scholarly journals SNMP for Cloud Environment Energy Efficiency

2021 ◽  
Author(s):  
Marta Chinnici ◽  
Asif Iqbal ◽  
ah lian kor ◽  
colin pattinson ◽  
eric rondeau
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Power Consumption ◽  
Empirical Evidence ◽  
Rapid Growth ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Second Stage ◽  
The Core ◽  
Snmp Protocol

Abstract Cloud computing has seen rapid growth and environments are now providing multiple physical servers with several virtual machines running on those servers. Networks have grown larger and have become more powerful in recent years. A vital problem related to this advancement is that it has become increasingly complex to manage networks. SNMP is one standard which is applied as a solution to this management of networks problem. This work utilizes SNMP to explore the capabilities of SNMP protocol and its features for monitoring, control and automation of virtual machines and hypervisors. For this target, a stage-wise solution has been formed that obtains results of experiments from the first stage uses SNMPv3 and feed to the second stage for further processing and advancement. The target of the controlling experiments is to explore the extent of SNMP capability in the control of virtual machines running in a hypervisor, also in terms of energy efficiency. The core contribution based on real experiments is conducted to provide empirical evidence for the relation between power consumption and virtual machines.

scholarly journals Enhancement of Security and Energy Efficiency in Cloud Computing

2019 ◽  
Vol 8 (2) ◽  
pp. 2459-2462
Keyword(s):  
Energy Efficiency ◽  
Cloud Computing ◽  
Load Balancing ◽  
Job Scheduling ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Gang Scheduling ◽  
Job Scheduler ◽  
Heavy Application

This paper focuses on the job scheduling in cloud environment. Here the task has been scheduled in cloud and fog. Cloud provides services to heavy application while fog provides service to lighter application. The job scheduler would be helpful to reduce burden of cloud and help in energy optimization. The jobs are scheduled according to their types and priority. Various job scheduling algorithm such as gang scheduling, FCFS and round robin mechanism have been discussed in this research for load balancing and improve the compilation time. The simulation has been made using Matlab on virtual machines.

scholarly journals A REVIEW ON TASK ALLOCATION PERFORMANCE ISSUES IN CLOUD ENVIRONMENT

2017 ◽  
Vol 16 (3) ◽  
pp. 6225-6232
Author(s):  
Kavita Redishettywar ◽  
Prof. Rafik Juber Thekiya
Keyword(s):  
Cloud Computing ◽  
Task Allocation ◽  
Virtual Machines ◽  
Bottom Layer ◽  
Computer Hardware ◽  
Cloud Environment ◽  
Core Concept ◽  
The Core ◽  
Load Imbalance ◽  
Cloud Server

Cloud computing is an emerging paradigm in the computer industry where the computing is moved to a cloud of computers. It has become one of the buzz words of the industry. The core concept of cloud computing is, quite simply, that the vast computing resources that we need will reside somewhere out there in the cloud of computers and we’ll connect to them and use them as and when needed. Computing can be described as any activity of using and/or developing computer hardware and software. It includes everything that sits in the bottom layer, i.e. everything from raw compute power to storage capabilities. Cloud computing ties together all these entities and delivers them as a single integrated entity under its own sophisticated management. Load balancing is a mechanism that distributes the dynamic workload equally across over the nodes or virtual machines within the whole cloud server to avoid a state of conflict wherever some virtual machines are measured as heavily loaded whereas others nodes or hosts are measured as idle or doing very little work. It helps to realize a high client satisfaction and resource utilization magnitude relation, consequently increasing the performance and resource utility of the system. It additionally makes sure that each computing resource in the cloud server is distributed with efficiently and fairly among all the requests of the client. It additionally prevents bottlenecks of the system which can occur because of load imbalance.

A Multi-Tier Architecture for the Management of Supply Chain of Cloud Resources in a Virtualized Cloud Environment

2021 ◽  
Vol 14 (3) ◽  
pp. 1-17
Author(s):  
Muhammad Aliyu ◽  
Murali M. ◽  
Abdulsalam Y. Gital ◽  
Souley Boukari ◽  
Rumana Kabir ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Supply Chain ◽  
Spanning Tree ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Chain Management ◽  
Fair Scheduling ◽  
The Core ◽  
Cloud Resource ◽  
Core Function

As cloud resource demand grows, supply chain management (SCM), which is the core function of cloud computing, faces serious challenges. Quite a number of techniques have been proposed by many researchers for such a challenge. As such, numerous proposed strategies are still under reckoning and modification so as to enhance its potential. An optimized dynamic scheme that combined several algorithms' characteristics was proposed to map out such a challenge. The hybridized proposed scheme involved the meta-heuristic swarm mechanism of ant colony optimization (ACO) and deterministic spanning tree (SPT) algorithm as it obtained faster convergence chain, ensured resource utilization in least time and cost. Extensive experiments conducted in cloudsim simulator provided an efficient result in terms of minimized makespan time and throughput as compared to SPT, round robin (RR), and pre-emptive fair scheduling algorithm (PFSA) as it significantly improves performance.

An Optimal Way of VM Placement Strategy in Cloud Computing Platform Using ABCS Algorithm

2021 ◽  
Vol 12 (3) ◽  
pp. 16-38
Author(s):  
Pushpa R. ◽  
M. Siddappa
Keyword(s):  
Cloud Computing ◽  
Power Consumption ◽  
Virtual Machines ◽  
Fitness Function ◽  
Vm Placement ◽  
Migration Strategy ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Migration Cost ◽  
Chicken Swarm Optimization

In this paper, VM replacement strategy is developed using the optimization algorithm, namely artificial bee chicken swarm optimization (ABCSO), in cloud computing model. The ABCSO algorithm is the integration of the artificial bee colony (ABC) in chicken swarm optimization (CSO). This method employed VM placement based on the requirement of the VM for the completion of the particular task using the service provider. Initially, the cloud system is designed, and the proposed ABCSO-based VM placement approach is employed for handling the factors, such as load, CPU usage, memory, and power by moving the virtual machines optimally. The best VM migration strategy is determined using the fitness function by considering the factors, like migration cost, load, and power consumption. The proposed ABCSO method achieved a minimal load of 0.1688, minimal power consumption of 0.0419, and minimal migration cost of 0.0567, respectively.

On IoT and Its Integration With Cloud Computing

2021 ◽  
pp. 37-64
Author(s):  
Iram Abrar ◽  
Sahil Nazir Pottoo ◽  
Faheem Syeed Masoodi ◽  
Alwi Bamhdi
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Power Consumption ◽  
Rapid Growth ◽  
Storage Capacity ◽  
The Other ◽  
Computational Power ◽  
New Paradigm ◽  
Other Hand

Internet of things witnessed rapid growth in the last decade and is considered to be a promising field that plays an all-important role in every aspect of modern-day life. However, the growth of IoT is seriously hindered by factors like limited storage, communication capabilities, and computational power. On the other hand, cloud has the potential to support a large amount of data as it has massive storage capacity and can perform complex computations. Considering the tremendous potential of these two technologies and the manner in which they complement one another, they have been integrated to form what is commonly referred to as the cloud of things (CoT). This integration is beneficial as the resulting system is more robust, intelligent, powerful, and offers promising solutions to the users. However, the new paradigm (CoT) is faced with a significant number of challenges that need to be addressed. This chapter discusses in detail various challenges like reliability, latency, scalability, heterogeneity, power consumption, standardization, etc. faced by the cloud of things.

Receiver Initiated Deadline Aware Load Balancing Strategy (RDLBS) for Cloud Environment

2017 ◽  
Vol 8 (3) ◽  
pp. 53-73
Author(s):  
Raza Abbas Haidri ◽  
Chittaranjan Padmanabh Katti ◽  
Prem Chandra Saxena
Keyword(s):  
Cloud Computing ◽  
Performance Evaluation ◽  
Load Balancing ◽  
Virtual Machines ◽  
Turnaround Time ◽  
Cloud Environment ◽  
Computing Technology ◽  
Trade Off ◽  
The Cost ◽  
State Of Art

The emerging cloud computing technology is the attention of both commercial and academic spheres. Generally, the cost of the faster resource is more than the slower ones, therefore, there is a trade-off between deadline and cost. In this paper, the authors propose a receiver initiated deadline aware load balancing strategy (RDLBS) which tries to meet the deadline of the requests and optimizes the rate of revenue. RDLBS balances the load among the virtual machines (VMs) by migrating the request from the overloaded VMs to underloaded VMs. Turnaround time is also computed for the performance evaluation. The experiments are conducted by using CloudSim simulator and results are compared with existing state of art algorithms with similar objectives.

Provisioning of Virtual Machines in the Context of an Auto-Scaling Cloud Computing Environment

2020 ◽  
Vol 17 (6) ◽  
pp. 2430-2434
Author(s):  
R. S. Rajput ◽  
Dinesh Goyal ◽  
Rashid Hussain ◽  
Pratham Singh
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Resource Utilization ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Auto Scaling ◽  
Adequate Resource ◽  
Cloud Resources

The cloud computing environment is accomplishing cloud workload by distributing between several nodes or shift to the higher resource so that no computing resource will be overloaded. However, several techniques are used for the management of computing workload in the cloud environment, but still, it is an exciting domain of investigation and research. Control of the workload and scaling of cloud resources are some essential aspects of the cloud computing environment. A well-organized load balancing plan ensures adequate resource utilization. The auto-scaling is a technique to include or terminate additional computing resources based on the scaling policies without involving humans efforts. In the present paper, we developed a method for optimal use of cloud resources by the implementation of a modified auto-scaling feature. We also incorporated an auto-scaling controller for the optimal use of cloud resources.

scholarly journals Load balancing in Cloud Computing: Issues and Challenges

2021 ◽  
Vol 12 (2) ◽  
pp. 3224-3231
Author(s):  
K. Balaji, Et. al.
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Load Distribution ◽  
Data Centers ◽  
Job Scheduling ◽  
Virtual Machines ◽  
Cloud Services ◽  
Cloud Environment ◽  
Computing Technology ◽  
Load Balancer

The evolution of IT led Cloud computing technology emerge as a new prototype in providing the services to its users on rented basis at any time or place. Considering the flexibility of cloud services, innumerable organizations switched their businesses to the cloud technology by setting up more data centers. Nevertheless, it has become mandatory to provide profitable execution of tasks and appropriate  resource utilization. A few approaches were outlined in literature to enhance performance, job scheduling, storage resources, QoS and load distribution. Load balancing concept permits data centers to avert over-loading or under-loading in virtual machines that as such is an issue in cloud computing domain. Consequently, it necessitate the researchers to layout and apply a proper load balancer for cloud environment. The respective study represents a view of problems and threats faced by the current load balancing techniques and make the researchers find more efficient algorithms.

scholarly journals REVIEW OF TASK SCHEDULING METHODS FOR REAL TIME TASKS IN CLOUD ENVIRONMENT

2020 ◽  
Vol 5 (1) ◽  
pp. 85-89
Author(s):  
Dinkan Patel ◽  
Anjuman Ranavadiya
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Dynamic Scheduling ◽  
Scheduling Algorithms ◽  
Minimum Time ◽  
Cloud Environment ◽  
Static Scheduling ◽  
On Demand

Cloud Computing is a type of Internet model that enables convenient, on-demand resources that can be used rapidly and with minimum effort. Cloud Computing can be IaaS, PaaS or SaaS. Scheduling of these tasks is important so that resources can be utilized efficiently with minimum time which in turn gives better performance. Real time tasks require dynamic scheduling as tasks cannot be known in advance as in static scheduling approach. There are different task scheduling algorithms that can be utilized to increase the performance in real time and performing these on virtual machines can prove to be useful. Here a review of various task scheduling algorithms is done which can be used to perform the task and allocate resources so that performance can be increased.

scholarly journals Efficient utilization of energy consumption in cloud environment

2018 ◽  
Vol 7 (1.7) ◽  
pp. 189
Author(s):  
G Soniya Priyatharsini ◽  
N Malarvizhi
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Data Center ◽  
Virtual Machines ◽  
Vital Role ◽  
Cloud Environment ◽  
Efficient Utilization ◽  
Server Consolidation ◽  
Resource Request

In the tremendous growth of the cloud computing, server consolidation plays a vital role. It gives more benefits also it gives the pollution towards the nature. Inspite of avoiding this, saving of energy in the data center or the cluster is more important. In this regard this paper proposes a method of which reduces the energy consumption. This is done by identifying the active physical machines and the remaining machines were kept in the sleep or off mode according to the user’s availability. Here Virtual Machines (VMs) are grouped under the particular types. Based on this type the customer’s resource request can be maintained.

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