Energy Efficient Virtual Machines Placement Over Cloud-Fog Network Architecture

Alharbi, Hatem A.; El-Gorashi, Taisir E. H.; Elmirghani, Jaafar M. H.

doi:10.1109/access.2020.2995393

Cited by 37 publications

(25 citation statements)

References 73 publications

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“…The service placement problem over the distributed processing architecture depicted in Figure 1 is computationally costly to solve as it is a non-deterministic polynomial (NP)-hard problem. Generally, due to the NPhard nature of placement problems, it is impossible to devise heuristic algorithms that can find the optimal solution within polynomial time [92]. , where is the number of processing servers.…”

Section: Energy Efficient Distributed Processing (Eedp) Heuristicmentioning

confidence: 99%

Energy Efficient Distributed Processing for IoT

et al. 2020

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In the near future, the number of Internet connected objects is expected to be between 26-50 billion devices. This figure is expected to grow even further due to the production of miniaturized portable devices that are lightweight, energy, and cost-efficient. In this paper, the entire IoT-fog-cloud architecture is modeled, the service placement problem is formulated using Mixed Integer Linear Programming (MILP) and the total power consumption is jointly minimized for processing and networking. We evaluate the distributed processing paradigm for both the un-capacitated and capacitated design settings in order to provide solutions for the long-term and short-term basis, respectively. Furthermore, four aspects of the IoT processing placement problem are examined: 1) IoT services with non-splittable tasks, 2) IoT services with splittable tasks, 3) impact of processing overheads needed for inter-service communication and 4) deployment of special-purpose data centers (SP-DCs) as opposed to the conventional general-purpose data center (GP-DC) in the core network. The results showed that for the capacitated problem, task splitting introduces power savings of up to 86% compared to 46% with non-splittable tasks. Moreover, it is observed that the overheads due to inter-service communication greatly impacts the total number of splits. However much insignificant the overhead factor, the results showed that this is not a trivial matter and hence much attention needs to be paid to this area to make the best use of the resources that are available at the edge of the network.

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Section: Energy Efficient Distributed Processing (Eedp) Heuristicmentioning

confidence: 99%

Energy Efficient Distributed Processing for IoT

et al. 2020

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“…The core network (WAN) is considered the backbone infrastructure of the whole telecom network since it interconnects major cities. Also, the IP over the WDM, which is the main technology used in the WAN network provides wide capacity, high scalability, and fast communication to support the transfer speeds of the WAN network [4]. Each WAN network connects with a MAN, which provides a direct connection between the WAN network and its residential users (users located in the LAN network area).…”

Section: Telecommunication Networkmentioning

confidence: 99%

“…Consequently, fog computing has emerged to complement cloud computing, which considers the requirements of transferring a huge amount of data as well as the network latency [3]. The main feature of fog computing is to extend cloud services to the edge of the network (closer to end-users) [3], [4]. Thus, proposing fog computing has provided many advantages such as real-time processing, rapid scaling, and resource pooling [5], in addition to increasing the security and density of devices and mobility [6].…”

Section: Introductionmentioning

confidence: 99%

“…They found that the optimal VM placement decision highly depends on the tradeoff between the power saved by the network and power consumed by hosting extra VM copies. Further, the authors in [8] have optimized VM services to reduce the energy consumption of cloud-fog architecture. In their design, different download rates and different VM types have been considered.…”

Section: Introductionmentioning

confidence: 99%

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Towards a Green Approach for Minimizing Carbon Emissions in Fog-Cloud Architecture

Aldossary

Alharbi²

2021

IEEE Access

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Fog computing is developed to complement cloud computing by extending the cloud services (computing, storage, networking, and management) to the edge of the network in order to reduce service latency. With the rapid growth of cloud users and the emergence of fog computing, the demand for cloud/fog resources has increased dramatically. Also, the incremental use of the cloud/fog resources and their applications has increased energy consumption and carbon emitted, which caused significant environmental challenges. Optimizing the placement of requested applications (e.g., in the form of virtual machines) is one of the main solutions, which has a primary effect in reducing the energy consumption of cloud/fog architecture and consequently their carbon emissions. However, due to the geographic distribution of cloud and fog data centers, there is a variety of carbon emission levels to consider, which makes optimizing the placement of applications in distributed cloud/fog more challenging than in centralized clouds in terms of carbon efficiency. In this paper, we propose a multi-level approach using a mixed-integer linear programming (MILP) model to minimize carbon emissions (CO2) by optimizing the usage of cloud/fog resources. These resources are managed by virtual machines (VMs), which can be optimally and virtually used. In our model, we used the British Telecom (BT) as a telecom network example with an aim to minimize the CO2 emission, considering several scenarios of traffic demand during different times of the day and year (seasons). The results show that the optimal location to host applications highly relies on the carbon intensity and traffic demands. The results also show there is a trade-off between CO2 emission reduced by shortening network journey, and CO2 increased by hosting more applications into the fog nodes. In addition, the results demonstrate that the proposed green fog-cloud architecture outperforms the central cloud and the distributed clouds in terms of reducing the total CO2 emission by up to 91% and 71%, respectively. Finally, we develop a heuristic algorithm to mimic and validate the presented work, and it shows comparable results to the MILP model.INDEX TERMS Cloud computing, fog computing, green fog-cloud architecture, applications placement, energy-efficiency, carbon emissions CO2.

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“…In this paper, we focus on an energy-efficient resource provisioning approach by virtualizing the resources in a distributed processing network that we refer to as the CFN architecture. This work builds on our earlier proposals in various areas such as distributed processing in the IoT/Fog [2,9,31,46], green core and DC networks [4, 5, 10, 12, 15-20, 28, 29, 32, 36, 39, 44], network virtualization and service embedding in core and IoT networks [3,6,7,37] and machine learning and network optimization for healthcare systems [23][24][25][26], and network coding in the core network [34,35]. Our previous work in [8] dealt with the idea of generic service embedding in an IoT setting, we take the work further in this paper by refining the optimization model and abstracting the virtual service requests (VSRs) that comprise of multiple Virtual Machines (VMs) inter-connected in a virtual topology.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient AI over a Virtualized Cloud Fog Network

Yosuf

Mohamed

Alenazi

et al. 2021

Proceedings of the Twelfth ACM International Conference on Future Energy Systems

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Deep Neural Networks (DNNs) have served as a catalyst in introducing a plethora of next-generation services in the era of Internet of Things (IoT), thanks to the availability of massive amounts of data collected by the objects on the edge. Currently, DNN models are used to deliver many Artificial Intelligence (AI) services that include image and natural language processing, speech recognition, and robotics. Accordingly, such services utilize various DNN models that make it computationally intensive for deployment on the edge devices alone. Thus, most AI models are offloaded to distant cloud data centers (CDCs), which tend to consolidate large amounts of computing and storage resources into one or more CDCs. Deploying services in the CDC will inevitably lead to excessive latencies and overall increase in power consumption. Instead, fog computing allows for cloud services to be extended to the edge of the network, which allows for data processing to be performed closer to the end-user device. However, different from cloud data centers, fog nodes have limited computational power and are highly distributed in the network. In this paper, using Mixed Integer Linear Programming (MILP), we formulate the placement of DNN inference models, which is abstracted as a network embedding problem in a Cloud Fog Network (CFN) architecture, where power savings are introduced through trade-offs between processing and networking. We study the performance of the CFN architecture by comparing the energy savings when compared to the baseline approach which is the CDC. CCS CONCEPTS• Computing methodologies → Machine learning; • Networks → Network architectures.

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Energy Efficient Virtual Machines Placement Over Cloud-Fog Network Architecture

Cited by 37 publications

References 73 publications

Energy Efficient Distributed Processing for IoT

Energy Efficient Distributed Processing for IoT

Towards a Green Approach for Minimizing Carbon Emissions in Fog-Cloud Architecture

Energy-Efficient AI over a Virtualized Cloud Fog Network

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