Sphere: Simulator of edge infrastructures for the optimization of performance and resources energy consumption

被引:17
|
作者
Fernandez-Cerero, Damian [1 ]
Fernandez-Montes, Alejandro [1 ]
Javier Ortega, F. [1 ]
Jakobik, Agnieszka [2 ]
Widlak, Adrian [2 ]
机构
[1] Univ Seville, Escuela Tecn Super Ingn Informat, Seville, Spain
[2] Cracow Univ Technol, Inst Comp Sci, Krakow, Poland
关键词
Edge computing; Fog computing; Cloudlet computing; Cloud computing; Energy-aware scheduling; TOOLKIT; SCHEDULER; POLICIES;
D O I
10.1016/j.simpat.2019.101966
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Edge computing constitutes a key paradigm to address the new requirements of areas such as smart cars, industry 4.0, and health care, where massive amounts of heterogeneous data from continuous geographically-distributed sources have to be processed and computed near real-time. To this end, new distributed infrastructures consisting on small computing clusters close to data sources, also known as Cloudlets have emerged. In order to evaluate the performance of these solutions we present Sphere, a simulation tool that enables researchers to establish various scenarios, including: (a) topology and orchestration model of the infrastructure; (b) incoming workload patterns; (c) resource-managing models; and (d) scheduling policies. Moreover, Sphere allows researchers to apply efficiency and performance policies both at infrastructure and cluster levels. The simulator presents the following benefits: (a) Evaluation of various orchestration models; (b) Analysis of resource-efficiency and performance strategies at Edge-infrastructure and cluster (Cloudlet/Cloud) level; (c) Execution of diverse workload generation patterns; (d) Evaluation of strategies for the infrastructure communication, as well as the impact on tasks completion time (makespan); and (e) Simulation of each cluster (Cloudlet/Cloud) independently, including resource-managing, scheduling and resource-efficiency models. Finally, we performed a deep evaluation based on realistic Edge-Computing use cases. The results of the experiments confirm that it is a performant and reliable tool for the analysis of orchestration, graph-resolving, energy-efficiency, resource-managing and scheduling strategies in Edge-computing environments.
引用
收藏
页数:17
相关论文
共 50 条
  • [41] Resources allocation optimization algorithm based on the comprehensive utility in edge computing applications
    Liu, Yanpei
    Zhu, Yunjing
    Bin, Yanru
    Chen, Ningning
    MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2022, 19 (09) : 9147 - 9167
  • [42] On Understanding Time, Energy and Cost Performance of Wimpy Heterogeneous Systems for Edge Computing
    Loghin, Dumitrel
    Ramapantulu, Lavanya
    Teo, Yong Meng
    2017 IEEE 1ST INTERNATIONAL CONFERENCE ON EDGE COMPUTING (IEEE EDGE), 2017, : 1 - 8
  • [43] Automating Performance and Energy Consumption Analysis for Cloud
    Chen, Feifei
    Grundy, John
    Schneider, Jean-Guy
    Yang, Yun
    He, Qiang
    2015 IEEE WORLD CONGRESS ON SERVICES, 2015, : 63 - 70
  • [44] MAPER: mobility-aware energy-efficient container registry migrations for edge computing infrastructures
    Temp, Daniel C.
    da Costa, Alexandre A. F.
    Vieira, Angelo N. C.
    Oribes, Ester S.
    Lopes Jr, Ivan M.
    de Souza, Paulo Silas S.
    Luizelli, Marcelo C.
    Lorenzon, Arthur F.
    Rossi, Fabio D.
    JOURNAL OF SUPERCOMPUTING, 2025, 81 (01)
  • [45] Energy Consumption of Neural Networks on NVIDIA Edge Boards: an Empirical Model
    Lahmer, Seyyidahmed
    Khoshsirat, Aria
    Rossi, Michele
    Zanella, Andrea
    2022 20TH INTERNATIONAL SYMPOSIUM ON MODELING AND OPTIMIZATION IN MOBILE, AD HOC, AND WIRELESS NETWORKS (WIOPT 2022), 2022, : 365 - 371
  • [46] On the Energy Consumption of UAV Edge Computing in Non-Terrestrial Networks
    Traspadini, Alessandro
    Giordani, Marco
    Giambene, Giovanni
    De Cola, Tomaso
    Zorzi, Michele
    FIFTY-SEVENTH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS, IEEECONF, 2023, : 1684 - 1690
  • [47] Particle Swarm Optimization for Performance Management in Multi-cluster IoT Edge Architectures
    Azimi, Shelernaz
    Pahl, Claus
    Shirvani, Mirsaeid Hosseini
    PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND SERVICES SCIENCE (CLOSER), 2020, : 328 - 337
  • [48] Energy Efficient and Optimized Makespan Workflow Scheduling Algorithm for Heterogeneous Resources in Fog-Cloud-Edge Collaboration
    Bisht, Jyoti
    Subrahmanyam, V. V.
    PROCEEDINGS OF 2020 6TH IEEE INTERNATIONAL WOMEN IN ENGINEERING (WIE) CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (WIECON-ECE 2020), 2020, : 78 - 83
  • [49] Distributed Energy Resources Parameter Monitoring in Microgrids Using Blockchain and Edge Computing
    Campillo, J.
    Dominguez-Jimenez, J. A.
    Ariza, H.
    Payares, E. D.
    Martinez-Santos, J. C.
    2020 IEEE PES TRANSACTIVE ENERGY SYSTEMS CONFERENCE (TESC), 2020,
  • [50] The effect of task processing management on energy consumption at the edge of Internet of things network with using reinforcement learning method
    Mohammadian, Asghar
    Zarrabi, Houman
    Jabbehdari, Sam
    Rahmani, Amir Masoud
    COMPUTERS & INDUSTRIAL ENGINEERING, 2024, 195