TRAM: Technique for resource allocation and management in fog computing environment

Wadhwa, Heena; Aron, Rajni

doi:10.1007/s11227-021-03885-3

Cited by 38 publications

(7 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the existing literature, some research on centralized allocation strategy in fog computing is based on simple methods without complex computations classified in the fundamental methods. They include such methods as Throttled, Round Robin (RR), Active Monitoring [56], First Fit [58] and TRAM [57]. In this part, the selected fundamental methods are reviewed.…”

Section: Fundamental Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Resource Allocation in Fog Computing: A Systematic Review

Salem¹,

Algaphari²

2023

JST

View full text Add to dashboard Cite

Despite the importance of resource allocation issues, there is no systematic, comprehensive and detailed survey on resource allocation approaches in the fog-computing context. In this article, we provide a Systematic Literature Review (SLR) on the resource allocation approaches in fog environments in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and provide open issues. The presented taxonomy is categorized into three main fields: centralized allocation, decentralized allocation, and integrated allocation (published between 2017 and March 2022). According to what is known in fog computing, load balancing and service placement are among the most important basic parameters that ensure service quality. These fields are classified into four methods, approximate, exact, fundamental, and hybrid. In addition, this article investigates resource allocation metrics with all advantages and limitations related to chosen resource allocation mechanisms in networks.

show abstract

Section: Fundamental Methodsmentioning

confidence: 99%

“…In the cloud/fog-based model, smart buildings having many apartments consist of IoT devices that were regarded. In addition, Wadhwa et al [57] designed a novel resource allocation and management approach. TRAM, a resource allocation and management technique, is proposed to ensure resource utilization at the fog layer.…”

Section: Fundamental Methodsmentioning

confidence: 99%

Resource Allocation in Fog Computing: A Systematic Review

Salem¹,

Algaphari²

2023

JST

View full text Add to dashboard Cite

show abstract

“…This paper identifies and provides a critical analysis of the many optimization strategies applied to energy management issues, with a focus on forecasting, demand management, economic dispatch, and unit commitment. Inferences from the review suggested that mixed integer programming approaches [28] were frequently adopted, given their effectiveness in resolving the energy management issue in microgrids and their ease of use.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Task Scheduling for Cloud Computing Platforms Using Energy Management Algorithm: A Comparative Analysis of Workflow Execution Time

Ahmed,

Adnan,

Abdullah

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Cloud computing platform offers numerous applications and resources such as data storage, databases, and network building. However, efficient task scheduling is crucial for maximizing the overall execution time. In this study, workflows are used as datasets to compare scheduling algorithms, including Shortest Job First, First Come, First Served, (DVFS) and Energy Management Algorithms (EMA). To facilitate comparison, the number of virtual machines in the Visual Studio Net framework environment is increased. The experimental findings indicate that increasing the number of virtual machines reduces Makespan. Moreover, the Energy Management Algorithm (EMA) outperforms Shortest Job First by 2.79% for the CyberShake process and surpasses the First Come, First Serve algorithm by 12.28%. Additionally, EMA produces 21.88% better results than both algorithms combined. For the Montage process, EMA performs 4.50% better than Shortest Job First and 25.75% superior to the First Come, First Serve policy. Finally, we ran simulations to determine the performance of the suggested mechanism and contrasted it with the widely used energy-efficient techniques. The simulation results demonstrate that the suggested structural design may successfully reduce the amount of data and give suitable scheduling to the cloud. INDEX TERMS Energy Management Algorithm (EMA); First come First Serve (DVFS); Shortest Job First (RR); Makespan; VMs I. INTRODUCTION

show abstract

“…One of the most crucial performance testing measures, particularly for collaborative apps and services, is latency [78][79][80][81]. Advanced computational capabilities are offered by cloud servers.…”

Section: Latencymentioning

confidence: 99%

The Integration of WoT and Edge Computing: Issues and Challenges

et al. 2023

View full text Add to dashboard Cite

The Web of Things is an improvement on the Internet of Things (IoT) that incorporates smart objects into both the web architecture (application) and the internet (network). WoT applications are inescapable in residential homes and communities. The intent behind WoT applications is to increase sustainable development for reducing resource consumption. The Web of Things (WoT) aims to create a decentralized Internet of Things. Edge computing addresses IoT computing demands by reducing the escalation in resource congestion situations. In edge computing data is placed closed to the end users which diverts computation load from the centralized data centers. Furthermore, the dispersed structure balances network traffic and minimizes traffic peaks in IoT networks. Therefore, resulting in reducing transmission delays between edge servers and end users which improves response times for real-time WoT applications. Low battery supply to nodes with enough power resources can increase the lifespan of the individual nodes by moving processing and communication overhead from the nodes. This paper integrates WoT and edge computing and compares their functionalities. In addition, it demonstrates how edge computing enhances WoT performance and concentrates on transmission, storage, and computation aspects. Furthermore, for performance evaluation it categorizes edge computing based on different architectures. Moreover, the challenges of Web of Things and edge computing have been discussed in terms of bandwidth, latency, energy, and cost. Finally, advantages of the Web of Things and edge computing have been discussed.

show abstract

TRAM: Technique for resource allocation and management in fog computing environment

Cited by 38 publications

References 26 publications

Resource Allocation in Fog Computing: A Systematic Review

Resource Allocation in Fog Computing: A Systematic Review

An Efficient Task Scheduling for Cloud Computing Platforms Using Energy Management Algorithm: A Comparative Analysis of Workflow Execution Time

The Integration of WoT and Edge Computing: Issues and Challenges

Contact Info

Product

Resources

About