On Workload Characterization of Relational Database Environments
IEEE Transactions on Software Engineering
Dynamic Voltage Scaling with Links for Power Optimization of Interconnection Networks
HPCA '03 Proceedings of the 9th International Symposium on High-Performance Computer Architecture
Realistic Load Testing ofWeb Applications
CSMR '06 Proceedings of the Conference on Software Maintenance and Reengineering
Live migration of virtual machines
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
VirtualPower: coordinated power management in virtualized enterprise systems
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
Synthesizing client load models for performance engineering via web crawling
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
No "power" struggles: coordinated multi-level power management for the data center
Proceedings of the 13th international conference on Architectural support for programming languages and operating systems
Energy management for hypervisor-based virtual machines
ATC'07 2007 USENIX Annual Technical Conference on Proceedings of the USENIX Annual Technical Conference
Power-aware dynamic placement of HPC applications
Proceedings of the 22nd annual international conference on Supercomputing
The cost of a cloud: research problems in data center networks
ACM SIGCOMM Computer Communication Review
GreenCloud: a new architecture for green data center
ICAC-INDST '09 Proceedings of the 6th international conference industry session on Autonomic computing and communications industry session
Designing and evaluating an energy efficient Cloud
The Journal of Supercomputing
Virtual machine power metering and provisioning
Proceedings of the 1st ACM symposium on Cloud computing
WattApp: an application aware power meter for shared data centers
Proceedings of the 7th international conference on Autonomic computing
Profiling Energy Consumption of VMs for Green Cloud Computing
DASC '11 Proceedings of the 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing
Characterizing Power and Energy Usage in Cloud Computing Systems
CLOUDCOM '11 Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science
Energy-aware service execution
LCN '11 Proceedings of the 2011 IEEE 36th Conference on Local Computer Networks
Energy efficient utilization of resources in cloud computing systems
The Journal of Supercomputing
CLOUD '12 Proceedings of the 2012 IEEE Fifth International Conference on Cloud Computing
SRDS '12 Proceedings of the 2012 IEEE 31st Symposium on Reliable Distributed Systems
Experimental analysis of task-based energy consumption in cloud computing systems
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
A Highly Practical Approach toward Achieving Minimum Data Sets Storage Cost in the Cloud
IEEE Transactions on Parallel and Distributed Systems
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In cloud environments, IT solutions are delivered to users via shared infrastructure. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A key objective of cloud providers is thus to develop resource provisioning and management solutions at minimum energy consumption while still guaranteeing Service Level Agreements (SLAs). However, a thorough understanding of both system performance and energy consumption patterns in complex cloud systems is imperative to achieve a balance of energy efficiency and acceptable performance. In this paper, we present StressCloud, a performance and energy consumption analysis tool for cloud systems. StressCloud can automatically generate load tests and profile system performance and energy consumption data. Using StressCloud, we have conducted extensive experiments to profile and analyse system performance and energy consumption with different types and mixes of runtime tasks. We collected fine-grained energy consumption and performance data with different resource allocation strategies, system configurations and workloads. The experimental results show the correlation coefficients of energy consumption, system resource allocation strategies and workload, as well as the performance of the cloud applications. Our results can be used to guide the design and deployment of cloud applications to balance energy and performance requirements.