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PerfGreen: Performance and Energy Aware Resource Provisioning for Heterogeneous Clouds
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Cloud computing)
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap. (Cloud computing)
2018 (Engelska)Ingår i: 2018 IEEE International Conference on Autonomic Computing (ICAC), 2018, s. 81-90Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Improving energy efficiency in a cloud environment is challenging because of poor energy proportionality, low resource utilization, interference as well as workload, application, and hardware dynamism. In this paper we present PerfGreen, a dynamic auto-tuning resource management system for improving energy efficiency with minimal performance impact in heterogeneous clouds. PerfGreen achieves this through a combination of admission control, scheduling, and online resource allocation methods with performance isolation and application priority techniques. Scheduling in PerfGreen is energy aware and power management capabilities such as CPU frequency adaptation and hard CPU power limiting are exploited. CPU scaling is combined with performance isolation techniques, including CPU pinning and quota enforcement, for prioritized virtual machines to improve energy efficiency. An evaluation based on our prototype implementation shows that PerfGreen with its energy-aware scheduler and resource allocator on average reduces energy usage by 53%, improves performance per watt by 64%, and server density by 25% while keeping performance deviations to a minimum.

Ort, förlag, år, upplaga, sidor
2018. s. 81-90
Serie
Proceedings of the International Conference on Autonomic Computing, ISSN 2474-0756
Nationell ämneskategori
Datorsystem
Identifikatorer
URN: urn:nbn:se:umu:diva-145925DOI: 10.1109/ICAC.2018.00018ISI: 000450120900009OAI: oai:DiVA.org:umu-145925DiVA, id: diva2:1192262
Konferens
15TH IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING (ICAC 2018), Trento, ITALY, SEP 03-07, 2018
Tillgänglig från: 2018-03-22 Skapad: 2018-03-22 Senast uppdaterad: 2019-01-07Bibliografiskt granskad
Ingår i avhandling
1. Energy-efficient cloud computing: autonomic resource provisioning for datacenters
Öppna denna publikation i ny flik eller fönster >>Energy-efficient cloud computing: autonomic resource provisioning for datacenters
2018 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Energy efficiency has become an increasingly important concern in data centers because of issues associated with energy consumption, such as capital costs, operating expenses, and environmental impact. While energy loss due to suboptimal use of facilities and non-IT equipment has largely been reduced through the use of best-practice technologies, addressing energy wastage in IT equipment still requires the design and implementation of energy-aware resource management systems. This thesis focuses on the development of resource allocation methods to improve energy efficiency in data centers. The thesis employs three approaches to improve efficiency for optimized power and performance: scaling virtual machine (VM) and server processing capabilities to reduce energy consumption; improving resource usage through workload consolidation; and exploiting resource heterogeneity.

To achieve these goals, the first part of the thesis proposes models, algorithms, and techniques that reduce energy usage through the use of VM scaling, VM sizing for CPU and memory, CPU frequency adaptation, as well as hardware power capping for server-level resource allocation. The proposed online performance and power models capture system behavior while adapting to changes in the underlying infrastructure. Based on these models, the thesis proposes controllers that dynamically determine power-efficient resource allocations while minimizing performance penalty.

These methods are then extended to support resource overbooking and workload consolidation to improve resource utilization and energy efficiency across the cluster or data center. In order to cater for different performance requirements among collocated applications, such as latency-sensitive services and batch jobs, the controllers apply service differentiation among prioritized VMs and performance isolation techniques, including CPU pinning, quota enforcement, and online resource tuning.

This thesis also considers resource heterogeneity and proposes heterogeneousaware scheduling techniques to improve energy efficiency by integrating hardware accelerators (in this case FPGAs) and exploiting differences in energy footprint of different servers. In addition, the thesis provides a comprehensive study of the overheads associated with a number of virtualization platforms in order to understand the trade-offs provided by the latest technological advances and to make the best resource allocation decisions accordingly. The proposed methods in this thesis are evaluated by implementing prototypes on real testbeds and conducting experiments using real workload data taken from production systems and synthetic workload data that we generated. Our evaluation results demonstrate that the proposed approaches provide improved energy management of resources in virtualized data centers.

Ort, förlag, år, upplaga, sidor
Umeå: Umeå University, 2018. s. 63
Serie
Report / UMINF, ISSN 0348-0542 ; 18.05
Nyckelord
Cloud computing, datacenter, energy-efficiency, performance management, virtualization
Nationell ämneskategori
Datorsystem
Identifikatorer
urn:nbn:se:umu:diva-145926 (URN)978-91-7601-862-0 (ISBN)
Disputation
2018-04-16, MA121, MIT-building, Umeå, 10:15 (Engelska)
Opponent
Handledare
Tillgänglig från: 2018-03-26 Skapad: 2018-03-22 Senast uppdaterad: 2018-06-09Bibliografiskt granskad

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