Stratus: Load Balancing the Cloud for Carbon Emissions Control

Doyle, Joseph; Shorten, Robert; O’Mahony, Donal

doi:10.1109/tcc.2013.4

Cited by 78 publications

(54 citation statements)

References 31 publications

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“…Soni et al [10] addressed workload as a multiobjective problem comprising of number of tasks where each task must be finished within its deadline time. Authors have taken three objectives-to balance Load, maintaining request priority and minimizing the execution error.…”

Section: A Time and Costmentioning

confidence: 99%

“…Their results achieved reduction of 26.41% energy consumption as compare standard PALB algorithm by reducing the number of power-on machines. J. Doyle et al [10] observed the challenge of receiving dynamic requests for cloud services across the globe, proposed the Stratus system based on Vornoi partitions. Their model details and minimizes the carbon emissions (carbon intensity-g/KWh), electricity cost, cooling cost and average job time taken for execution.…”

Section: Energy Efficiencymentioning

confidence: 99%

See 1 more Smart Citation

Optimization Techniques for Resource Provisioning and Load Balancing in Cloud Environment: A Review

Kaur¹,

Kaur²,

Singh³

2017

IJIEEB

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Abstract-Cloud computing is an emerging technology which provides unlimited access of versatile resources to users. The multifaceted and dynamic aspects of cloud computing require efficient and optimized techniques for resource provisioning and load balancing. Cloud monitoring is required identifying overutilized and underutilized of physical machines which hosting Virtual Machines (VMs). Load balancing is necessary for efficient and effective utilization of resources. Most of the authors have taken the objective to reduce the makespan for executing requests on multiple VMs. In this paper, a thorough review on scheduling and load balancing techniques has been done and different techniques have been analyzed on the basis of SLA Violations, CPU utilization, energy consumption and cost parameters.

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Section: A Time and Costmentioning

confidence: 99%

Section: Energy Efficiencymentioning

confidence: 99%

Optimization Techniques for Resource Provisioning and Load Balancing in Cloud Environment: A Review

Kaur¹,

Kaur²,

Singh³

2017

IJIEEB

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“…Existing research addresses energy cost optimization [77,96,97,125], and joint optimization of energy cost and carbon footprint of data centers [39,46,69,78,100,127]. The existing cost-aware workload management solutions lack a mathematical analysis for the optimality of the solutions.…”

Section: Application Of the Solutionsmentioning

confidence: 99%

“…Carbon capping in data centers has recently received attention both in industry [3] and in literature [39,46,69,78,100,127]. Large data center operators took initiative to utilize renewable energy in data centers.…”

Section: Carbon Cappingmentioning

confidence: 99%

Sustainable Cloud Computing

Domdouzis

2015

Green Information Technology

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Energy consumption of the data centers worldwide is rapidly growing fueled by everincreasing demand for Cloud computing applications ranging from social networking to e-commerce. Understandably, ensuring energy-efficiency and sustainability of Cloud data centers without compromising performance is important for both economic and environmental reasons. In order to achieve these objectives, this dissertation develops a cyber-physical multi-tier server and workload management architecture which operates at the local and the global (geo-distributed) data center level.This dissertation devises optimization frameworks for each tier to optimize energy consumption, energy cost and carbon footprint of the data centers. The proposed solutions are aware of various energy management tradeoffs that manifest due to the cyber-physical interactions in data centers, while providing provable guarantee on the solutions' computation efficiency and energy/cost efficiency. The local data center level energy management takes into account the impact of server consolidation on the cooling energy, avoids cooling-computing power tradeoff, and optimizes the total energy (computing and cooling energy) considering the data centers' technology trends (servers' power proportionality and the cooling power efficiency). The global data center level cost management explores the diversity of the data centers to minimize the utility cost while satisfying the carbon cap requirement of the Cloud and while dealing with the adversity of the prediction error on the data center parameters. Finally, the synergy of the local and the global data center energy and cost optimization is shown to help towards achieving carbon neutrality (net-zero) in a cost efficient manner.

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“…Often load-balancing policies consider web server systems as a target [11,26], where one of the most important result is to bound the maximum response time that the clients are exposed to [19]. Load-balancing strategies can be guided by many different purposes, for example geographical [2,33], driven by the electricity price to reduce the datacenter operation cost [15], or specifically designed for cloud applications [5,23,24].…”

Section: Related Workmentioning

confidence: 99%

Control-theoretical load-balancing for cloud applications with brownout

Dürango

Dellkrantz

Maggio

et al. 2014

53rd IEEE Conference on Decision and Control

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Abstract-Cloud applications are often subject to unexpected events like flash crowds and hardware failures. Without a predictable behaviour, users may abandon an unresponsive application. This problem has been partially solved on two separate fronts: first, by adding a self-adaptive feature called brownout inside cloud applications to bound response times by modulating user experience, and, second, by introducing replicas -copies of the applications having the same functionalities -for redundancy and adding a load-balancer to direct incoming traffic.However, existing load-balancing strategies interfere with brownout self-adaptivity. Load-balancers are often based on response times, that are already controlled by the self-adaptive features of the application, hence they are not a good indicator of how well a replica is performing.In this paper, we present novel load-balancing strategies, specifically designed to support brownout applications. They base their decision not on response time, but on user experience degradation. We implemented our strategies in a selfadaptive application simulator, together with some state-of-theart solutions. Results obtained in multiple scenarios show that the proposed strategies bring significant improvements when compared to the state-of-the-art ones.

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Stratus: Load Balancing the Cloud for Carbon Emissions Control

Cited by 78 publications

References 31 publications

Optimization Techniques for Resource Provisioning and Load Balancing in Cloud Environment: A Review

Optimization Techniques for Resource Provisioning and Load Balancing in Cloud Environment: A Review

Sustainable Cloud Computing

Control-theoretical load-balancing for cloud applications with brownout

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