Utility-function-driven energy-efficient cooling in data centers

Das, Rajarshi; Kephart, Jeffrey O.; Lenchner, Jonathan; Hamann, Hendrik F.

doi:10.1145/1809049.1809058

Cited by 32 publications

(23 citation statements)

References 16 publications

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“…Thus, the utility based approach captures the goal of the adaptation explicitly and searches for solutions that meet the goal. The utility based approach has been applied to a range of applications, from workflow scheduling on grids [8] to data center cooling [9], and here is applied to adaptive VM placement.…”

Section: Utility(a T) = Income(a T) − Energycost(a T)mentioning

confidence: 99%

“…In the original proposal for utility-based adaptation [5], a controller is responsible for selecting the collection of control parameters that maximize the utility function, using a utility calculator that implements a model of the environment. Utility functions have been applied in autonomic computing for: configuring the properties of application hosting environments such as web servers [5], for selecting between alternative providers of a service [24], for managing the physical environment within data centres [9], for allocating jobs within a collection of workflows to machines on a grid [8], for optimizing resource utilization for scientific applications [25], and for balancing the load of a collection of database queries over the nodes in a cluster [26]. As the designs of these applications have various features in common, a methodology has been proposed for the development of utility-based applications [27], which we follow in "Utility-based resource allocation" section.…”

Section: Utility-based Resource Allocationmentioning

confidence: 99%

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Optimizing virtual machine placement for energy and SLA in clouds using utility functions

Mosa

Paton

2016

J Cloud Comp

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Cloud computing provides on-demand access to a shared pool of computing resources, which enables organizations to outsource their IT infrastructure. Cloud providers are building data centers to handle the continuous increase in cloud users' demands. Consequently, these cloud data centers consume, and have the potential to waste, substantial amounts of energy. This energy consumption increases the operational cost and the CO 2 emissions. The goal of this paper is to develop an optimized energy and SLA-aware virtual machine (VM) placement strategy that dynamically assigns VMs to Physical Machines (PMs) in cloud data centers. This placement strategy co-optimizes energy consumption and service level agreement (SLA) violations. The proposed solution adopts utility functions to formulate the VM placement problem. A genetic algorithm searches the possible VMs-to-PMs assignments with a view to finding an assignment that maximizes utility. Simulation results using CloudSim show that the proposed utility-based approach reduced the average energy consumption by approximately 6 % and the overall SLA violations by more than 38 %, using fewer VM migrations and PM shutdowns, compared to a well-known heuristics-based approach.

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Section: Utility(a T) = Income(a T) − Energycost(a T)mentioning

confidence: 99%

Section: Utility-based Resource Allocationmentioning

confidence: 99%

Optimizing virtual machine placement for energy and SLA in clouds using utility functions

Mosa

Paton

2016

J Cloud Comp

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“…The work here does not propose any new scheduling algorithm for efficient utilisation of datacentre resources; however it uses basic resource allocation technique to model the performance of datacentre AMs in terms of the effectiveness of resource request and allocation management. Other research, e.g., [12] and [13] have proposed scheduling algorithms that optimise the performance of datacentres.…”

Section: B Datacentre Resource Request and Allocation Managementmentioning

confidence: 99%

Dead-zone logic in autonomic systems

Eze

Anthony

2014

2014 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)

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-Dead-Zone logic is a mechanism to prevent autonomic managers from unnecessary, inefficient and ineffective control brevity when the system is sufficiently close to its target state. It provides a natural and powerful framework for achieving dependable self-management in autonomic systems by enabling autonomic managers to smartly carry out a change (or adapt) only when it is safe and efficient to do so -within a particular (defined) safety margin. This paper explores and evaluates the performance impact of dead-zone logic in trustworthy autonomic computing. Using two case example scenarios, we present empirical analyses that demonstrate the effectiveness of dead-zone logic in achieving stability, dependability and trustworthiness in adaptive systems. Dynamic temperature target tracking and autonomic datacentre resource request and allocation management scenarios are used. Results show that dead-zone logic can significantly enhance the trustability of autonomic systems.

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“…The GCO collects monitoring data on energy consumption of a large computing center, and publishes them through the Grid Observatory portal www.grid-observatory.org. These data include the detailed monitoring of the processors and motherboards, as well as global site information, such as overall consumption and external temperature, as global optimization is a promising way of research [4]. A second barrier is making the collected data usable.…”

Section: Motivation and Goalsmentioning

confidence: 99%

The Green Computing Observatory

Germain-Renaud¹

2012

Proceedings of EGI Community Forum 2012 / EMI Second Technical Conference — PoS(EGICF12-EMITC2)

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The Green Computing Observatory (GCO) is a collaborative effort to provide the scientific community with a comprehensive set of traces of energy consumption of a production cluster. These traces include the detailed monitoring of the hardware and software, as well as global site information such as the overall consumption and overall cooling. The acquired data is transformed into an XML format built from a specifically designed ontology and published through the Grid Observatory website.

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Utility-function-driven energy-efficient cooling in data centers

Cited by 32 publications

References 16 publications

Optimizing virtual machine placement for energy and SLA in clouds using utility functions

Optimizing virtual machine placement for energy and SLA in clouds using utility functions

Dead-zone logic in autonomic systems

The Green Computing Observatory

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