scholarly journals Task scheduling Algorithms in Multi cloud Environment

2020 ◽  
Vol 8 (6) ◽  
pp. 4530-4533
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Task Scheduling ◽  
Resource Sharing ◽  
Optimal Solution ◽  
Cloud Environment ◽  
Server Side ◽  
The Past ◽  
Multiple Clouds ◽  
Multi Cloud

One of the most commonly used technology with massive demands in the field of distributed computing is cloud computing. Cloud computing has evolved in various forms like single cloud, hybrid cloud and multi-cloud. The evolution of cloud to handle hundred and thousands of user demands, at a time, thereby facilitating resource sharing, reduction in loss of information, elimination of data storage on server side and many many more the topic of task scheduling will be prominent in all forms of cloud computing and in distributed architecture. Here, we discuss the multiple cloud architecture and the scheduling techniques applied to evenly distribute the workload across multiple clouds. Algorithms like Cloud list Scheduling (CLS), Cloud min min scheduling (CMMS), Minimum completion cloud (MCC), Median max algorithm (MEMAX), Multiobjective scheduling (MOS) are some methods suggested in the past for finding a near to optimal solution for task allocation.

Hybrid Multi-Cloud Demystifying SLAs for Smart City Enterprises Using IoT Applications

2020 ◽  
pp. 52-67
Author(s):  
Lubna Luxmi Dhirani ◽  
Thomas Newe ◽  
Shahzad Nizamani
Keyword(s):  
Cloud Computing ◽  
Smart City ◽  
Influencing Factor ◽  
It Management ◽  
Peak Performance ◽  
Cloud Environment ◽  
Iot Applications ◽  
Single Standard ◽  
Multiple Clouds ◽  
Multi Cloud

Cloud computing migrations are increasing rapidly. The main influencing factor being IT management costs. IoT-based enterprises that started their cloud journey by setting up small private clouds within their enterprise have often found that as the applications and services they use broaden. Then the shift towards incorporating public clouds becomes inevitable. The current problem that many of these firms are encountering is the difficulty of managing multiple clouds that reside within different vendors running on different platforms, computational requirements, and vendor SLAs. Lack of support for a single standard for an overall multi-cloud hybrid model exposes the hybrid IT-management to further threats. This makes it difficult for an adopting enterprise to manage and maintain its cloud-based systems during peak performance hours, which often leads to system downtime. This chapter discusses various SLA issues specific to a hybrid multi-cloud environment and suggests possible solutions to help adopting firms in their management.

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.

Local Pollination-Based Moth Search Algorithm for Task-Scheduling Heterogeneous Cloud Environment

2020 ◽  
Author(s):  
M Gokuldhev ◽  
G Singaravel
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Search Algorithm ◽  
Scheduling Algorithm ◽  
Optimal Solution ◽  
Flower Pollination Algorithm ◽  
Cloud Environment ◽  
Resource Needs ◽  
Implementation Time ◽  
Heterogeneous Cloud

Abstract Nowadays, Cloud computing is a new computing model in the field of information technology and research. Generally, the cloud environment aims in providing the resource that depends upon the user’s necessity. The major problem caused by cloud computing is task scheduling. Nevertheless, the previous scheduling methods concentrate only on the resource needs, memory, implementation time and cost. In this paper, we introduced an optimal task-scheduling algorithm of the local pollination-based moth search algorithm (LPMSA), which is the hybridization of moth search algorithm (MSA) and flower pollination algorithm (FPA). The proposed LPMSA chooses an optimal solution for proper task scheduling in the cloud. Moreover, the exploitation capacity of MSA is improved by using the local search of the FPA algorithm. In this work, we use 2-fold simulation processes that are implemented under the platform of JAVA. The proposed LPMSA for task-scheduling performance is evaluated using low and high heterogeneous machines with uniform and non-uniform parameters. The experimental analysis demonstrates that the proposed LPMSA approach is well suitable for cloud task scheduling thereby reducing the makespan and energy consumption during proper task scheduling.

scholarly journals Data mining techniques and algorithms in cloud environment a review

2018 ◽  
Vol 7 (1.9) ◽  
pp. 265 ◽  
Author(s):  
K Rajamani ◽  
D Sheela
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Data Storage ◽  
Resource Sharing ◽  
Cloud Environment ◽  
Computing Environment ◽  
Significant Information ◽  
Cloud Computing Environment ◽  
Data Mining Techniques ◽  
Different Types

Cloud Computing is resourceful in which computing resources are made available on- demand to the user as needed. Data mining is a process of discovering interesting patterns from a large amount of data. The difficulty is in collecting these data and carrying out compu-tations to get the significant information. Data mining techniques and applications can be effectively used in cloud computing environment. Data mining and the cloud computing are considered as major technologies. The data mining in cloud computing allows organizations to centralize the management of software and data storage. This paper provides a review of various data mining techniques and different types of algorithms in cloud computing which can be used for resource sharing.

Task Scheduling for Multi-Cloud Computing Subject to Security and Reliability Constraints

2021 ◽  
Vol 8 (4) ◽  
pp. 848-865
Author(s):  
Qing-Hua Zhu ◽  
Huan Tang ◽  
Jia-Jie Huang ◽  
Yan Hou
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Security And Reliability ◽  
Multi Cloud

PpBAC

2018 ◽  
Vol 30 (4) ◽  
pp. 14-31 ◽  
Author(s):  
Suyel Namasudra ◽  
Pinki Roy
Keyword(s):  
Cloud Computing ◽  
Access Control ◽  
Data Storage ◽  
Control Method ◽  
Cloud Environment ◽  
Authorized User ◽  
Control Scheme ◽  
Control Schemes ◽  
Confidential Data ◽  
Efficient Data

This article describes how nowadays, cloud computing is one of the advanced areas of Information Technology (IT) sector. Since there are many hackers and malicious users on the internet, it is very important to secure the confidentiality of data in the cloud environment. In recent years, access control has emerged as a challenging issue of cloud computing. Access control method allows data accessing of an authorized user. Existing access control schemes mainly focus on the confidentiality of the data storage. In this article, a novel access control scheme has been proposed for efficient data accessing. The proposed scheme allows reducing the searching cost and accessing time, while providing the data to the user. It also maintains the security of the user's confidential data.

Multi-Cloud Data Management Using Shamir's Secret Sharing and Quantum Byzantine Agreement Schemes

2016 ◽  
pp. 1205-1222
Author(s):  
Mohammed A. AlZain ◽  
Alice S. Li ◽  
Ben Soh ◽  
Eric Pardede
Keyword(s):  
Cloud Computing ◽  
Performance Evaluation ◽  
Data Management ◽  
Data Storage ◽  
Secret Sharing ◽  
Byzantine Agreement ◽  
Management Model ◽  
Cloud Data ◽  
Cloud Data Management ◽  
Multi Cloud

Cloud computing is a phenomenal distributed computing paradigm that provides flexible, low-cost on-demand data management to businesses. However, this so-called outsourcing of computing resources causes business data security and privacy concerns. Although various methods have been proposed to deal with these concerns, none of these relates to multi-clouds. This paper presents a practical data management model in a public and private multi-cloud environment. The proposed model BFT-MCDB incorporates Shamir's Secret Sharing approach and Quantum Byzantine Agreement protocol to improve trustworthiness and security of business data storage, without compromising performance. The performance evaluation is carried out using a cloud computing simulator called CloudSim. The experimental results show significantly better performance in terms of data storage and data retrieval compared to other common cloud cryptographic based models. The performance evaluation based on CloudSim experiments demonstrates the feasibility of the proposed multi-cloud data management model.

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