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Automated analysis of performance and energy consumption for cloud applications
- In ICPE
, 2014
"... 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 ..."
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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.
Analyzing the Impact of CPU Pinning and Partial CPU Loads on Performance and Energy Efficiency
"... Abstract—While workload colocation is a necessity to increase energy efficiency of contemporary multi-core hardware, it also increases the risk of performance anomalies due to workload interference. Pinning certain workloads to a subset of CPUs is a simple approach to increasing workload isolation, ..."
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Abstract—While workload colocation is a necessity to increase energy efficiency of contemporary multi-core hardware, it also increases the risk of performance anomalies due to workload interference. Pinning certain workloads to a subset of CPUs is a simple approach to increasing workload isolation, but its effect depends on workload type and system architecture. Apart from common sense guidelines, the effect of pinning has not been extensively studied so far. In this paper we study the impact of CPU pinning on performance interference and energy efficiency for pairs of colocated workloads. Besides various combinations of workloads, virtualiza-tion and resource isolation, we explore the effects of pinning depending on the level of background load. The presented results are based on more than 1000 exper-iments carried out on an Intel-based NUMA system, with all power management features enabled to reflect real-world settings. We find that less common CPU pinning configurations improve energy efficiency at par-tial background loads, indicating that systems hosting colocated workloads could benefit from dynamic CPU pinning based on CPU load and workload type. I.
An Experimental Methodology to Evaluate Energy Efficiency and Performance in an Enterprise Virtualized Environment
"... Computing servers generally have a narrow dynamic power range. For instance, even completely idle servers consume between 50 % and 70 % of their peak power. Since the us-age rate of the server has the main influence on its power consumption, energy-efficiency is achieved whenever the uti-lization of ..."
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Computing servers generally have a narrow dynamic power range. For instance, even completely idle servers consume between 50 % and 70 % of their peak power. Since the us-age rate of the server has the main influence on its power consumption, energy-efficiency is achieved whenever the uti-lization of the servers that are powered on reaches its peak. For this purpose, enterprises generally adopt the following technique: consolidate as many workloads as possible via virtualization in a minimum amount of servers (i.e. maxi-mize utilization) and power down the ones that remain idle (i.e. reduce power consumption). However, such approach can severely impact servers ’ performance and reliability. In this paper, we propose a methodology to determine the ideal values for power consumption and utilization for
StressCloud: A Tool for Analysing Performance and Energy Consumption of Cloud Applications
"... Abstract — Finding the best deployment configuration that maximises energy efficiency while guaranteeing system performance of cloud applications is an extremely challenging task. It requires the evaluation of system performance and energy consumption under a wide variety of realistic workloads and ..."
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Abstract — Finding the best deployment configuration that maximises energy efficiency while guaranteeing system performance of cloud applications is an extremely challenging task. It requires the evaluation of system performance and energy consumption under a wide variety of realistic workloads and deployment configurations. This paper demonstrates StressCloud, an automatic performance and energy consumption analysis tool for cloud applications in real-world cloud environments. StressCloud supports 1) the modelling of realistic cloud application workloads, 2) the automatic generation and running of load tests, and 3) the profiling of system performance and energy consumption. A demonstration video can be accessed at:
A survey on Energy-Aware Cloud
"... The Cloud computing has enabled businesses and individuals to utilise the potential infrastructure in the Cloud without dealing with the cost and complexities associated with large computations. It saves businesses the initial setup, updates and maintenance cost. And individuals are provided by the ..."
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The Cloud computing has enabled businesses and individuals to utilise the potential infrastructure in the Cloud without dealing with the cost and complexities associated with large computations. It saves businesses the initial setup, updates and maintenance cost. And individuals are provided by the physical resources they might need for a time they need them. They pay as they use the resources. Cloud computing has revolutionized the way processing is carried out. Cloud led to the establishment of large data centers that contribute in the energy consumed worldwide and consequently the carbon emission and environmental drawbacks. Green Cloud computing evolves around the development of algorithms that decreases the energy consumption and became an active research area. Green cloud strategies are proposed and tested via a broad range of assumptions. Surveying these strategies can identify the fitness of them in achieving the common objectives along with the energy consumption. We identified the way energy consumption is observed and what energy saving methods are applied. Based on that we present a taxonomy and analysis of their strength and weakness of the existing methods. Ultimately, regarding the result of the analysis, the challenges are discussed and trends for future research in green Cloud computing are identified.
Automating Performance and Energy Consumption Analysis for Cloud Applications
"... Abstract — In cloud environments, IT solutions are delivered to users via shared infrastructure, enabling cloud service providers to deploy applications as services according to user QoS (Quality of Service) requirements. One consequence of this cloud model is the huge amount of energy consumption a ..."
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Abstract — In cloud environments, IT solutions are delivered to users via shared infrastructure, enabling cloud service providers to deploy applications as services according to user QoS (Quality of Service) requirements. One consequence of this cloud model is the huge amount of energy consumption and significant carbon footprints caused by large cloud infrastructures. A key and common objective of cloud service providers is thus to develop cloud application deployment and management solutions with minimum energy consumption while guaranteeing performance and other QoS specified in Service Level Agreements (SLAs). However, finding the best deployment configuration that maximises energy efficiency while guaranteeing system performance is an extremely challenging task, which requires the evaluation of system performance and energy consumption under various workloads and deployment configurations. In order to simplify this process we have developed StressCloud, an automatic performance and energy consumption analysis tool for cloud applications in real-world cloud environments. StressCloud supports the modelling of realistic cloud application workloads, the automatic generation of load tests, and the profiling of system performance and energy consumption. We demonstrate the utility of StressCloud by analysing the performance and energy consumption of a cloud application under a broad range of different deployment configurations.
Teachers ’ Training and
"... Cloud computing is very common today due to its various characteristics and benefits. It is cost efficient and thus very popular, but has great impact on environment such as increase in pollution, climatic changes and shortage of energy resources. Green cloud computing is important issue at present ..."
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Cloud computing is very common today due to its various characteristics and benefits. It is cost efficient and thus very popular, but has great impact on environment such as increase in pollution, climatic changes and shortage of energy resources. Green cloud computing is important issue at present time. In this paper, the author have made critical observations of energy efficiency in all fields of cloud computing and briefly describe about various research that have been done for green cloud computing. The power and energy consumption of cloud services is also discussed.
A SURVEY ON ENERGY EFFICIENT WITH TASK CONSOLIDATION IN THE VIRTUALIZED CLOUD COMPUTING ENVIRONMENT
"... Cloud computing is a new model of computing that is widely used in today’s industry, organizations and society in information technology service delivery as a utility. It enables organizations to reduce operational expenditure and capital expenditure. However, cloud computing with underutilized reso ..."
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Cloud computing is a new model of computing that is widely used in today’s industry, organizations and society in information technology service delivery as a utility. It enables organizations to reduce operational expenditure and capital expenditure. However, cloud computing with underutilized resources still consumes an unacceptable amount of energy than fully utilized resource. Many techniques for optimizing energy consumption in virtualized cloud have been proposed. This paper surveys different energy efficient models with task consolidation in the virtualized cloud computing environment.
