scholarly journals Cloud- and Fog-Integrated Smart Grid Model for Efficient Resource Utilisation

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7846
Author(s):  
Junaid Akram ◽  
Arsalan Tahir ◽  
Hafiz Suliman Munawar ◽  
Awais Akram ◽  
Abbas Z. Kouzani ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Smart Grid ◽  
Virtual Machines ◽  
Fog Computing ◽  
Round Robin ◽  
Ant Colony ◽  
Electrical Network ◽  
Ant Colony Optimisation ◽  
Inertia Weight

The smart grid (SG) is a contemporary electrical network that enhances the network’s performance, reliability, stability, and energy efficiency. The integration of cloud and fog computing with SG can increase its efficiency. The combination of SG with cloud computing enhances resource allocation. To minimise the burden on the Cloud and optimise resource allocation, the concept of fog computing integration with cloud computing is presented. Fog has three essential functionalities: location awareness, low latency, and mobility. We offer a cloud and fog-based architecture for information management in this study. By allocating virtual machines using a load-balancing mechanism, fog computing makes the system more efficient (VMs). We proposed a novel approach based on binary particle swarm optimisation with inertia weight adjusted using simulated annealing. The technique is named BPSOSA. Inertia weight is an important factor in BPSOSA which adjusts the size of the search space for finding the optimal solution. The BPSOSA technique is compared against the round robin, odds algorithm, and ant colony optimisation. In terms of response time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 53.99 ms, 82.08 ms, and 81.58 ms, respectively. In terms of processing time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 52.94 ms, 81.20 ms, and 80.56 ms, respectively. Compared to BPSOSA, ant colony optimisation has slightly better cost efficiency, however, the difference is insignificant.

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

scholarly journals Optimized Resource Allocation Algorithm for Crowd-Creation Space Computing Based on Cloud Computing Environment

2021 ◽  
pp. 08-25
Author(s):  
Mustafa El .. ◽  
◽  
◽  
Aaras Y Y.Kraidi
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Simulated Annealing ◽  
Task Allocation ◽  
Ant Colony Algorithm ◽  
Simulated Annealing Algorithm ◽  
Ant Colony ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Resource Allocation Algorithm

The crowd-creation space is a manifestation of the development of innovation theory to a certain stage. With the creation of the crowd-creation space, the problem of optimizing the resource allocation of the crowd-creation space has become a research hotspot. The emergence of cloud computing provides a new idea for solving the problem of resource allocation. Common cloud computing resource allocation algorithms include genetic algorithms, simulated annealing algorithms, and ant colony algorithms. These algorithms have their obvious shortcomings, which are not conducive to solving the problem of optimal resource allocation for crowd-creation space computing. Based on this, this paper proposes an In the cloud computing environment, the algorithm for optimizing resource allocation for crowd-creation space computing adopts a combination of genetic algorithm and ant colony algorithm and optimizes it by citing some mechanisms of simulated annealing algorithm. The algorithm in this paper is an improved genetic ant colony algorithm (HGAACO). In this paper, the feasibility of the algorithm is verified through experiments. The experimental results show that with 20 tasks, the ant colony algorithm task allocation time is 93ms, the genetic ant colony algorithm time is 90ms, and the improved algorithm task allocation time proposed in this paper is 74ms, obviously superior. The algorithm proposed in this paper has a certain reference value for solving the creative space computing optimization resource allocation.

The Study on Cloud Computing Resource Allocation Method

2012 ◽  
Vol 198-199 ◽  
pp. 1506-1513 ◽  
Author(s):  
Ling Yan Wang ◽  
Ai Min Liu
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Virtual Machines ◽  
Optimization Methods ◽  
Resource Provisioning ◽  
Scheduling Problems ◽  
Multi Agent Systems ◽  
Optimization Simulation ◽  
Multi Agent ◽  
Fair Scheduler

Resource allocation and scheduling problems in the field of cloud computing can be classified into two major groups. The first one is in the area of MapReduce task scheduling. The default scheduler is the FIFO one. Two other schedulers that are available as plug-in for Hadoop: Fair scheduler and Capacity scheduler. We presented recent research in this area to enhance performance or to better suit a specific application. MapReduce scheduling research involves introducing alternative schedulers, or proposing enhancements for existing schedulers such as streaming and input format specification. The second problem is the provisioning of virtual machines and processes to the physical machines and its different resources. We presented the major cloud hypervisors available today. We described the different methods used to solve the resource allocation problem including optimization, simulation, distributed multi-agent systems and SoA. Finally, we presented the related topic of connecting clouds which uses similar resource provisioning methods. The above two scheduling problems are often mixed up, yet they are related. For example, MapReduce benchmarks can be used to evaluate VM provisioning methods. Enhancing the solution to one problem can affect the other. Similar methods can be used in solving both problems, such as optimization methods. Cloud computing is a platform that hosts applications and services for businesses and users to accesses computing as a service. In this paper, we identify two scheduling and resource allocation problems in cloud computing.

scholarly journals OLB: A Nature Inspired Approach for Load Balancing in Cloud Computing

2015 ◽  
Vol 15 (4) ◽  
pp. 138-148 ◽  
Author(s):  
B. Mallikarjuna ◽  
P. Venkata Krishna
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Distributed Systems ◽  
Load Balancing ◽  
Virtual Machines ◽  
Simulation Analysis ◽  
Hash Table ◽  
Distributed Hash Table ◽  
Efficient Resource

Abstract Load balancing is treated as one of the important mechanisms for efficient resource allocation in cloud computing. In future there will appear a necessity of fully autonomic distributed systems to address the load balancing issues. With reference to this, we proposed a load balancing mechanism called Osmosis Load Balancing (OLB). OLB works on the principle of osmosis to reschedule the tasks in virtual machines. The solution is based on the Distributed Hash Table (DHT) with a chord overlay mechanism. The Chord overlay is used for managing bio inspired agents and status of the cloud. By simulation analysis, the proposed algorithm has shown better performance in different scenarios, both in heterogeneous and homogeneous clouds.

scholarly journals A Multi Agent Based Dynamic Resource Allocation in Fog-Cloud Computing Environment

2021 ◽  
Vol 18 (22) ◽  
pp. 413
Author(s):  
Ismail Zaharaddeen Yakubu ◽  
Lele Muhammed ◽  
Zainab Aliyu Musa ◽  
Zakari Idris Matinja ◽  
Ilya Musa Adamu
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Fog Computing ◽  
Dynamic Resource Allocation ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Agent Based ◽  
Edge Network ◽  
Dynamic Resource ◽  
Iot Devices

Cloud high latency limitation has necessitated the introduction of Fog computing paradigm that extends computing infrastructures in the cloud data centers to the edge network. Extended cloud resources provide processing, storage and network services to time sensitive request associated to the Internet of Things (IoT) services in network edge. The rapid increase in adoption of IoT devices, variations in user requirements, limited processing and storage capacity of fog resources and problem of fog resources over saturation has made provisioning and allotment of computing resources in fog environment a formidable task. Satisfying application and request deadline is the most substantial challenge compared to other dynamic variations in parameters of client requirements. To curtail these issues, the integrated fog-cloud computing environment and efficient resource selection method is highly required. This paper proposed an agent based dynamic resource allocation that employs the use of host agent to analyze the QoSrequirements of application and request and select a suitable execution layer. The host agent forwards the application request to a layer agent which is responsible for the allocation of best resource that satisfies the requirement of the application request. Host agent and layers agents maintains resource information tables for matching of task and computing resources. CloudSim toolkit functionalities were extended to simulate a realistic fog environment where the proposed method is evaluated. The experimental results proved that the proposed method performs better in terms of processing time, latency and percentage QoS delivery. HIGHLIGHTS The distance between the cloud infrastructure and the edge IoT devices makes the cloud not too competent for some IoT applications, especially the sensitive ones To minimize the latency in the cloud and ensure prompt response to user requests, Fog computing, which extends the cloud services to edge network was introduced The proliferation in adoption of IoT devices and fog resource limitations has made resource scheduling in fog computing a tedious one GRAPHICAL ABSTRACT

scholarly journals Deployment of Applications Using Nginx Ingress Controller

2021 ◽  
Vol 23 (07) ◽  
pp. 352-357
Author(s):  
Gautham S ◽  
◽  
Maddula Abhijit ◽  
Prof. Sahana. B ◽  
◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Traffic Management ◽  
Virtual Machines ◽  
Storage Capacity ◽  
Production Environment ◽  
Wide Range ◽  
Software Code ◽  
Ease Of Access

Cloud computing is a method of storing and manipulating data by utilizing a network of remote servers. Cloud computing is becoming increasingly popular owing to its large storage capacity, ease of access, and wide range of services. Virtualization entered the picture when cloud computing progressed, and technologies or software such as virtual machines emerged. However, when customers’ computational needs for storage and servers rose, virtual machines were unable to meet those expectations owing to scalability and resource allocation limitations. As a result, containerization came into the picture. Containerization refers to the packaging of software code together with all of its necessary elements such as frameworks, libraries, and other dependencies such that they are isolated or segregated in their own container. Kubernetes used as an orchestration tool implements an ingress controller to route external traffic to deployments running on pods via ingress resource. This enables effective traffic management among the running applications avoiding unwanted blackouts in the production environment.

scholarly journals Resource Allocation in Cloud Computing using Heuristic Load Balancing Algorithm

2020 ◽  
Author(s):  
Anup Shrestha ◽  
Suriayati Chuprat ◽  
Nandini Mukherjee
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Load Balancing ◽  
Completion Time ◽  
Virtual Machines ◽  
It Services ◽  
Computing Environment ◽  
Load Balancing Algorithm ◽  
Better Than

Cloud computing is becoming more popular, unlike conventional computing, due to its added advantages. This is because it offers utility-based services to its subscribers upon their demand. Furthermore, this computing environment provides IT services to its users where they pay for every use. However, the increasing number of tasks requires virtual machines for them to be accomplished quickly. Load balancing a critical concern in cloud computing due to the massive increase in users' numbers. This paper proposes the best heuristic load balancing algorithm that will schedule a strategy for resource allocation that will minimize make span (completion time) in any technology that involves use cloud computing. The proposed algorithm performs better than other load balancing algorithms.

Application of Particle Swarm Optimisation-R Factor Optimisation Algorithm for Efficient Resource Allocation and Scheduling in Cloud Environment

2020 ◽  
Vol 17 (4) ◽  
pp. 1990-1998
Author(s):  
R. Valarmathi ◽  
T. Sheela
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Management ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Cost Optimization ◽  
Optimal Solution ◽  
Service Needs ◽  
Cloud Environment ◽  
Efficient Resource

Cloud computing is a powerful technology of computing which renders flexible services anywhere to the user. Resource management and task scheduling are essential perspectives of cloud computing. One of the main problems of cloud computing was task scheduling. Usually task scheduling and resource management in cloud is a tough optimization issue at the time of considering quality of service needs. Huge works under task scheduling focuses only on deadline issues and cost optimization and it avoids the significance of availability, robustness and reliability. The main purpose of this study is to develop an Optimized Algorithm for Efficient Resource Allocation and Scheduling in Cloud Environment. This study uses PSO and R factor algorithm. The main aim of PSO algorithm is that tasks are scheduled to VM (virtual machines) to reduce the time of waiting and throughput of system. PSO is a technique inspired by social and collective behavior of animal swarms in nature and wherein particles search the problem space to predict near optimal or optimal solution. A hybrid algorithm combining PSO and R-factor has been developed with the purpose of reducing the processing time, make span and cost of task execution simultaneously. The test results and simulation reveals that the proposed method offers better efficiency than the previously prevalent approaches.

Sign in / Sign up

Export Citation Format

Share Document