Fatemeh Jalali

Fog Computing May Help to Save Energy in Cloud Computing

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 servers 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.

Energy Consumption of Photo Sharing in Online Social Networks

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 consumption of interactive cloud-based document processing applications

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.

Energy consumption modelling of optical networks

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 Content Distribution from Nano Data Centers versus Centralized Data Centers

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.

Greening IoT with Fog: A Survey

The current growth in Internet services, mobile devices, and machine-to-machine (M2M) technologies is providing the building blocks for the Internet of Things (IoT) as it is being applied across all industry sectors. With ongoing proliferation of IoT applications, a new platform called Fog/edge computing, in addition to Cloud computing, is being developed to address requirements such as bandwidth, latency and location awareness. As with previous many telecommunication systems, energy consumption concerns in IoT have been deferred to the point that it may become a bottleneck in the future. This work conducts a survey of existing literature addressing IoT energy consumption growth. We firstly highlight the factors and technologies in the system design, application layer and network virtualizations which lead to higher or lower energy consumption of an IoT service. Furthermore, we report strategies that can help to alleviate power consumption of IoT applications and services using Fog computing. Our objective is to provide a survey for network designers and policy makers who wish to gain an insight into deploying energy-efficient IoT applications.

Energy Consumption Comparison of Interactive Cloud-Based and Local Applications

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 Skydrives 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 survey of Internet energy efficiency metrics

Several metrics have been widely applied to quantify the “energy efficiency” of the Internet and ICT. In this paper we analyse and compare these metrics when applied to telecommunication network equipment, networks and services. We show that different metrics can imply different, and possibly conflicting, strategies for improving energy efficiency. Some guidelines are suggested for the appropriate application of these metrics.