| Title | A Cloud and Fog based Architecture for Energy Management of Smart City by using Meta-heuristic Techniques |
|---|---|
| ID_Doc | 38790 |
| Authors | Butt, AA; Khan, S; Ashfaq, T; Javaid, S; Sattar, NA; Javaid, N |
| Title | A Cloud and Fog based Architecture for Energy Management of Smart City by using Meta-heuristic Techniques |
| Year | 2019 |
| Published | |
| Abstract | Cloud servers provide services over the internet by using Virtual Machines (VMs). The power consumption of Physical Machines (PMs) needs to be considered, as VMs are running on physical machines. When a consumer sends request to the cloud, it takes time to respond because of distant location of cloud. Due to which delay and latency issue arises. Fog is introduced to overcome the peculiarities of cloud. In fog computing environment, the operational challenges for the research community are: reducing the energy consumption and load balancing. The energy consumption of the fog resources depends on the requests that are allocated to the set of VMs. This is a challenging task. In this paper, three layered architecture cloud, fog and consumer layer are proposed. The cloud and fog provide VMs to run the consumers' application quickly. The meta-heuristic algorithm that is: Genetic Algorithm (GA) is proposed and Binary Particle Swarm Optimization (BPSO) is implemented to balance the set of requests on VMs of cloud and fog. The proposed and implemented algorithm is compared with existing PSO and BAT algorithms to measure efficiency. The Closest Data Center (CDC), Optimize Response Time (ORT), Reconfigure Dynamically with Load (RDL) is implemented to optimize the Response Time (RT) and Processing Time (PT). These policies also decide which requests are allocated to which Data Center (DC). The proposed GA and implemented BPSO are use to minimize the computational cost and also decrease the RT and PT of DCs. |
Similar Articles
| ID | Score | Article |
|---|---|---|
37069
|
Maiti, P; Apat, HK; Kumar, A; Sahoo, B; Turuk, AK Deployment of Multi-tier Fog Computing System for IoT Services in Smart City(2019) | |
| 42527
|
Choudhury, S; Luhach, AK; Rodrigues, JJPC; AL-Numay, M; Ghosh, U; Roy, DS A Residual Resource Fitness-Based Genetic Algorithm for a Fog-Level Virtual Machine Placement for Green Smart City Services(2023)Sustainability, 15, 11 | |
| 44412
|
Tripathy, SS; Roy, DS; Barik, RK M2FBalancer: A mist-assisted fog computing-based load balancing strategy for smart cities(2021)Journal Of Ambient Intelligence And Smart Environments, 13, 3 | |
| 41961
|
Reddy, KHK; Luhach, AK; Kumar, VV; Pratihar, S; Kumar, D; Roy, DS Towards energy efficient Smart city services: A software defined resource management scheme for data centers(2022) | |
| 38468
|
Jayadeyan, A; Syafiza, I; Ahmad, MR; Azyze, NLAMS; Rasidi, NF; Zainan, NH Energy-Efficient Network Architecture for Smart City Development(2023)Przeglad Elektrotechniczny, 99, 9 | |
| 39013
|
Pei, P; Huo, ZJ; Martínez, OS; Crespo, RG Minimal Green Energy Consumption and Workload Management for Data Centers on Smart City Platforms(2020)Sustainability, 12, 8 | |
| 41256
|
Choudhury, S; Pradhan, B; Francis, SAJ; Roy, DS An energy efficient fog level resource management scheme for software defined cities(2023) | |
| 42661
|
Malhotra, A; Dhurandher, SK; Gupta, M; Kumar, B Best fit power weighted difference method for fog node selection in smart cities(2020)Iet Communications, 14, 19 |