Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems

Sharma, Naveen; Gummeson, Jeremy; Irwin, David; Shenoy, Prashant

doi:10.1109/secon.2010.5508260

Cited by 141 publications

(95 citation statements)

References 20 publications

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“…From this figure, we can notice that the solar radiation data for every day follows a quadratic relation to the time of the day. As it is also suggested in [28], we can take advantage of this characteristic in order to formulate a radiation model for every month by employing quadratic fitting. To that end, the power H generated at a solar panel with surface A m 2 can be described by…”

Section: A Solar Harvestingmentioning

confidence: 99%

Connectivity Analysis in Clustered Wireless Sensor Networks Powered by Solar Energy

Mekikis

Kartsakli

Antonopoulos

et al. 2018

IEEE Trans. Wireless Commun.

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Emerging 5G communication paradigms, such as machine-type communication, have triggered an explosion in ad-hoc applications that require connectivity among the nodes of wireless networks.Ensuring a reliable network operation under fading conditions is not straightforward, as the transmission schemes and the network topology, i.e., uniform or clustered deployments, affect the performance and should be taken into account. Moreover, as the number of nodes increases, exploiting natural energy sources and wireless energy harvesting (WEH) could be the key to the elimination of maintenance costs, while also boosting immensely the network lifetime. In this way, zero-energy wireless-powered sensor networks (WPSNs) could be achieved, if all components are powered by green sources. Hence, designing accurate mathematical models that capture the network behavior under these circumstances is necessary to provide a deeper comprehension of such networks. In this paper, we provide an analytical model for the connectivity in a large-scale zero-energy clustered WPSN under two common transmission schemes, namely unicast and broadcast. The sensors are WEH-enabled, while the network components are solar-powered and employ a novel energy allocation algorithm. In our results, we evaluate the tradeoffs among the various scenarios via extensive simulations and identify the conditions that yield a fully connected zero-energy WPSN.

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Section: A Solar Harvestingmentioning

confidence: 99%

Connectivity Analysis in Clustered Wireless Sensor Networks Powered by Solar Energy

Mekikis

Kartsakli

Antonopoulos

et al. 2018

IEEE Trans. Wireless Commun.

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“…Cloud resources usage can be predicted for a given Cloud by using Extreme Learning Machine algorithm on VM usage traces and user behavior (Ismaeel and Miri, 2016). Sharma et al present a prediction model for green energy availability (Sharma et al, 2010). The model is able to predict next day energy harvesting based on weather forecasts.…”

Section: Related Workmentioning

confidence: 99%

A CO2 Emissions Accounting Framework with Market-based Incentives for Cloud Infrastructures

Margery

Guyon²,

Orgerie

et al. 2017

Proceedings of the 6th International Conference on Smart Cities and Green ICT Systems

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Abstract:CO2 emissions related to Cloud computing reach nowadays worrying levels, without any reduction in sight. Often, Cloud users, asking for virtual machines, are not aware of such emissions which concern the entire Cloud infrastructures and are thus difficult to split into the actual resources utilization, such as virtual machines. We propose a CO2 emissions accounting framework giving flexibility to the Cloud providers, predictability to the users and allocating all the carbon costs to the users. This paper shows the architecture of our accounting framework and ideas on how to practically implement it.

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“…The cost of brown energy comes from the contract that each DC has with the local power supply company, which varies with the time of day. Finally, the amount of green energy that will be likely available in the next period can be predicted using historical data and weather forecast [21]. Then, each local resource manager migrates VMs sending its data to some remote DC.…”

Section: 2mentioning

confidence: 99%

Orchestrating connectivity services to support elastic operations in datacenter federations

2015

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Abstract-Datacenter federations are able to manage appropriately the green energy resources available in each datacenter (DC) thanks to their geographically distributed infrastructure, thus reducing energy expenditure. Scheduling algorithms can compute virtual machine migration, transferring huge amounts of raw data from one DC to another to minimize operational costs and ensuring a certain Quality of Experience (QoE). Because green energy availability greatly depends on weather conditions, in this work we present a statistical model to improve green solar energy availability estimation accuracy and we use it in a mixed integer linear programming (MILP) formulation to compute optimal virtual machine placement. Optical connections can be used to provide connectivity services of enough capacity to support those migrations. In particular, elastic optical networks can provide connections with multi-granular bitrate, which can be adapted on demand. DC resource managers can request optical connections and control their capacity. However, that scheme involves the resource managers to implement algorithms and interfaces to deal with network specifics and complexity. To solve that issue, in this paper we propose coordinating transfer-based inter-DC connectivity services; inter-DC connectivity is requested in terms of volume of data and completion time. We analyze cost savings when each connectivity model is applied in a DC federation. For the sake of a compelling analysis, exhaustive simulation experiments are carried out considering realistic scenarios. Results show that the notificationbased model can save up to 20% of energy costs and more than 40% of communication costs in the evaluated scenarios.Keywords: Federated datacenters, Energy costs minimization, Inter-datacenter networks. INTRODUCTIONThe huge energy consumption of datacenters (DC) requires an elastic resource management, e.g. by turning servers off when they are not used or turning them on to satisfy increments in the demand. Thanks to virtualization, jobs (e.g., web applications) can be encapsulated in virtual machines (VM) and run in the most proper server according to their performance goals. The local resource manager can migrate VMs from one server to another looking for reducing energy consumption while ensuring the committed quality of experience (QoE) [1]. In that regard, the live-migration technique allows migrating VMs from one server to another without stopping them resulting in reduced downtimes. Large Internet companies, such as Google, have their own IT infrastructures consisting in a number of large DCs placed in geographically diverse locations to guarantee the appropriate QoE to users; DCs are interconnected through a wide area network [2]. Using such infrastructure, workloads can be moved among DCs to take advantage of reduced energy cost during off-peak energy periods in some locations while using green energy when it is available in some other locations. Servers are turned off when they are not used, thus minimizing their energy expendit...

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Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems

Cited by 141 publications

References 20 publications

Connectivity Analysis in Clustered Wireless Sensor Networks Powered by Solar Energy

Connectivity Analysis in Clustered Wireless Sensor Networks Powered by Solar Energy

A CO2 Emissions Accounting Framework with Market-based Incentives for Cloud Infrastructures

Orchestrating connectivity services to support elastic operations in datacenter federations

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