Deadline-aware and energy-efficient IoT task scheduling in fog computing systems: A semi-greedy approach

Journal of Network and Computer Applications, Journal Year: 2022, Volume and Issue: 201, P. 103333 - 103333

Published: Jan. 26, 2022

Language: Английский

---

AFED-EF: An Energy-Efficient VM Allocation Algorithm for IoT Applications in a Cloud Data Center

IEEE Transactions on Green Communications and Networking, Journal Year: 2021, Volume and Issue: 5(2), P. 658 - 669

Published: March 19, 2021

Cloud Data Centers (CDCs) have become a vital computing infrastructure for enterprises. However, CDCs consume substantial energy due to the increased demand power, especially Internet of Things (IoT) applications. Although great deal research in green resource allocation algorithms been proposed reduce consumption CDCs, existing approaches mostly focus on minimizing number active Physical Machines (PMs) and rarely address issue load fluctuation efficiency Virtual Machine (VM) provisions jointly. Moreover, lack mechanisms consider redirect incoming traffics appropriate resources optimize Quality Services (QoSs) provided by CDCs. We propose novel adaptive energy-aware VM deployment mechanism called AFED-EF IoT applications handle these problems. The algorithm can efficiently has good performance during placement. carried out extensive experimental analysis using real-world workload based more than thousand PlanetLab VMs. results illustrate that outperforms other consumption, Service Level Agreements (SLA) violation, efficiency.

Language: Английский

---

Middleware for the Internet of Things: A survey on requirements, enabling technologies, and solutions

Journal of Systems Architecture, Journal Year: 2021, Volume and Issue: 117, P. 102098 - 102098

Published: March 14, 2021

Language: Английский

---

Resource Management in Cloud and Cloud-influenced Technologies for Internet of Things Applications

ACM Computing Surveys, Journal Year: 2022, Volume and Issue: 55(12), P. 1 - 37

Published: Dec. 16, 2022

The trend of adopting Internet Things (IoT) in healthcare, smart cities, Industry 4.0, and so on is increasing by means cloud computing, which provides on-demand storage computation facilities over the Internet. To meet specific requirements IoT applications, has also shifted its service offering platform to next-generation models, such as fog, mist, dew computing. As a result, have become part parcel applications that play significant roles improving quality human life. In addition inherent advantages advanced improve performance further, it essential understand how resources cloud-influenced platforms are managed support various phases end-to-end deployment. Considering this importance, article, we provide brief description, systematic review, possible research directions every aspect resource management tasks, workload modeling, provisioning, scheduling, allocation, load balancing, energy management, heterogeneity platforms, from perspective. primary objective article help early researchers gain insight into underlying concepts tasks for applications.

Language: Английский

---

Security prioritized multiple workflow allocation model under precedence constraints in cloud computing environment

Cluster Computing, Journal Year: 2023, Volume and Issue: 27(1), P. 341 - 376

Published: Jan. 3, 2023

Language: Английский

---

A hybrid meta-heuristic algorithm for multi-objective IoT service placement in fog computing environments

Decision Analytics Journal, Journal Year: 2023, Volume and Issue: 10, P. 100379 - 100379

Published: Dec. 12, 2023

The fog computing paradigm is promising for deploying various delay-sensitive Internet of Things (IoT) applications. resource-constrained devices restrict the number application deployments due to a lack efficient resource estimation and discovery mechanisms emergent heterogeneous IoT An allocation strategy one best choices meet these application's Quality Service (QoS) requirements improve system performance. However, finding applications with more than QoS parameter challenge, it has been proved as non-deterministic polynomial time (NP)-complete problem. This article formulates classical weighted multi-objective service placement optimize three parameters, i.e., makespan, cost, energy. non-convexity nature solution space motivates us focus on population-based meta-heuristic algorithm, i.e. Genetic Algorithm (GA), Simulated Annealing (SA) Particle Swarm Optimization (PSO), along their combination GA-SA, GA-PSO. It implements algorithm compares greedy-based random approach, varying different parameters. final results reveal that hybrid method GA-SA outperforms other state-of-the-art algorithms.

Language: Английский

---

Toward an autonomic approach for Internet of Things service placement using gray wolf optimization in the fog computing environment

Software Practice and Experience, Journal Year: 2021, Volume and Issue: 51(8), P. 1745 - 1772

Published: May 16, 2021

Abstract Divers and the huge amount of data produced by Internet Things (IoT) applications on one hand, inherent limitations local equipment to handle these data, other leads present emerging closer technologies end‐users such as fog computing environment. Nevertheless, despite numerous advantages an environment, it still needs state‐of‐the‐art approaches cope with some limitations. In literature, resource placement strategies are generally proposed address problems, in which IoT mapped nodes. However, its importance, different attempt enhance overall system's performance users' expectations: none is satisfactory. this article, deploy nodes, autonomic service approach based gray wolf optimization scheme proposed, enhancing while considering execution costs. Besides, concepts help make appropriate automanagement system that fits better environment's dynamic behavior. Simulation results demonstrate outperforms converges solution near‐optimal application deployment nodes respect performing services 93.7%, average waiting time for performed 100%, remaining sent extra provisioned period zero.

Language: Английский

---

A fuzzy approach for optimal placement of IoT applications in fog-cloud computing

Cluster Computing, Journal Year: 2021, Volume and Issue: 25(1), P. 303 - 320

Published: Sept. 7, 2021

Language: Английский

---

A Utilization Based Genetic Algorithm for virtual machine placement in cloud systems

Computer Communications, Journal Year: 2023, Volume and Issue: 214, P. 136 - 148

Published: Nov. 29, 2023

Language: Английский

---

DECA: A Dynamic Energy Cost and Carbon Emission-Efficient Application Placement Method for Edge Clouds

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 70192 - 70213

Published: Jan. 1, 2021

As an increasing amount of data processing is done at the network edge, high energy costs and carbon emission Edge Clouds (ECs) are becoming significant challenges. The placement application components (e.g., in form containerized microservices) on ECs has important effect consumption ECs, impacting both emissions. Due to geographic distribution there a variety resources, prices rates consider, which makes optimizing applications for cost efficiency even more challenging than centralized clouds. This paper presents Dynamic Energy Carbon emission-efficient Application method (DECA) ECs. DECA addresses initial re-optimization using migrations. considers geographically varying as well usage computing resources same time. By combining prediction-based A* algorithm with Fuzzy Sets technique, intelligent decisions optimize Simulation results show ability providing tradeoff

Language: Английский

---