We implement and validate a dynamic resource provisioning framework for virtualized server environments wherein the provisioning problem is posed as one of sequential optimization under uncertainty and solved using a lookahead control scheme. The proposed approach accounts for the switching costs incurred while provisioning virtual machines and explicitly encodes the corresponding risk in the optimization problem. Experiments using the Trade6 enterprise application show that a server cluster managed by the controller conserves, on average, 22% of the power required by a system without dynamic control while still maintaining QoS goals. Finally, we use trace-based simulations to analyze controller performance on server clusters larger than our testbed, and show how concepts from approximation theory can be used to further reduce the computational burden of controlling large systems.
A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems
"... Traditionally, the development of computing systems has been focused on performance improvements driven by the demand of applications from consumer, scientific and business domains. However, the ever increasing energy consumption of computing systems has started to limit further performance growth d ..."
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Cited by 58 (4 self)
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Traditionally, the development of computing systems has been focused on performance improvements driven by the demand of applications from consumer, scientific and business domains. However, the ever increasing energy consumption of computing systems has started to limit further performance growth due to overwhelming electricity bills and carbon dioxide footprints. Therefore, the goal of the computer system design has been shifted to power and energy efficiency. To identify open challenges in the area and facilitate future advancements it is essential to synthesize and classify the research on power and energy-efficient design conducted to date. In this work we discuss causes and problems of high power / energy consumption, and present a taxonomy of energy-efficient design of computing systems covering the hardware, operating system, virtualization and data center levels. We survey various key works in the area and map them to our taxonomy to guide future design and development efforts. This chapter is concluded with a discussion of advancements identified in energy-efficient computing and our vision on future
A Cooperative Game Theoretical Technique for Joint Optimization of Energy Consumption and Response Time in Computational Grids
"... Abstract—With the explosive growth in computers and the growing scarcity in electric supply, reduction of energy consumption in large-scale computing systems has become a paramount research issue. In this paper, we study the problem of allocation of tasks onto a computational grid, with the aim to s ..."
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Cited by 44 (18 self)
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Abstract—With the explosive growth in computers and the growing scarcity in electric supply, reduction of energy consumption in large-scale computing systems has become a paramount research issue. In this paper, we study the problem of allocation of tasks onto a computational grid, with the aim to simultaneously minimize the energy consumption and the makespan subject to the constraints of deadlines and tasks ’ architectural requirements. We propose a solution from cooperative game theory based on the concept of Nash Bargaining Solution (NBS). In this cooperative game, machines collectively arrive at a decision that describes the task allocation that is collectively best for the system, ensuring that the allocations are both energy and makespan optimized. Through rigorous mathematical proofs we show that the proposed cooperative game in mere Oðnm logðmÞÞ time (where n is the number of tasks, and m is the number of machines in the system) produces a NBS that guarantees Pareto optimally. The simulation results show that the proposed technique achieves superior performance compared to the Greedy and Linear Relaxation (LR) heuristics and with competitive performance relative to the optimal solution implemented in LINDO for small-scale problems. Index Terms—Energy-aware systems, distributed systems, constrained optimization, convex programming. Ç 1
Paragon: Qos-aware scheduling for heterogeneous datacenters
- In Proceedings of the eighteenth international
, 2013
"... Large-scale datacenters (DCs) host tens of thousands of diverse applications each day. However, interference between colocated workloads and the difficulty to match applications to one of the many hardware platforms available can degrade performance, violating the quality of service (QoS) guarantees ..."
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Cited by 37 (7 self)
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Large-scale datacenters (DCs) host tens of thousands of diverse applications each day. However, interference between colocated workloads and the difficulty to match applications to one of the many hardware platforms available can degrade performance, violating the quality of service (QoS) guarantees that many cloud workloads require. While previous work has identified the impact of heterogeneity and interference, existing solutions are computationally intensive, cannot be applied online and do not scale beyond few applications. We present Paragon, an online and scalable DC scheduler that is heterogeneity and interference-aware. Paragon is derived from robust analytical methods and instead of profiling each application in detail, it leverages information the system already has about applications it has previously seen. It uses collaborative filtering techniques to quickly and accurately classify an unknown, incoming workload with respect to heterogeneity and interference in multiple shared resources, by identifying similarities to previously scheduled applications. The classification allows Paragon to greedily schedule applications in a manner that minimizes interference and maximizes server utilization. Paragon scales to tens of thousands of servers with marginal scheduling overheads in terms of time or state. We evaluate Paragon with a wide range of workload scenarios, on both small and large-scale systems, including 1,000 servers on EC2. For a 2,500-workload scenario, Paragon enforces performance guarantees for 91 % of applications, while significantly improving utilization. In comparison, heterogeneity-oblivious, interference-oblivious and least-loaded schedulers only provide similar guarantees for 14%, 11 % and 3 % of workloads. The differences are more striking in oversubscribed scenarios where resource efficiency is more critical.
Quasar: Resource-Efficient and QoS-Aware Cluster Management
"... Cloud computing promises flexibility and high performance for users and high cost-efficiency for operators. Neverthe-less, most cloud facilities operate at very low utilization, hurting both cost effectiveness and future scalability. We present Quasar, a cluster management system that increases reso ..."
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Cited by 23 (5 self)
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Cloud computing promises flexibility and high performance for users and high cost-efficiency for operators. Neverthe-less, most cloud facilities operate at very low utilization, hurting both cost effectiveness and future scalability. We present Quasar, a cluster management system that increases resource utilization while providing consistently high application performance. Quasar employs three tech-niques. First, it does not rely on resource reservations, which lead to underutilization as users do not necessarily understand workload dynamics and physical resource re-quirements of complex codebases. Instead, users express performance constraints for each workload, letting Quasar determine the right amount of resources to meet these con-straints at any point. Second, Quasar uses classification tech-niques to quickly and accurately determine the impact of the amount of resources (scale-out and scale-up), type of resources, and interference on performance for each work-load and dataset. Third, it uses the classification results to jointly perform resource allocation and assignment, quickly exploring the large space of options for an efficient way to pack workloads on available resources. Quasar monitors workload performance and adjusts resource allocation and assignment when needed. We evaluate Quasar over a wide range of workload scenarios, including combinations of dis-tributed analytics frameworks and low-latency, stateful ser-vices, both on a local cluster and a cluster of dedicated EC2 servers. At steady state, Quasar improves resource utiliza-tion by 47 % in the 200-server EC2 cluster, while meeting performance constraints for workloads of all types.
A Weighted Sum Technique for the Joint Optimization of Performance and Power Consumption in Data Centers
"... Abstract—With data centers, end-users can realize the pervasiveness of services that will be one day the cornerstone of our lives. However, data centers are often classified as computing systems that consume the most amounts of power. To circumvent such a problem, we propose a self-adaptive weighted ..."
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Cited by 15 (10 self)
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Abstract—With data centers, end-users can realize the pervasiveness of services that will be one day the cornerstone of our lives. However, data centers are often classified as computing systems that consume the most amounts of power. To circumvent such a problem, we propose a self-adaptive weighted sum methodology that jointly optimizes the performance and power consumption of any given data center. Compared to traditional methodologies for multi-objective optimization problems, the proposed self-adaptive weighted sum technique does not rely on a systematical change of weights during the optimization procedure. The proposed technique is compared with the greedy and LR heuristics for large-scale problems, and the optimal solution for small-scale problems implemented in LINDO. the experimental results revealed that the proposed selfadaptive weighted sum technique outperforms both of the heuristics and projects a competitive performance compared to the optimal solution. Keywords—Meta-heuristics, distributed systems, adaptive methods, resource allocation. I.
A goal programming approach for the joint optimization of energy consumption and response time in computational grids
- in Performance Computing and Communications Conference (IPCCC), 2009 IEEE 28th International. IEEE, 2009
"... Abstract—We study the multi-objective problem of mapping independent tasks onto a set of computational grid machines that simultaneously minimizes the energy consumption and response time (makespan) subject to the constraints of deadlines and architectural requirements. We propose an algorithm based ..."
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Cited by 15 (8 self)
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Abstract—We study the multi-objective problem of mapping independent tasks onto a set of computational grid machines that simultaneously minimizes the energy consumption and response time (makespan) subject to the constraints of deadlines and architectural requirements. We propose an algorithm based on goal programming that effectively converges to the compromised Pareto optimal solution. Compared to other traditional multi-objective optimization techniques that require identification of the Pareto frontier, goal programming directly converges to the compromised solution. Such a property makes goal programming a very efficient multi-objective optimization technique. Moreover, simulation results show that the proposed technique achieves superior performance compared to the greedy and linear relaxation heuristics, and competitive performance relative to the optimal solution implemented in LINDO for small-scale problems. Keywords-distributed systems; goal programming; optimization; energy-efficiency; I.
Whare-Map: Heterogeneity in “Homogeneous” Warehouse-Scale Computers
"... Modern “warehouse scale computers ” (WSCs) continue to be embraced as homogeneous computing platforms. However, due to frequent machine replacements and upgrades, modern WSCs are in fact composed of diverse commodity microarchitectures and machine configurations. Yet, current WSCs are architected wi ..."
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Cited by 14 (2 self)
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Modern “warehouse scale computers ” (WSCs) continue to be embraced as homogeneous computing platforms. However, due to frequent machine replacements and upgrades, modern WSCs are in fact composed of diverse commodity microarchitectures and machine configurations. Yet, current WSCs are architected with the assumption of homogeneity, leavingapotentiallysignificantperformanceopportunity unexplored. In this paper, we expose and quantify the performance impact ofthe“homogeneity assumption”for modernproduction WSCs using industry-strength large-scale web-service workloads. In addition, we argue for, and evaluate the benefits of, a heterogeneity-aware WSC using commercial web-service production workloads including Google’s websearch. We also identify key factors impacting the availableperformanceopportunitywhenexploitingheterogeneity and introduce a new metric, opportunity factor, to quantify an application’s sensitivity to the heterogeneity in a given WSC. To exploit heterogeneity in “homogeneous ” WSCs, we propose “Whare-Map, ” the WSC Heterogeneity Aware Mapper that leverages already in-place continuous profiling subsystems found in production environments. When employing “Whare-Map”, we observe a cluster-wide performance improvement of 15 % on average over heterogeneity– oblivious job placement and up to an 80 % improvement for web-service applications that are particularly sensitive to heterogeneity. 1.
Navigating heterogeneous processors with market mechanisms
- in HPCA ’13
"... Specialization of datacenter resources brings performance and energy improvements in response to the growing scale and diversity of cloud applications. Yet heterogeneous hardware adds complexity and volatility to latency-sensitive applications. A resource allocation mechanism that leverages architec ..."
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Cited by 14 (4 self)
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Specialization of datacenter resources brings performance and energy improvements in response to the growing scale and diversity of cloud applications. Yet heterogeneous hardware adds complexity and volatility to latency-sensitive applications. A resource allocation mechanism that leverages architectural principles can overcome both of these obstacles. We integrate research in heterogeneous architectures with recent advances in multi-agent systems. Embedding architec-tural insight into proxies that bid on behalf of applications, a market effectively allocates hardware to applications with diverse preferences and valuations. Exploring a space of het-erogeneous datacenter configurations, which mix server-class Xeon and mobile-class Atom processors, we find an optimal heterogeneous balance that improves both welfare and energy-efficiency. We further design and evaluate twelve design points along the Xeon-to-Atom spectrum, and find that a mix of three processor architectures achieves a 12 × reduction in response time violations relative to equal-power homogeneous systems. 1.
