Tiny computers located in end-user premises are becoming popular as local servers for Internet of... more Tiny computers located in end-user premises are becoming popular as local servers for Internet of Things (IoT) and Fog computing services. These highly distributed servers that can host and distribute content and applications in a peer-to-peer (P2P) fashion are known as nano data centers (nDCs). Despite the growing popularity of nano servers, their energy consumption is not well-investigated. To study energy consumption of nDCs, we propose and use flow-based and time-based energy consumption models for shared and unshared network equipment, respectively. To apply and validate these models, a set of measurements and experiments are performed to compare energy consumption of a service provided by nDCs and centralized data centers (DCs). A number of findings emerge from our study, including the factors in the system design that allow nDCs to consume less energy than its centralized counterpart. These include the type of access network attached to nano servers and nano server's time utilization (the ratio of the idle time to active time). Additionally, the type of applications running on nDCs and factors such as number of downloads, number of updates, and amount of preloaded copies of data influence the energy cost. Our results reveal that number of hops between a user and content has little impact on the total energy consumption compared to the above-mentioned factors. We show that nano servers in Fog computing can complement centralized DCs to serve certain applications, mostly IoT applications for which the source of data is in end-user premises, and lead to energy saving if the applications (or a part of them) are off-loadable from centralized DCs and run on nDCs.
—Cloud computing and cloud-based services are a rapidly growing sector of the expanding digital e... more —Cloud computing and cloud-based services are a rapidly growing sector of the expanding digital economy. Recent studies have suggested that processing a task in the cloud is more energy-efficient than processing the same task locally. However, these studies have generally ignored the network transport energy and the additional power consumed by end-user devices when accessing the cloud. In this paper, we develop a simple model to estimate the incremental power consumption involved in using interactive cloud services. We then apply our model to a representative cloud-based word processing application and observe from our measurements that the volume of traffic generated by a session of the application typically exceeds the amount of data keyed in by the user by more than a factor of 1000. This has important implications on the overall power consumption of the service. We provide insights into the reasons behind the observed traffic levels. Finally, we compare our estimates of the power consumption with performing the same task on a low-power consuming computer. Our study reveals that it is not always energy-wise to use the cloud. Performing certain tasks locally can be more energy-efficient than using the cloud.
—Interactive cloud computing and cloud-based applications are a rapidly growing sector of the exp... more —Interactive cloud computing and cloud-based applications are a rapidly growing sector of the expanding digital economy because they provide access to advanced computing and storage services via simple, compact personal devices. Recent studies have suggested that processing a task in the cloud is more energy-efficient than processing the same task locally. However, these studies have generally ignored the power consumption of the network and end-user devices when accessing the cloud. In this paper, we develop a power consumption model for interactive cloud applications that includes the power consumption of end-user devices and the influence of the applications on the power consumption of the various network elements along the path between the user and the cloud data centre. As examples, we apply our model to Google Drive and Microsoft Skydrive's word processing, presentation and spreadsheet interactive applications. We demonstrate via extensive packet-level traffic measurements that the volume of traffic generated by a session of the application vastly exceeds the amount of data keyed in by the user. This has important implications on the overall power consumption of the service. We show that using the cloud to perform certain tasks consumes more power (by a watt to 10 watts depending on the scenario) than performing the same tasks locally on a low-power consuming computer and a tablet.
A simple, generic measurement-based power consumption model is described and is shown to apply to... more A simple, generic measurement-based power consumption model is described and is shown to apply to equipment, networks and services. This model is used to construct power consumption estimates for a diverse range of network scenarios including customer premises equipment and access, edge and core networks and services provided over a network.
Energy consumption of nano data centers has recently been a topic of interest as they emerge as a... more Energy consumption of nano data centers has recently been a topic of interest as they emerge as a novel computing and storage platform. We present end-to-end energy consumption models for nano data centers and its centralized counterpart. To assess the energy consumption of nano and centralized data centers, we propose flowbased and time-based energy consumption models for shared and single user network equipment. To evaluate our models, a set of measurements and practical experiments are performed. Our results indicate that nano data centers might lead to energy savings depending on various factors such as location of nano servers, type of access network attached to nano servers, and the ratio of active time to idle time of nano servers. Thus, nano data centers can complement centralized ones and lead to savings energy if certain applications are off-loadable from centralized data centers.
Online social networks (OSNs) with their huge number of active users consume significant amount e... more Online social networks (OSNs) with their huge number of active users consume significant amount energy both in the data centers and in the transport network. Existing studies focus mainly on the energy consumption in the data centers and do not take into account the energy consumption during the transport of data between end-users and data centers. To indicate the amount of the neglected energy, this paper provides a comprehensive framework and a set of measurements for understanding the energy consumption of cloud applications such as photo sharing in social networks. A new energy model is developed to estimate the energy consumption of cloud applications and applied to sharing photos on Facebook, as an example. Our results indicate that the energy consumption involved in the network and end-user devices for photo sharing is approximately equal to 60% of the energy consumption of all Facebook data enters. Therefore, achieving an energy-efficient cloud service requires energy efficiency improvement in the transport network and end-user devices along with the related data centers.
Energy harvesters are used in today’s Wireless Sensor Networks (WSNs) to harvest energy from the ... more Energy harvesters are used in today’s Wireless Sensor Networks (WSNs) to harvest energy from the environment. Although an energy harvester can provide a supply source with a much greater lifetime than a battery, the amount of available energy for an energy harvesting system is a random variable. Furthermore, the proper management of energy harvesters has a considerable impact on reliability. It has been observed that cooperative error control mechanisms like Cooperative Automatic Repeat Request (C-ARQ) and Cooperative Hybrid ARQ (C-HARQ) can be used for improving the energy management and reliability in Energy Harvesting WSNs (EH-WSNs). Recently, the impact of C-ARC mechanism has been considered in an energy harvesting system (GAP4S), however, GAP4S is an EH-WSN powered by microwave with linear recharge rate and unbounded storage capacity. This work evaluates the effect of C-HARC using a new method of relay node selection in EH-WSNs which are powered by the solar energy and have variable recharge rates. Furthermore, a comparative study of C-ARQ and C-HARQ mechanisms, considering the impact of variations in transmission rate and packet error probability on the energy consumption, is provided. The results show that C-HARQ outperforms C-ARQ under the same conditions and the energy consumption of a relay node in C-HARQ is 50% less than the one in C-ARQ.
Two important objectives in wireless sensor networks are reliability and reducing energy consumpt... more Two important objectives in wireless sensor networks are reliability and reducing energy consumption. Hence, overcoming energy constraints and utilizing error control schemes such as Automatic Repeat Request (ARQ) and Forward Error Correction (FEC) are necessary to improve the energy efficiency and reliability. However, these two concerns are at odds, so there is a trade-off between them. Considering this point, the impact of various error control schemes on these objectives and the trade-off between them has been considered in Bluetooth networks recently. However, all these works consider ideal assumptions (e.g., perfect error detection) only. This work evaluates the energy-efficiency of Bluetooth error control schemes in Rayleigh fading channels taking into consideration both ideal assumptions and residual error probability of the CRC code in ARQ schemes. A comparative analysis of coding techniques using different BCH codes on the AUX1 packet is provided. In addition, the impact of variations in number of hops and SNR on the effectiveness of proposed coding techniques is analyzed through simulation. This analysis provides information that help network designers to choose suitable packet types and coding techniques for Bluetooth networks depending on the network situation.
To scavenge the energy from the environment and extend the network lifetime, some wireless sensor... more To scavenge the energy from the environment and extend the network lifetime, some wireless sensor networks (WSNs) have been equipped with energy harvesters recently. However, the variable amount of environmental energy can affect the reliability of energy harvesting wireless sensor networks (EH-WSNs). In addition, data transmission over a wireless media is vulnerable. Hence, utilizing suitable error control schemes are necessary to improve the reliability. Regarding this point, Automatic Repeat Request (ARQ) and Cooperative ARQ (C-ARQ) schemes are applied in this generation of WSNs. Conventional ARQ as well as C-ARQ scheme are considered and examined through simulation. A comparative analysis of these two schemes in terms of energy consumption, energy efficiency and reliability is provided. This analysis shows that C-ARQ is the more appropriate error control scheme in EH-WSNs to manage the variable environmental energy and improve the reliability and energy efficiency.
Tiny computers located in end-user premises are becoming popular as local servers for Internet of... more Tiny computers located in end-user premises are becoming popular as local servers for Internet of Things (IoT) and Fog computing services. These highly distributed servers that can host and distribute content and applications in a peer-to-peer (P2P) fashion are known as nano data centers (nDCs). Despite the growing popularity of nano servers, their energy consumption is not well-investigated. To study energy consumption of nDCs, we propose and use flow-based and time-based energy consumption models for shared and unshared network equipment, respectively. To apply and validate these models, a set of measurements and experiments are performed to compare energy consumption of a service provided by nDCs and centralized data centers (DCs). A number of findings emerge from our study, including the factors in the system design that allow nDCs to consume less energy than its centralized counterpart. These include the type of access network attached to nano servers and nano server's time utilization (the ratio of the idle time to active time). Additionally, the type of applications running on nDCs and factors such as number of downloads, number of updates, and amount of preloaded copies of data influence the energy cost. Our results reveal that number of hops between a user and content has little impact on the total energy consumption compared to the above-mentioned factors. We show that nano servers in Fog computing can complement centralized DCs to serve certain applications, mostly IoT applications for which the source of data is in end-user premises, and lead to energy saving if the applications (or a part of them) are off-loadable from centralized DCs and run on nDCs.
—Cloud computing and cloud-based services are a rapidly growing sector of the expanding digital e... more —Cloud computing and cloud-based services are a rapidly growing sector of the expanding digital economy. Recent studies have suggested that processing a task in the cloud is more energy-efficient than processing the same task locally. However, these studies have generally ignored the network transport energy and the additional power consumed by end-user devices when accessing the cloud. In this paper, we develop a simple model to estimate the incremental power consumption involved in using interactive cloud services. We then apply our model to a representative cloud-based word processing application and observe from our measurements that the volume of traffic generated by a session of the application typically exceeds the amount of data keyed in by the user by more than a factor of 1000. This has important implications on the overall power consumption of the service. We provide insights into the reasons behind the observed traffic levels. Finally, we compare our estimates of the power consumption with performing the same task on a low-power consuming computer. Our study reveals that it is not always energy-wise to use the cloud. Performing certain tasks locally can be more energy-efficient than using the cloud.
—Interactive cloud computing and cloud-based applications are a rapidly growing sector of the exp... more —Interactive cloud computing and cloud-based applications are a rapidly growing sector of the expanding digital economy because they provide access to advanced computing and storage services via simple, compact personal devices. Recent studies have suggested that processing a task in the cloud is more energy-efficient than processing the same task locally. However, these studies have generally ignored the power consumption of the network and end-user devices when accessing the cloud. In this paper, we develop a power consumption model for interactive cloud applications that includes the power consumption of end-user devices and the influence of the applications on the power consumption of the various network elements along the path between the user and the cloud data centre. As examples, we apply our model to Google Drive and Microsoft Skydrive's word processing, presentation and spreadsheet interactive applications. We demonstrate via extensive packet-level traffic measurements that the volume of traffic generated by a session of the application vastly exceeds the amount of data keyed in by the user. This has important implications on the overall power consumption of the service. We show that using the cloud to perform certain tasks consumes more power (by a watt to 10 watts depending on the scenario) than performing the same tasks locally on a low-power consuming computer and a tablet.
A simple, generic measurement-based power consumption model is described and is shown to apply to... more A simple, generic measurement-based power consumption model is described and is shown to apply to equipment, networks and services. This model is used to construct power consumption estimates for a diverse range of network scenarios including customer premises equipment and access, edge and core networks and services provided over a network.
Energy consumption of nano data centers has recently been a topic of interest as they emerge as a... more Energy consumption of nano data centers has recently been a topic of interest as they emerge as a novel computing and storage platform. We present end-to-end energy consumption models for nano data centers and its centralized counterpart. To assess the energy consumption of nano and centralized data centers, we propose flowbased and time-based energy consumption models for shared and single user network equipment. To evaluate our models, a set of measurements and practical experiments are performed. Our results indicate that nano data centers might lead to energy savings depending on various factors such as location of nano servers, type of access network attached to nano servers, and the ratio of active time to idle time of nano servers. Thus, nano data centers can complement centralized ones and lead to savings energy if certain applications are off-loadable from centralized data centers.
Online social networks (OSNs) with their huge number of active users consume significant amount e... more Online social networks (OSNs) with their huge number of active users consume significant amount energy both in the data centers and in the transport network. Existing studies focus mainly on the energy consumption in the data centers and do not take into account the energy consumption during the transport of data between end-users and data centers. To indicate the amount of the neglected energy, this paper provides a comprehensive framework and a set of measurements for understanding the energy consumption of cloud applications such as photo sharing in social networks. A new energy model is developed to estimate the energy consumption of cloud applications and applied to sharing photos on Facebook, as an example. Our results indicate that the energy consumption involved in the network and end-user devices for photo sharing is approximately equal to 60% of the energy consumption of all Facebook data enters. Therefore, achieving an energy-efficient cloud service requires energy efficiency improvement in the transport network and end-user devices along with the related data centers.
Energy harvesters are used in today’s Wireless Sensor Networks (WSNs) to harvest energy from the ... more Energy harvesters are used in today’s Wireless Sensor Networks (WSNs) to harvest energy from the environment. Although an energy harvester can provide a supply source with a much greater lifetime than a battery, the amount of available energy for an energy harvesting system is a random variable. Furthermore, the proper management of energy harvesters has a considerable impact on reliability. It has been observed that cooperative error control mechanisms like Cooperative Automatic Repeat Request (C-ARQ) and Cooperative Hybrid ARQ (C-HARQ) can be used for improving the energy management and reliability in Energy Harvesting WSNs (EH-WSNs). Recently, the impact of C-ARC mechanism has been considered in an energy harvesting system (GAP4S), however, GAP4S is an EH-WSN powered by microwave with linear recharge rate and unbounded storage capacity. This work evaluates the effect of C-HARC using a new method of relay node selection in EH-WSNs which are powered by the solar energy and have variable recharge rates. Furthermore, a comparative study of C-ARQ and C-HARQ mechanisms, considering the impact of variations in transmission rate and packet error probability on the energy consumption, is provided. The results show that C-HARQ outperforms C-ARQ under the same conditions and the energy consumption of a relay node in C-HARQ is 50% less than the one in C-ARQ.
Two important objectives in wireless sensor networks are reliability and reducing energy consumpt... more Two important objectives in wireless sensor networks are reliability and reducing energy consumption. Hence, overcoming energy constraints and utilizing error control schemes such as Automatic Repeat Request (ARQ) and Forward Error Correction (FEC) are necessary to improve the energy efficiency and reliability. However, these two concerns are at odds, so there is a trade-off between them. Considering this point, the impact of various error control schemes on these objectives and the trade-off between them has been considered in Bluetooth networks recently. However, all these works consider ideal assumptions (e.g., perfect error detection) only. This work evaluates the energy-efficiency of Bluetooth error control schemes in Rayleigh fading channels taking into consideration both ideal assumptions and residual error probability of the CRC code in ARQ schemes. A comparative analysis of coding techniques using different BCH codes on the AUX1 packet is provided. In addition, the impact of variations in number of hops and SNR on the effectiveness of proposed coding techniques is analyzed through simulation. This analysis provides information that help network designers to choose suitable packet types and coding techniques for Bluetooth networks depending on the network situation.
To scavenge the energy from the environment and extend the network lifetime, some wireless sensor... more To scavenge the energy from the environment and extend the network lifetime, some wireless sensor networks (WSNs) have been equipped with energy harvesters recently. However, the variable amount of environmental energy can affect the reliability of energy harvesting wireless sensor networks (EH-WSNs). In addition, data transmission over a wireless media is vulnerable. Hence, utilizing suitable error control schemes are necessary to improve the reliability. Regarding this point, Automatic Repeat Request (ARQ) and Cooperative ARQ (C-ARQ) schemes are applied in this generation of WSNs. Conventional ARQ as well as C-ARQ scheme are considered and examined through simulation. A comparative analysis of these two schemes in terms of energy consumption, energy efficiency and reliability is provided. This analysis shows that C-ARQ is the more appropriate error control scheme in EH-WSNs to manage the variable environmental energy and improve the reliability and energy efficiency.
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Papers by Fatemeh Jalali