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Power and Performance Optimization in FPGA-accelerated Clouds
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.ORCID-id: 0000-0002-3166-506X
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
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2018 (Engelska)Ingår i: Concurrency and Computation, ISSN 1532-0626, E-ISSN 1532-0634, Vol. 30, nr 18, artikel-id e4526Artikel i tidskrift (Övrigt vetenskapligt) Published
Abstract [en]

Energy management has become increasingly necessary in data centers to address all energy-related costs, including capital costs, operating expenses, and environmental impacts. Heterogeneous systems with mixed hardware architectures provide both throughput and processing efficiency for different specialized application types and thus have a potential for significant energy savings. However, the presence of multiple and different processing elements increases the complexity of resource assignment. In this paper, we propose a system for efficient resource management in heterogeneous clouds. The proposed approach maps applications' requirement to different resources reducing power usage with minimum impact on performance. A technique that combines the scheduling of custom hardware accelerators, in our case, Field-Programmable Gate Arrays (FPGAs) and optimized resource allocation technique for commodity servers, is proposed. We consider an energy-aware scheduling technique that uses both the applications' performance and their deadlines to control the assignment of FPGAs to applications that would consume the most energy. Once the scheduler has performed the mapping between a VM and an FPGA, an optimizer handles the remaining VMs in the server, using vertical scaling and CPU frequency adaptation to reduce energy consumption while maintaining the required performance. Our evaluation using interactive and data-intensive applications compare the effectiveness of the proposed solution in energy savings as well as maintaining applications performance, obtaining up to a 32% improvement in the performance-energy ratio on a mix of multimedia and e-commerce applications.

Ort, förlag, år, upplaga, sidor
John Wiley & Sons, 2018. Vol. 30, nr 18, artikel-id e4526
Nyckelord [en]
cloud computing, energy efficiency, FPGA-aware
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:umu:diva-121092DOI: 10.1002/cpe.4526ISI: 000442575600010OAI: oai:DiVA.org:umu-121092DiVA, id: diva2:930990
Forskningsfinansiär
VetenskapsrådetTillgänglig från: 2016-05-26 Skapad: 2016-05-26 Senast uppdaterad: 2019-01-15Bibliografiskt granskad
Ingår i avhandling
1. Energy-efficient resource provisioning for cloud data centers
Öppna denna publikation i ny flik eller fönster >>Energy-efficient resource provisioning for cloud data centers
2016 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Energy efficiency has become a fundamental concern in data centers, raising issues to all energy-related costs, including capital costs, operating expenses, and environmental impact. Energy inefficiency is mainly caused by unoptimized use of energy by sub-components of these data centers. For example, energy can be lost due to transport and conversion, cooling, and lightning. Energy can be wasted while running an idle server or when using unoptimized functions to perform a task. Addressing this problem as a whole requires redesigning data centers, rethinking components, and implementing energy-aware algorithms for data center operation. As one step towards achieving this goal, this thesis focuses on the development of resource allocation algorithms to improve the energy efficiency of servers in virtualized data centers. The thesis proposes models, techniques, and algorithms to improve data center resource efficiency for optimized power and performance. We present approaches that takes advantage of horizontal scaling, vertical scaling, CPU frequency scaling, and the scheduling of FPGAs to reduce the power consumption of servers while meeting performance requirements of applications. We design online performance and power models to capture system behaviour while adapting to changes in the underlying infrastructure. Based on these models, we propose controllers that dynamically determine power-efficient resource allocations. We also devise optimization strategies for colocated applications and evaluate their suitability in a number of scenarios. The proposed strategies simplify the handling of trade-offs between power minimization and meeting performance targets. We also consider fluctuations in resource allocation in decision making. Additionally, we propose a scheduling algorithm for the use of custom hardware accelerators, FPGAs, and their integration to data centers for the purpose of increasing processing and energy efficiency. Our evaluation results demonstrate that our proposed approaches provide improved energy-efficient management of resources in virtualized data centers.

Ort, förlag, år, upplaga, sidor
Umeå: Department of Computing Science, Umeå University, 2016. s. 24
Serie
UMINF, ISSN 0348-0542 ; 16.05
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
urn:nbn:se:umu:diva-121093 (URN)978-91-7601-433-2 (ISBN)
Handledare
Tillgänglig från: 2016-05-26 Skapad: 2016-05-26 Senast uppdaterad: 2018-06-07Bibliografiskt granskad
2. 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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