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A Multi-Classifiers Based Algorithm for Energy Efficient Tasks Offloading in Fog Computing

SENSORS 2023年第16期23卷 7209-7209页

作者： Alasmari, Moteb K. Alwakeel, Sami S. Alohali, Yousef A. King Saud Univ Coll Comp & Informat Sci Riyadh 11362 Saudi Arabia

The IoT has connected a vast number of devices on a massive internet scale. With the rapid increase in devices and data, offloading tasks from IoT devices to remote Cloud data centers becomes unproductive and costly. Optimizing energy consumption in IoT devices while meeting deadlines and data constraints is challenging. Fog Computing aids efficient IoT task processing with proximity to nodes and lower service delay. Cloud task offloading occurs frequently due to Fog Computing's limited resources compared to remote Cloud, necessitating improved techniques for accurate categorization and distribution of IoT device task offloading in a hybrid IoT, Fog, and Cloud paradigm. This article explores relevant offloading strategies in Fog Computing and proposes MCEETO, an intelligent energy-aware allocation strategy, utilizing a multi-classifier-based algorithm for efficient task offloading by selecting optimal Fog Devices (FDs) for module placement. MCEETO decision parameters include task attributes, Fog node characteristics, network latency, and bandwidth. The method is evaluated using the iFogSim simulator and compared with edge-ward and Cloud-only strategies. The proposed solution is more energy-efficient, saving around 11.36% compared to Cloud-only and approximately 9.30% compared to the edge-ward strategy. Additionally, the MCEETO algorithm achieved a 67% and 96% reduction in network usage compared to both strategies.

关键词： fog computing internet of things module placement energy efficiency task offloading iFogSim

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IoT application modules placement in heterogeneous fog–cloud infrastructure

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International Journal of Information Technology (Singapore) 2021年第5期13卷 1975-1982页

作者： Arora, Upma Singh, Nipur Department of Computer Science Gurukul Kangri Vishwavidhyalaya Haridwar UK India

Due to the increasing demands of minimizing the delay for Internet of things (IoT) applications, cloud computing researchers are now moving towards the research in fog computing and edge computing area. Fog computing serves the purpose of bringing devices closer to the proximity of data centre by performing some specific possible tasks nearest to the vicinity of devices requiring the services and ultimately reduces the delay. Thus the complete infrastructure becomes combined fog–cloud in itself. Furthermore, Fog computing serves the need of running the IoT applications by placing the application modules on to the devices available in the said fog–cloud infrastructure. For this purpose, various module placement and scheduling approaches have been developed, but considering the heterogeneous and dynamic nature in the environment is the primary concern of our work. The purpose of this paper is to present the Heterogeneous Shortest module First (HSMF) Algorithm for placement of the modules of an application in the heterogeneous fog–cloud computing environment. Using this algorithm, we place heterogeneous modules on to the different Fog devices available in the network. The proposed approach is then compared to the available cloud-only and edge-ward application module placement approach. The results obtained from our presented framework clearly state improvement in the total execution time and total network usage. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Fog–cloud computing Heterogeneous IoT application modules module placement Resource allocation Task scheduling

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Task offloading in mobile fog computing by classification and regression tree

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2020年第1期13卷 104-122页

作者： Rahbari, Dadmehr Nickray, Mohsen Univ Qom Dept Comp Engn & Informat Technol Alghadir Ave Qom Iran

Fog computing (FC) as an extension of cloud computing provides a lot of smart devices at the network edge, which can store and process data near end users. Because FC reduces latency and power consumption, it is suitable for the Internet of Things (IoT) applications as healthcare, vehicles, and smart cities. In FC, the mobile devices (MDs) can offload their heavy tasks to fog devices (FDs). The selection of best FD for offloading has serious challenges in the time and energy. In this paper, we present a module placement method by Classification and regression tree Algorithm (MPCA). We select the best FDs for modules by MPCA. Initially, the power consumption of MDs are checked, if this value is greater than Wi-Fi's power consumption, then offloading will be done. The MPCA's decision parameters for selecting the best FD include authentication, confidentiality, integrity, availability, capacity, speed, and cost. To optimize MPCA, we analyze and apply the probability of network's resource utilization in the module offloading. This method is called by (MPMCP). To evaluate our proposed approach, we simulate MPCA and MPMCP algorithms and compare them with First Fit (FF) and local mobile processing methods in Cloud, FDs, and MDs. The results include the power consumption, response time and performance show that the proposed methods are superior to other compared methods.

关键词： Mobile fog computing module placement Task offloading Classification and regression tree Markov chain process

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Studying and developing a resource allocation algorithm in Fog computing

Studying and developing a resource allocation algorithm in F...

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International Conference on Advanced Computing and Applications (ACOMP)

作者： Nguyen Quang-Hung Truong Pham Thanh An Ho Chi Minh City Univ Technol VNU HCM Ho Chi Minh City Vietnam

ISBN: (纸本)9781538691861

Internet of Things (IoT) is becoming popular. Many IoT devices will bring huge data moving within a network infrastructure. Cloud has become so widespread that almost many business or end users are using private and/or public cloud. Cloud servers will be responsible for processing incoming data and sending them back after processing. As a result, the amount of data moving will be extremely large and processing of Big Data will gradually appear. Many enterprise companies included Cisco, Dell, Microsoft, Intel has pioneered Fog computing since 2015. In Fog computing, data will be scattered instead of concentrating data like the traditional Cloud computing. The main goal of this study is to propose a resource allocation algorithm to distribute modules of an IoT application to the entire network in Fog computing environment. This proposed algorithm must run successfully on a simulator. With different environments, the algorithm will allocate different modules depending on the configuration of each device. To evaluate the algorithm, this study uses the iFogSim toolkit, which extends from the CloudSim toolkit, for modeling and simulation of Fog computing environment. Current version of iFogSim is sufficient to be able to implement some criteria necessary for this study.

关键词： fog computing cloud computing module placement first fit algorithm

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A Two-Stage Technique for Quick and Low Power Offloading in IoT 18

A Two-Stage Technique for Quick and Low Power Offloading in ...

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International Conference on Smart Cities and Internet of Things (SCIOT)

作者： Rahbari, Dadmehr Nickray, Mohsen Heydari, Gholamreza Univ Qom Dept Comp Engn & Informat Technol Qom Iran

ISBN: (纸本)9781450365321

Fog computing (FC) as an extension of cloud computing provides a lot of smart devices at the network edge, which can store and process data near end users. Since FC reduces latency and power consumption, it is suitable for the Internet of Things (IoT) applications in smart cities. In FC, the Mobile Devices (MDs) can offload its heavy tasks to Fog Devices (FDs). The selection of best FD for offloading has serious challenges in the time and energy. In this paper, we present a module placement method by Classification and regression tree Algorithm (MPCA). We select the best FDs for modules by MPCA. Initially, if the total power consumption of CPU and memory in MDs is greater than Wi-Fi's power consumption, the offloading will be done. The MPCA's decision parameters for selecting of best FD include authentication, confidentiality, integrity, availability, capacity, speed, and cost. To evaluate our proposed approach, we simulate MPCA and compare it with First-Fit (FF) and local mobile processing methods in Cloud, fog, and MDs. The results show that the proposed method is superior to other compared methods in the average power consumption by 38.41%, response time by 37%, and 13.76% in performance.

关键词： Internet of Things Mobile fog computing module placement Smart City

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Comparative study of meta-heuristic 3D floorplanning algorithms

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NEUROCOMPUTING 2015年第PartA期150卷 67-81页

作者： Cuesta-Infante, Alfredo Colmenar, J. Manuel Bankovic, Zorana Risco-Martin, Jose L. Zapater, Marina Hidalgo, J. Ignacio Ayala, Jose L. Moya, Jose M. CES Felipe II Madrid Spain Univ Complutense Madrid E-28040 Madrid Spain Univ Politecn Madrid E-28040 Madrid Spain

Constant necessity of improving performance has brought the invention of 3D chips. The improvement is achieved due to the reduction of wire length, which results in decreased interconnection delay. However, 3D stacks have less heat dissipation due to the inner layers, which leads to increased temperature and the appearance of hot spots. This problem can be mitigated through appropriate floorplanning. For this reason, in this work we present and compare five different solutions for floorplanning of 3D chips. Each solution uses a different representation, and all are based on meta-heuristic algorithms, namely three of them are based on simulated annealing, while two other are based on evolutionary algorithms. The results show great capability of all the solutions in optimizing temperature and wire length, as they all exhibit significant improvements comparing to the benchmark floorplans. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Floorplan module placement Meta-heuristic search

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module placement for fault-tolerant microfluidics-based biochips

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ACM TRANSACTIONS ON DESIGN AUTOMATION OF ELECTRONIC SYSTEMS 2006年第3期11卷 682-710页

作者： Su, Fei Chakrabarty, Krishnendu Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA

Microfluidics-based biochips are soon expected to revolutionize clinical diagnosis, DNA sequencing, and other laboratory procedures involving molecular biology. Most microfluidic biochips today are based on the principle of continuous fluid flow and they rely on permanently etched microchannels, micropumps, and microvalves. We focus here on the automated design of "digital" droplet-based microfluidic biochips. In contrast to conventional continuous-flow systems, digital microfluidics offers dynamic reconfigurability;groups of cells in a microfluidics array can be reconfigured to change their functionality during the concurrent execution of a set of bioassays. We present a simulated annealing-based technique for module placement in such biochips. The placement procedure not only addresses chip area, but also considers fault tolerance, which allows a microfluidic module to be relocated elsewhere in the system when a single cell is detected to be faulty. Simulation results are presented for case studies involving the polymerase chain reaction and multiplexed in vitro clinical diagnostics.

关键词： algorithms design performance reliability physical design automation module placement microfluidics biochips

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Thermal driven module placement using sequence-pair

Thermal driven module placement using sequence-pair

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IEEE Asia Pacific Conference on Circuits and Systems

作者： Okada, Norihide Kodama, Chikaaki Sato, Takashi Fujiyoshi, Kunihiro Tokyo Univ Agr & Technol Dept Elect & Elect Engn Tokyo Japan Tokyo Inst Technol Dept Elect & Appl Phys Tokyo Japan

ISBN: (纸本)9781424403868

The increase of power consumption in recent VLSI chip has led to uneven thermal distribution and high temprature on the chip. This brings inappropriate distribution of wire delay and may adversely effect the behavior of the chip by the increase of clock skew. The rise of local temperature causes the electro migration which brings wire disconnections. Therefore, some module placement methods considering the thermal distribution were proposed so far. However, these conventional methods require much time for obtaining the placement. In this paper, we propose a method to quickly obtain a module placement with good thermal distribution. We confirmed that the proposed method with Simulated Annealing can obtain good placements in shorter time.

关键词： thermal driven module placement sequence-pair

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Runtime Temporal Partitioning Assembly to Reduce FPGA Reconfiguration Time

Runtime Temporal Partitioning Assembly to Reduce FPGA Reconf...

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International Conference on Reconfigurable Computing and FPGAs

作者： Jara-Berrocal, Abelardo Gordon-Ross, Ann Univ Florida NSF Ctr High Performance Reconfigurable Comp CHRE Dept Elect & Comp Engn Gainesville FL 32610 USA

ISBN: (纸本)9781424452934

Large applications that exceed available FPGA resources must time-multiplex these resources using smaller hardware modules. In order to orchestrate this time-multiplexing, temporal partitioning partitions these hardware modules into multiple subsets, each of which fit within the available resources. During a temporal partition transition, the FPGA is reconfigured to the subsequent temporal partition. However, FPGA reconfiguration time can impose significant performance overhead as the entire FPGA fabric must be reconfigured even if only a small portion has changed. Partially reconfigurable (PR) FPGAs can decrease reconfiguration time by only reconfiguring the portions of the FPGA fabric that differ. In this paper, we present a design methodology using a simulated annealing-based module placement optimization engine to minimize FPGA reconfiguration overhead by exploiting module overlap across successive temporal partitions. Experimental results show that our methodology reduces FPGA reconfiguration time by 44% on average.

关键词： temporal partitioning partial reconfiguration field programmable gate arrays module placement

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A novel analog module placement procedure based on hierarchical genetic algorithm

A novel analog module placement procedure based on hierarchi...

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9th IASTED International Conference on Artificial Intelligence and Soft Computing

作者： Yoshikawa, M Fujino, T Terai, H Ritsumeikan Univ Dept VLSI Syst Design Shiga 5258577 Japan

ISBN: (纸本)0889865361

In analog design, components must be modeled in a detailed fashion since their electrical behaviour affects deeply the final result. Whereas the physical design of digital circuits is automated to a large extent, the layout of analog circuits is still a manual, time-consuming and error-prone task. In this paper, we proposed a novel analog placement procedure based on Genetic Algorithm. For selection control, new objective functions are introduced for analog-specific capabilities. The proposed algorithm has three level hierarchical structures. Simulation results evaluating the proposed algorithm are shown good performance.

关键词： analog constraint genetic algorithm slicing structure module placement

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