scholarly journals Spider Monkey Optimization based Energy-Efficient Resource Allocation in Cloud Environment

2022 ◽  
Vol 19 (1) ◽  
pp. 1710
Author(s):  
Jitendra Kumar Samriya ◽  
Narander Kumar
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Spider Monkey ◽  
Energy Utilization ◽  
Broadband Service ◽  
Time Migration ◽  
Wide Range ◽  
Spider Monkey Optimization ◽  
Cloud Computation

The origin of Cloud computing is from the principle of utility computing, which is characterized as a broadband service providing storage and computational resources. It provides a large variety of processing options and heterogeneous tools, allowing it to meet the needs of a wide range of applications at different levels. As a result, resource allocation and management are critical in cloud computing. In this work, the Spider Monkey Optimization (SMO) is used for attaining an optimized resource allocation. The key parameters considered to regulate the performance of SMO are its application time, migration time, and resource utilization. Energy consumption is another key factor in cloud computation which is also considered in this work. The Green Cloud Scheduling Model (GCSM) is followed in this work for the energy utilization of the resources. This is done by scheduling the heterogeneity tasks with the support of a scheduler unit which schedules and allocates the tasks which are deadline-constrained enclosed to nodes which are only energy-conscious. Assessing these methods is formulated using the cloud simulator programming process. The parameter used to determine the energy efficiency of this method is its energy consumption. The simulated outcome of the proposed approach proves to be effective in response time, makespan, energy consumption along with resource utility corresponding to the existing algorithms.

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 Multi-Objective Spider Monkey Optimization for Energy Efficient clustering and Routing in wireless sensor networks

2021 ◽  
Author(s):  
Duraimurugan Samiayya ◽  
Avudaiammal Ramalingam
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Fitness Function ◽  
Heavy Traffic ◽  
Spider Monkey ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Multi Objective ◽  
Spider Monkey Optimization

Abstract In wireless sensor network (WSN), the gateways far away from the base station (BS) uses the gateways nearer to the BS to forward the data. It causes heavy traffic to the gateways in proximity with the BS. They need to manage this heavy traffic load but it leads to additional energy consumption and reduction in network lifetime. In order to overcome these issues, loads around the gateways need to be balanced. In this paper, multi objective based spider monkey optimization (MOSMO) has been presented to balance the load and to improve the network lifetime through energy efficient routing and clustering. The objective functions such as routing fitness and clustering fitness have been considered for optimal routing and clustering. The routing fitness function is found by incorporating both the minimum distance traversed by the gateways and minimum number of the gateway hops. The clustering fitness function is the minimum fitness function of gateways. The fitness function of each gateway is computed based on both the mean load of gateways as well as the distance between gateways and BS. The performance of the proposed MOSMO based routing and clustering scheme is compared with the existing Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) based routing and clustering scheme. The QoS features such as delay, energy consumption, delivery ratio, throughput and network lifetime with various node density are analyzed. The proposed work is simulated using MATLAB. The results show that, the reduction in delay and energy consumption is about 18% and 17% respectively whereas improvement in delivery ratio, throughput and network life time is about 15%, 24% and 19% respectively when compared to the existing PSO and GWO methods.

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.

An Energy-Efficient Resource Scheduling Algorithm for Cloud Computing based on Resource Equivalence Optimization

2016 ◽  
Vol 8 (2) ◽  
pp. 43-57 ◽  
Author(s):  
Li Mao ◽  
De Yu Qi ◽  
Wei Wei Lin ◽  
Bo Liu ◽  
Ye Da Li
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Data Center ◽  
Energy Efficient ◽  
Resource Scheduling ◽  
Constraint Satisfaction Problems ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Efficient Resource

With the rapid growth of energy consumption in global data centers and IT systems, energy optimization has become an important issue to be solved in cloud data center. By introducing heterogeneous energy constraints of heterogeneous physical servers in cloud computing, an energy-efficient resource scheduling model for heterogeneous physical servers based on constraint satisfaction problems is presented. The method of model solving based on resource equivalence optimization is proposed, in which the resources in the same class are pruning treatment when allocating resource so as to reduce the solution space of the resource allocation model and speed up the model solution. Experimental results show that, compared with DynamicPower and MinPM, the proposed algorithm (EqPower) not only improves the performance of resource allocation, but also reduces energy consumption of cloud data center.

scholarly journals Resource Intensification for Mobile Devices Using the Approximate Computing Entities

2020 ◽  
Vol 2 (1) ◽  
pp. 26-36
Author(s):  
Dr. Suma V
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Approximate Computing ◽  
Local Resources ◽  
And Storage ◽  
High Storage

The mobile devices are termed to highly potential due to their capability of rendering services without being plugged to the electric grid. These device are becoming highly prominent due to their constant progress in computing as well as storing capacities and as they are very much closer to the users. Despites its advantages it still faces many problems due to the load balancing and energy consumption due to its limited battery limited and storage availability as some applications or the video downloading requires high storage facilities consuming majority of the energy in turn reducing the performance of the mobile devices. So as to improve the performance and the capability of the mobile devices the mobile cloud computing that integrates the mobile devices with the cloud paradigm has emerged as a promising paradigm. This enables the augmentation of the local resources for the mobile devices to enhance its capabilities in order to improve its functioning. This is basically done by proper offloading and resource allocation. The proposed method in the paper utilizes the optimal offloading strategy (Single and double strand offloading) and follows an Ant colony optimization based resource allocation for improving the functioning the mobile devices in terms of energy consumption and storage.

scholarly journals Network Slicing on 5G Vehicular Cloud Computing Systems

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1474
Author(s):  
Emmanouil Skondras ◽  
Angelos Michalas ◽  
Dimitrios J. Vergados ◽  
Emmanouel T. Michailidis ◽  
Nikolaos I. Miridakis ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Multiple Input Multiple Output ◽  
Access Network ◽  
Network Access ◽  
Network Slicing ◽  
Vehicular Cloud Computing ◽  
Vehicular Cloud ◽  
Sdn Controller

Fifth generation Vehicular Cloud Computing (5G-VCC) systems support various services with strict Quality of Service (QoS) constraints. Network access technologies such as Long-Term Evolution Advanced Pro with Full Dimensional Multiple-Input Multiple-Output (LTE-A Pro FD-MIMO) and LTE Vehicle to Everything (LTE-V2X) undertake the service of an increasing number of vehicular users, since each vehicle could serve multiple passenger with multiple services. Therefore, the design of efficient resource allocation schemes for 5G-VCC infrastructures is needed. This paper describes a network slicing scheme for 5G-VCC systems that aims to improve the performance of modern vehicular services. The QoS that each user perceives for his services as well as the energy consumption that each access network causes to user equipment are considered. Subsequently, the satisfactory grade of the user services is estimated by taking into consideration both the perceived QoS and the energy consumption. If the estimated satisfactory grade is above a predefined service threshold, then the necessary Resource Blocks (RBs) from the current Point of Access (PoA) are allocated to support the user’s services. On the contrary, if the estimated satisfactory grade is lower than the aforementioned threshold, additional RBs from a Virtual Resource Pool (VRP) located at the Software Defined Network (SDN) controller are committed by the PoA in order to satisfy the required services. The proposed scheme uses a Management and Orchestration (MANO) entity implemented by a SDN controller, orchestrating the entire procedure avoiding situations of interference from RBs of neighboring PoAs. Performance evaluation shows that the suggested method improves the resource allocation and enhances the performance of the offered services in terms of packet transfer delay, jitter, throughput and packet loss ratio.

Nelder Mead-based Spider Monkey Optimization for Optimal Power and Channel Allocation in MANET

2020 ◽  
pp. 2150119
Author(s):  
S. Jothi ◽  
A. Chandrasekar
Keyword(s):  
Energy Consumption ◽  
Optimization Algorithm ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Optimal Allocation ◽  
Channel Allocation ◽  
Spider Monkey ◽  
Total Energy Consumption ◽  
Optimal Power ◽  
Spider Monkey Optimization

The Mobile Ad-hoc Network (MANET) includes both the optimal allocation of shared channels and power in a network. Hence, obtaining the trade-off between energy consumption and delay is the major challenge. The source-constrained and maximizing lifetime of the unreliable wireless network includes optimal power allocation. We proposed an Integrated Spider Monkey Optimization Algorithm (ISMOA) for the minimization of energy consumption and the enhancement of throughput in MANET. The Nelder Mead Model (NMM) is used to improve the performance of the local leader stage in the spider monkey optimization algorithm. In this work, the novel approach is used to improve the performance of total energy consumption, throughput, and delay. The experimental works are executed in NS-2 software. The experimental results demonstrate the low delay, energy consumption with high throughput performance. Moreover, the proposed method outperforms the optimal performance compared to state-of-the-art methods.

scholarly journals Efficient of Floading Approach for Resource Allocation and Optimization in Mobile Edge Computing Environment

2021 ◽  
Vol 12 (6) ◽  
pp. 2581-2593
Author(s):  
C. Anuradha, M. Ponnavaikko
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Smart Cities ◽  
Edge Computing ◽  
Cloud Services ◽  
Security And Privacy ◽  
The Internet ◽  
Mobile Edge Computing ◽  
Major Barrier

Cloud computing provides a platform for services and resources over the internet for users. The large pool of data resources and services has enabled the emergence of several novel applications such as smart grids, smart environments, and virtual reality. However, the state-of-the-art of cloud computing faces a delay constraint, which becomes a major barrier for reliable cloud services. This constraint is mostly highlighted in the case of smart cities (SC) and the Internet of Things (IoT). Therefore, the recent cloud computing paradigm has poor performance and cannot meet the low delay, navigation, and mobility support requirements.Machine-to-machine (M2M) connectivity has drawn considerable interest from both academia and industry with a growing number of machine-type communication devices (MTCDs). The data links with M2M communications are usually small but high bandwidth, unlike conventional networking networks, demanding performance management of both energy consumption and computing. The main challenges faced in mobile edge computing are task offloading, congestion control, Resource allocation, security and privacy issue, mobility and standardization .Our work mainly focus on offloading based resource allocation and security issues by analyzing the network parameters like reduction of latency and improvisation of bandwidth involved in cloud environment. The cloudsim simulation tool has been utilized to implement the offload balancing mechanism to decrease the energy consumption and optimize the computing resource allocation as well as improve computing capability.

scholarly journals What do programmers know about the energy consumption of software?

2015 ◽  
Author(s):  
Candy Pang ◽  
Abram Hindle ◽  
Bram Adams ◽  
Ahmed E Hassan
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Programming Languages ◽  
Best Practice ◽  
Battery Life ◽  
Infrastructure Management ◽  
Energy Education ◽  
Wide Range ◽  
Reduce Energy Consumption ◽  
Software Energy Consumption

Traditionally, programmers have received a wide range of training on programming languages and methodologies, but rarely about software energy consumption. Yet, the popularity of mobile devices and cloud computing require increased awareness about software energy consumption. On a mobile device, computation is often limited by the battery life. Under the demands of cloud computing, data centers struggle to reduce energy consumption through vir- tualization and data center infrastructure management (DCIM) systems. Efficient energy consumption of software is increasingly becoming an important non-functional requirement for programmers. However, are programmers knowledgeable enough about software energy consumption? Do programmers base their implementation decision on popular beliefs? In this article, we survey over 100 programmers for their knowledge of software energy con- sumption. We find that programmers have limited knowledge about energy efficiency, lack the knowledge about the best practice to reduce energy consumption of software, and are often unsure about how software consumes energy. Education about the importance of energy effective software will benefit the programmers. Our results highlight the need for training about energy consumption and efficiency.

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