An Algorithm-Centric Energy-Aware Design Methodology

Hajj, Hazem; El-Hajj, Wassim; Dabbagh, Mehiar; Arabi, T.

doi:10.1109/tvlsi.2013.2289906

Cited by 14 publications

(5 citation statements)

References 17 publications

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“…In [12] the authors propose a model for occupancy detection to build demand-driven heating. In [13], a KNN with the inclusion of a kernel function to study the energy consumption profile of a building that might be affected by external and contextual factors. The authors in [14] applied a multiagent system based on Support Vectors appliances (SVM), to optimize the HVAc system by detecting the number of occupants in a room getting results of over 90% accuracy detection.…”

Section: Previous Workmentioning

confidence: 99%

Energy Consumption Patterns and Inter-Appliance Associations using Data Mining Techniques

et al. 2022

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In this paper, we propose to model the behaviors of Moroccan consumers in terms of energy consumption in different Moroccan buildings using the open MORED (A Moroccan Building Electricity Dataset) dataset as a data warehouse. The techniques used are Machine Learning Algorithms and Data Mining Techniques. The results obtained in this paper allow us to understand the behavior of a Moroccan consumer in terms of energy consumption and the use of appliances in the home. Inter-Appliance Association and Peak Hours detected in this study will be used later to develop an Energy Management System specifically for a Moroccan building. This can lay the foundation for efficient Energy Demand Management while improving end-user participation.

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Section: Previous Workmentioning

confidence: 99%

Energy Consumption Patterns and Inter-Appliance Associations using Data Mining Techniques

et al. 2022

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“…Due to space limitation, we only cover here OS-layer related approaches, as they involve some workload prediction relevant to our work. Readers interested in the work done on the other layers may refer to [9].…”

Section: A Server Level Techniquesmentioning

confidence: 99%

Energy-Efficient Resource Allocation and Provisioning Framework for Cloud Data Centers

Dabbagh

Hamdaoui

Guizani

et al. 2015

IEEE Trans. Netw. Serv. Manage.

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130

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Energy efficiency has recently become a major issue in large data centers due to financial and environmental concerns. This paper proposes an integrated energy-aware resource provisioning framework for cloud data centers. The proposed framework: i) predicts the number of virtual machine (VM) requests, to be arriving at cloud data centers in the near future, along with the amount of CPU and memory resources associated with each of these requests, ii) provides accurate estimations of the number of physical machines (PMs) that cloud data centers need in order to serve their clients, and iii) reduces energy consumption of cloud data centers by putting to sleep unneeded PMs. Our framework is evaluated using real Google traces collected over a 29-day period from a Google cluster containing over 12,500 PMs. These evaluations show that our proposed energy-aware resource provisioning framework makes substantial energy savings.

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“…We limit our focus to the techniques that manage entire cloud centers and that rely on virtualization capabilities to do so rather than on those designed for saving energy in a single server, as the latter techniques are very general and are not specific to cloud centers. Readers who are interested in power management techniques at the single server level may refer to [8] for more details.…”

Section: Energy Conservation Techniquesmentioning

confidence: 99%

Toward energy-efficient cloud computing: Prediction, consolidation, and overcommitment

et al. 2015

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nergy efficiency has become a major concern in large data centers. In the United States, data centers consumed about 1.5 percent of the total generated electricity in 2006, an amount that is equivalent to the annual energy consumption of 5.8 million households [1]. In U.S. dollars, this translates into power costs of 4.5 billion per year. Data center owners, as a result, are eager now more than ever to save energy in any way they can in order to reduce their operating costs.There are also increasing environmental concerns that also call for the reduction of the amounts of energy consumed by these large data centers, especially after reporting that information and communication technology (ICT) itself contributes about 2 percent to global carbon emissions [2]. These energy costs and carbon footprints are expected to increase rapidly in the future as data centers are anticipated to grow significantly both in size and in numbers due to the increasing popularity of their offered services. All of these factors have alerted industry, academia, and government agencies to the importance of developing and implementing effective solutions and techniques that can reduce energy consumption in data centers.Cloud data centers are examples of such large data centers whose offered services are gaining in popularity, especially with the recently witnessed increasing reliance of mobile devices on cloud services [3,4]. Thus, our focus in this article is on energy consumption efficiency in cloud data centers. We start the article by introducing the cloud paradigm. We then explain the new challenges and opportunities that arise when trying to save energy in cloud centers. We then describe the most popular techniques and solutions that can be adopted by cloud data centers to save energy. Finally, we provide some conclusions and directions for future work. The Cloud ParadigmIn the cloud paradigm, a cloud provider company owns a cloud center that consists of a large number of servers, also called physical machines (PMs). These PMs are grouped into multiple management units called clusters, where each cluster manages and controls a large number of PMs, typically in the order of thousands. A cluster can be homogeneous, meaning that all of its managed PMs are identical, or it can be heterogeneous, meaning that it manages PMs with different resource capacities and capabilities.Cloud providers offer these computing resources as a service for their clients and charge them based on their usage in a pay-as-you-go fashion. Cloud clients submit requests to the cloud provider, specifying the amount of resources they need to perform certain tasks. Upon receiving a client request, the cloud provider scheduler creates a virtual machine (VM), allocates the requested resources to it, chooses one of the clusters to host the VM, and assigns the VM to one of the PMs within that cluster. Client requests are thus also referred to as VM requests. After this allocation process takes place, the client can then use its allocated resources to perform its tasks. ...

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An Algorithm-Centric Energy-Aware Design Methodology

Cited by 14 publications

References 17 publications

Energy Consumption Patterns and Inter-Appliance Associations using Data Mining Techniques

Energy Consumption Patterns and Inter-Appliance Associations using Data Mining Techniques

Energy-Efficient Resource Allocation and Provisioning Framework for Cloud Data Centers

Toward energy-efficient cloud computing: Prediction, consolidation, and overcommitment

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