scholarly journals DLMNN: A Deep Learning Modified Neural Network for Balancing the Load of Cloudlets on Cloud

2019 ◽  
Vol 9 (1) ◽  
pp. 6524-6532
Keyword(s):  
Neural Network ◽  
Cloud Computing ◽  
Feature Selection ◽  
Cloud Services ◽  
Second Step ◽  
Cloud Environment ◽  
Cloud Platform ◽  
Cloud Architecture ◽  
Load Balancing Algorithm ◽  
Cloud Servers

Cloud Computing a revolution in the computing world, has enabled the users to utilize the services on the Cloud platform from anywhere at any time. As there is an increase in the demand for the utilization of a cloud environment, there are several challenges to be addressed by the companies or organizations to provide uninterrupted cloud services. To make the cloud services available without interruption, the challenge of balancing the load on cloud servers is a must. Proper allocation of load on the servers optimize the performance of the cloud and improves the efficiency to offer uninterrupted services. Recent studies have shown, cloud always needs to have a capable algorithm to distribute the load on servers of cloud architecture to be available to process cloudlets submitted by the customers. Our paper looks for a new load balancing algorithm that uses the concepts of neural network and is used to allocate the tasks in the cloud. The proposed algorithm consists of two steps. First, Features of tasks and cloud servers are extracted, and the necessary features are selected. The feature selection can be done by using MPCA. In the second step, the selected features are sent as input to the DLMNN algorithm to schedule the task in the cloud. Finally, the experimental results of the proposed DLMNN are compared with some existing algorithms.

Secure Architecture for Cloud Environment

2018 ◽  
pp. 910-925
Author(s):  
Kashif Munir ◽  
Sellapan Palaniappan
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
End Users ◽  
Cloud Services ◽  
The Internet ◽  
Security Threats ◽  
Cloud Environment ◽  
Cloud Architecture ◽  
The World ◽  
Secure Architecture

Cloud computing is set of resources and services offered through the internet. Cloud services are delivered from data centers located throughout the world. Enterprises are rapidly adopting cloud services for their businesses, measures need to be developed so that organizations can be assured of security in their businesses and can choose a suitable vendor for their computing needs. In this chapter we identify the most vulnerable security threats/attacks in cloud computing, which will enable both end users and vendors to know about the key security threats associated with cloud computing and propose relevant solution directives to strengthen security in the cloud environment. This chapter also discusses secure cloud architecture for organizations to strengthen the security.

Secure Architecture for Cloud Environment

2015 ◽  
pp. 65-79
Author(s):  
Kashif Munir ◽  
Sellapan Palaniappan
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
End Users ◽  
Cloud Services ◽  
The Internet ◽  
Security Threats ◽  
Cloud Environment ◽  
Cloud Architecture ◽  
The World ◽  
Secure Architecture

Cloud computing is set of resources and services offered through the internet. Cloud services are delivered from data centers located throughout the world. Enterprises are rapidly adopting cloud services for their businesses, measures need to be developed so that organizations can be assured of security in their businesses and can choose a suitable vendor for their computing needs. In this chapter we identify the most vulnerable security threats/attacks in cloud computing, which will enable both end users and vendors to know about the key security threats associated with cloud computing and propose relevant solution directives to strengthen security in the cloud environment. This chapter also discusses secure cloud architecture for organizations to strengthen the security.

scholarly journals An Optimal Solution in cloud computing for Load Balancing using Genetic Algorithm

2019 ◽  
Vol 8 (6) ◽  
pp. 2490-2494
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machines ◽  
Optimal Solution ◽  
Cloud Services ◽  
Research Trend ◽  
Schedule Length ◽  
Research Gap ◽  
Load Balancing Algorithm

Cloud computing is a research trend which bring various cloud services to the users. Cloud environment face various challenges and issues to provide efficient services. In this paper, a novel Genetic Algorithm based load balancing algorithm has been implemented to balance the load in the network. The literature review has been studied to understand the research gap. More specifically, load balancing technique authenticate the network by enabling Virtual Machines (VM). The proposed technique has been further evaluated using the Schedule Length Runtime (SLR) and Energy consumption (EC) parameters. Overall, the effective results has been obtained such as 46% improvement in consuming the energy and 12 % accuracy for the SLR measurement. In addition, results has been compared with the conventional approaches to validate the outcomes.

A Review of Intrusion Detection Systems in Cloud Computing

2019 ◽  
pp. 54-83
Author(s):  
Chiba Zouhair ◽  
Noreddine Abghour ◽  
Khalid Moussaid ◽  
Amina El Omri ◽  
Mohamed Rida
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Detection System ◽  
Security Requirements ◽  
Intrusion Detection Systems ◽  
Cloud Environment ◽  
Detection Techniques ◽  
Detection Systems ◽  
Cloud Architecture ◽  
Data Source

Security is a major challenge faced by cloud computing (CC) due to its open and distributed architecture. Hence, it is vulnerable and prone to intrusions that affect confidentiality, availability, and integrity of cloud resources and offered services. Intrusion detection system (IDS) has become the most commonly used component of computer system security and compliance practices that defends cloud environment from various kinds of threats and attacks. This chapter presents the cloud architecture, an overview of different intrusions in the cloud, the challenges and essential characteristics of cloud-based IDS (CIDS), and detection techniques used by CIDS and their types. Then, the authors analyze 24 pertinent CIDS with respect to their various types, positioning, detection time, and data source. The analysis also gives the strength of each system and limitations in order to evaluate whether they carry out the security requirements of CC environment or not.

An Unified Secured Cloud System for the Education Sector of India

2020 ◽  
pp. 69-102
Author(s):  
Kimaya Arun Ambekar ◽  
Kamatchi R.
Keyword(s):  
Cloud Computing ◽  
Network Security ◽  
Access Control ◽  
Identity Management ◽  
Data Encryption ◽  
Cloud Services ◽  
Education Sector ◽  
Cloud Environment ◽  
The World ◽  
Community Cloud

Cloud computing is based on years of research on various computing paradigms. It provides elasticity, which is useful in the situations of uneven ICT resources demands. As the world is moving towards digitalization, the education sector is expected to meet the pace. Acquiring and maintaining the ICT resources also necessitates a huge amount of cost. Education sector as a community can use cloud services on various levels. Though the cloud is very successfully running technology, it also shows some flaws in the area of security, privacy and trust. The research demonstrates a model in which major security areas are covered like authorization, authentication, identity management, access control, privacy, data encryption, and network security. The total idea revolves around the community cloud as university at the center and other associated colleges accessing the resources. This study uses OpenStack environment to create a complete cloud environment. The validation of the model is performed using some cases and some tools.

A Two-Phase Load Balancing Algorithm for Cloud Environment

2021 ◽  
Vol 13 (1) ◽  
pp. 38-55
Author(s):  
Archana Singh ◽  
Rakesh Kumar
Keyword(s):  
Load Balancing ◽  
Data Center ◽  
Virtual Machines ◽  
Second Phase ◽  
Cloud Environment ◽  
Cloud Platform ◽  
Efficient Utilization ◽  
Two Phase ◽  
Excessive Load ◽  
Load Balancing Algorithm

Load balancing is the phenomenon of distributing workload over various computing resources efficiently. It offers enterprises to efficiently manage different application or workload demands by allocating available resources among different servers, computers, and networks. These services can be accessed and utilized either for home use or for business purposes. Due to the excessive load on the cloud, sometimes it is not feasible to offer all these services to different users efficiently. To solve this excessive load issue, an efficient load balancing technique is used to offer satisfactory services to users as per their expectations also leading to efficient utilization of resources and applications on the cloud platform. This paper presents an enhanced load balancing algorithm named as a two-phase load balancing algorithm. It uses a two-phase checking load balancing approach where the first phase is to divide all virtual machines into two different tables based on their state, that is, available or busy while in the second phase, it equally distributes the loads. The various parameters used to measure the performance of the proposed algorithm are cost, data center processing time, and response time. Cloud analyst simulation tool is used to simulate the algorithm. Simulation results demonstrate superiority of the algorithm with existing ones.

IoT Based Agriculture as a Cloud and Big Data Service

2020 ◽  
pp. 1499-1521
Author(s):  
Sukhpal Singh Gill ◽  
Inderveer Chana ◽  
Rajkumar Buyya
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Cloud Services ◽  
Cloud Environment ◽  
Big Data Technologies ◽  
Sensor Networking ◽  
New Applications

Cloud computing has transpired as a new model for managing and delivering applications as services efficiently. Convergence of cloud computing with technologies such as wireless sensor networking, Internet of Things (IoT) and Big Data analytics offers new applications' of cloud services. This paper proposes a cloud-based autonomic information system for delivering Agriculture-as-a-Service (AaaS) through the use of cloud and big data technologies. The proposed system gathers information from various users through preconfigured devices and IoT sensors and processes it in cloud using big data analytics and provides the required information to users automatically. The performance of the proposed system has been evaluated in Cloud environment and experimental results show that the proposed system offers better service and the Quality of Service (QoS) is also better in terms of QoS parameters.

A Proficient Approach for Load Balancing in Cloud Computing-Join Minimum Loaded Queue

2020 ◽  
Vol 11 (1) ◽  
pp. 12-36
Author(s):  
Minakshi Sharma ◽  
Rajneesh Kumar ◽  
Anurag Jain
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Resource Utilization ◽  
Cloud Service ◽  
Cloud Environment ◽  
Cloud Service Provider ◽  
Qos Parameters ◽  
Load Balancing Algorithm

Cloud load balancing is done to persist the services in the cloud environment along with quality of service (QoS) parameters. An efficient load balancing algorithm should be based on better optimization of these QoS parameters which results in efficient scheduling. Most of the load balancing algorithms which exist consider response time or resource utilization constraints but an efficient algorithm must consider both perspectives from the user side and cloud service provider side. This article presents a load balancing strategy that efficiently allocates tasks to virtualized resources to get maximum resource utilization in minimum response time. The proposed approach, join minimum loaded queue (JMLQ), is based on the existing join idle queue (JIQ) model that has been modified by replacing idle servers in the I-queues with servers having one task in execution list. The results of simulation in CloudSim verify that the proposed approach efficiently maximizes resource utilization by reducing the response time in comparison to its other variants.

Migration of applications to the Cloud: a user-driven approach

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
Anastasia Panori ◽  
Agustín González-Quel ◽  
Miguel Tavares ◽  
Dimitris Simitopoulos ◽  
Julián Arroyo
Keyword(s):  
Cloud Computing ◽  
Smart City ◽  
Public Services ◽  
Cloud Services ◽  
Cloud Environment ◽  
Public Cloud ◽  
Cloud Infrastructure ◽  
Migration Process ◽  
Empirical Application ◽  
Selection Of

During the last decade, there has been an increased interest on cloud computing and especially on the adoption of public cloud services. The process of developing cloud-based public services or migrating existing ones to the Cloud is considered to be of particular interest—as it may require the selection of the most suitable applications as well as their transformation to fit in the new cloud environment. This paper aims at presenting the main findings of a migration process regarding smart city applications to a cloud infrastructure. First, it summarises the methodology along with the main steps followed by the cities of Agueda (Portugal), Thessaloniki (Greece) and Valladolid (Spain) in order to implement this migration process within the framework of the STORM CLOUDS project. Furthermore, it illustrates some crucial results regarding monitoring and validation aspects during the empirical application that was conducted via these pilots. These findings should be received as a helpful experience for future efforts designed by cities or other organisations that are willing to move their applications to the Cloud.

Simulation of Cloud Platform Supporting Wireless Sensor Network

2020 ◽  
Vol 17 (6) ◽  
pp. 2613-2620
Author(s):  
Shweta Sharma ◽  
Amandeep Kaur
Keyword(s):  
Neural Network ◽  
Cloud Computing ◽  
Power Management ◽  
Wireless Sensor ◽  
Simulation Tool ◽  
Management Issue ◽  
Cloud Platform ◽  
Area Of Interest ◽  
Military Applications ◽  
Artificial Neural Network Ann

WSN has been an area of interest for a lot of researchers as well as the industry specialist due to its high usage in healthcare, military applications and in robotics. The focus area of WSN has been energy consumption. Integration of Cloud Computing in WSN has been observed by couple of research scholars as Cloud has been observed to be struggling against the power management issue. This paper presents a novel solution of power management in Cloud computing which uses the concept of clustering borrowed from WSN. The jobs are grouped or Clustered as per the algorithmic architecture of WSN and the minimization of migration is attempted using Genetic Algorithm (GA). Artificial Neural Network (ANN) is used as a conjunction to GA for energy efficiency. MATLAB is used as a simulation tool and consumed energy has been evaluated as the major evaluation parameter.

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