Machine Learning Based Efficient and Secure Storage Mechanism in Cloud Computing

The cloud is an online platform that offers services for end-users by ensuring the Quality of services (QoS) of the data. Since, the user’s access data through the internet, therefore problem like Security and confidentiality of cloud data appears. To resolve this problem, encryption mechanism named as Rivest–Shamir–Adleman (RSA) with Triple Data Encryption Standard (DES) approach is used in hybridization. This paper mainly focused on two issues, such as Security and Storage of data. The Security of cloud data is resolved using the encryption approach, whereas, the data storage is performed using Modified Best Fit Decreasing (MBFD) with Whale Optimization algorithm (WOA)&Artificial Neural Network (ANN) approach. The neural network with the whale as an optimization approach model makes sure the high confidentiality of cloud data storage in a managed way. From the experiment, it is analyzed that the proposed cloud system performs better in terms of energy consumption, delay, and Service Level Agreement (SLA) violation.

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Machine Learning Approach to Task Scheduling in Cloud Computing

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9048 ◽

2020 ◽

Vol 17 (9) ◽

pp. 4213-4218

Author(s):

H. S. Madhusudhan ◽

T. Satish Kumar ◽

G. Mahesh

Keyword(s):

Neural Network ◽

Machine Learning ◽

Genetic Algorithm ◽

Cloud Computing ◽

Task Scheduling ◽

Research Work ◽

Service Level Agreement ◽

Service Level ◽

Machine Learning Approach ◽

Artificial Neural Network Ann

Cloud computing provides on demand service on internet using network of remote servers. The pivotal role for any cloud environment would be to schedule tasks and the virtual machine scheduling have key role in maintaining Quality of Service (QOS) and Service Level Agreement (SLA). Task scheduling is the process of scheduling task (user requests) to certain resources and it is an NP-complete problem. The primary objectives of scheduling algorithms are to minimize makespan and improve resource utilization. In this research work an attempt is made to implement Artificial Neural Network (ANN), which is a methodology in machine learning technique and it is applied to implement task scheduling. It is observed that neural network trained with genetic algorithm will outperforms default genetic algorithm by an average efficiency of 25.56%.

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Energy-efficient cloud data center with fair service level agreement for green computing

Cluster Computing ◽

10.1007/s10586-021-03342-z ◽

2021 ◽

Author(s):

Ming-Jeng Yang

Keyword(s):

Data Center ◽

Energy Efficient ◽

Service Level Agreement ◽

Service Level ◽

Green Computing ◽

Cloud Data Center ◽

Cloud Data

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An enhanced security tree to secure cloud data

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.1.8925 ◽

2017 ◽

Vol 7 (1.1) ◽

pp. 64 ◽

Cited By ~ 1

Author(s):

S. Renu ◽

S.H. Krishna Veni

Keyword(s):

Data Storage ◽

Binary Tree ◽

Data Encryption ◽

Third Party ◽

Data Outsourcing ◽

Cloud Data ◽

Security Issues ◽

Security Services ◽

Computing Services ◽

User Data

The Cloud computing services and security issues are growing exponentially with time. All the CSPs provide utmost security but the issues still exist. Number of technologies and methods are emerged and futile day by day. In order to overcome this situation, we have also proposed a data storage security system using a binary tree approach. Entire services of the binary tree are provided by a Trusted Third Party (TTP) .TTP is a government or reputed organization which facilitates to protect user data from unauthorized access and disclosure. The security services are designed and implemented by the TTP and are executed at the user side. Data classification, Data Encryption and Data Storage are the three vital stages of the security services. An automated file classifier classify unorganized files into four different categories such as Sensitive, Private, Protected and Public. Applied cryptographic techniques are used for data encryption. File splitting and multiple cloud storage techniques are used for data outsourcing which reduces security risks considerably. This technique offers file protection even when the CSPs compromise.

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Energy Efficient, Resource-Aware, Prediction Based VM Provisioning Approach for Cloud Environment

International Journal of Ambient Computing and Intelligence ◽

10.4018/ijaci.2020070102 ◽

2020 ◽

Vol 11 (3) ◽

pp. 22-41

Author(s):

Akkrabani Bharani Pradeep Kumar ◽

P. Venkata Nageswara Rao

Keyword(s):

Energy Consumption ◽

Electrical Power ◽

Service Level Agreement ◽

Service Level ◽

Management Approach ◽

Cloud Data ◽

Proposed Model ◽

Key Aspects ◽

Cloud Infrastructures ◽

Resource Aware

Over the past few decades, computing environments have progressed from a single-user milieu to highly parallel supercomputing environments, network of workstations (NoWs) and distributed systems, to more recently popular systems like grids and clouds. Due to its great advantage of providing large computational capacity at low costs, cloud infrastructures can be employed as a very effective tool, but due to its dynamic nature and heterogeneity, cloud resources consuming enormous amount of electrical power and energy consumption control becomes a major issue in cloud datacenters. This article proposes a comprehensive prediction-based virtual machine management approach that aims to reduce energy consumption by reducing active physical servers in cloud data centers. The proposed model focuses on three key aspects of resource management namely, prediction-based delay provisioning; prediction-based migration, and resource-aware live migration. The comprehensive model minimizes energy consumption without violating the service level agreement and provides the required quality of service. The experiments to validate the efficacy of the proposed model are carried out on a simulated environment, with varying server and user applications and parameter sizes.

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Distributed Storage Strategy and Visual Analysis for Economic Big Data

Journal of Mathematics ◽

10.1155/2021/3224190 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Xiangli Chang ◽

Hailang Cui

Keyword(s):

Big Data ◽

Data Storage ◽

Visual Analysis ◽

Distributed Storage ◽

Storage System ◽

Service Level Agreement ◽

Wiener Filter ◽

Service Level ◽

Prototype System ◽

Data Types

With the increasing popularity of a large number of Internet-based services and a large number of services hosted on cloud platforms, a more powerful back-end storage system is needed to support these services. At present, it is very difficult or impossible to implement a distributed storage to meet all the above assumptions. Therefore, the focus of research is to limit different characteristics to design different distributed storage solutions to meet different usage scenarios. Economic big data should have the basic requirements of high storage efficiency and fast retrieval speed. The large number of small files and the diversity of file types make the storage and retrieval of economic big data face severe challenges. This paper is oriented to the application requirements of cross-modal analysis of economic big data. According to the source and characteristics of economic big data, the data types are analyzed and the database storage architecture and data storage structure of economic big data are designed. Taking into account the spatial, temporal, and semantic characteristics of economic big data, this paper proposes a unified coding method based on the spatiotemporal data multilevel division strategy combined with Geohash and Hilbert and spatiotemporal semantic constraints. A prototype system was constructed based on Mongo DB, and the performance of the multilevel partition algorithm proposed in this paper was verified by the prototype system based on the realization of data storage management functions. The Wiener distributed memory based on the principle of Wiener filter is used to store the workload of each workload distributed storage window in a distributed manner. For distributed storage workloads, this article adopts specific types of workloads. According to its periodicity, the workload is divided into distributed storage windows of specific duration. At the beginning of each distributed storage window, distributed storage is distributed to the next distributed storage window. Experiments and tests have verified the distributed storage strategy proposed in this article, which proves that the Wiener distributed storage solution can save platform resources and configuration costs while ensuring Service Level Agreement (SLA).

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DBaaS Multitenancy, Auto-tuning and SLA Maintenance in Cloud Environments: a Brief Survey

iSys - Brazilian Journal of Information Systems ◽

10.5753/isys.2018.362 ◽

2018 ◽

Vol 11 (2) ◽

pp. 30-42

Author(s):

Vinicius Da Silveira Segalin ◽

Carina Friedrich Dorneles ◽

Mario Antonio Ribeiro Dantas

Keyword(s):

Data Storage ◽

Service Providers ◽

Research Work ◽

Service Level Agreement ◽

Service Level ◽

Resources Management ◽

Database As A Service ◽

Cloud Environments ◽

And Performance ◽

The One

Cloud computing is a paradigm that presents many advantages to both costumers and service providers, such as low upfront investment, pay-per-use and easiness of use, delivering/enabling scalable services using Internet technologies. Among many types of services we have today, Database as a Service (DBaaS) is the one where a database is provided in the cloud in all its aspects. Examples of aspects related to DBaaS utilization are data storage, resources management and SLA maintenance. In this context, an important feature, related to it, is resource management and performance, which can be done in many different ways for several reasons, such as saving money, time, and meeting the requirements agreed between client and provider, that are defined in the Service Level Agreement (SLA). A SLA usually tries to protect the costumer from not receiving the contracted service and to ensure that the provider reaches the profit intended. In this paper it is presented a classification based on three main parameters that aim to manage resources for enhancing the performance on DBaaS and guarantee that the SLA is respected for both user and provider sides benefit. The proposal is based upon a survey of existing research work efforts.

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SLA and Performance Efficient Heuristics for Virtual Machines Placement in Cloud Data Centers

International Journal of Grid and High Performance Computing ◽

10.4018/ijghpc.2017070102 ◽

2017 ◽

Vol 9 (3) ◽

pp. 17-33 ◽

Cited By ~ 2

Author(s):

Oshin Sharma ◽

Hemraj Saini

Keyword(s):

Energy Consumption ◽

Data Centers ◽

Virtual Machines ◽

Service Level Agreement ◽

Service Level ◽

Consolidation Process ◽

Cloud Data ◽

Trade Offs ◽

And Performance ◽

High Level

Cloud computing has revolutionized the working models of IT industry and increasing the demand of cloud resources which further leads to increase in energy consumption of data centers. Virtual machines (VMs) are consolidated dynamically to reduce the number of host machines inside data centers by satisfying the customer's requirements and quality of services (QoS). Moreover, for using the services of cloud environment every cloud user has a service level agreement (SLA) that deals with energy and performance trade-offs. As, the excess of consolidation and migration may degrade the performance of system, therefore, this paper focuses the overall performance of the system instead of energy consumption during the consolidation process to maintain a trust level between cloud's users and providers. In addition, the paper proposed three different heuristics for virtual machine (VM) placement based on current and previous usage of resources. The proposed heuristics ensure a high level of service level agreements (SLA) and better performance of ESM metric in comparison to previous research.

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Dynamic Resource Allocation Method for Load Balance Scheduling Over Cloud Data Center Networks

Journal of Web Engineering ◽

10.13052/jwe1540-9589.2083 ◽

2021 ◽

Author(s):

Sakshi Chhabra ◽

Ashutosh Kumar Singh

Keyword(s):

Resource Allocation ◽

Load Balancing ◽

Response Time ◽

Resource Utilization ◽

Service Level Agreement ◽

Service Level ◽

Dynamic Resource Allocation ◽

Cloud Data Center ◽

Cloud Data ◽

Dynamic Resource

The cloud datacenter has numerous hosts as well as application requests where resources are dynamic. The demands placed on the resource allocation are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called Dynamic Resource Allocation for Load Balancing (DRALB) is proposed. The proposed solution constitutes two steps: First, the load manager analyzes the resource requirements such as CPU, Memory, Energy and Bandwidth usage and allocates an appropriate number of VMs for each application. Second, the resource information is collected and updated where resources are sorted into four queues according to the loads of resources i.e. CPU intensive, Memory intensive, Energy intensive and Bandwidth intensive. We demonstarate that SLA-aware scheduling not only facilitates the cloud consumers by resources availability and improves throughput, response time etc. but also maximizes the cloud profits with less resource utilization and SLA (Service Level Agreement) violation penalties. This method is based on diversity of client’s applications and searching the optimal resources for the particular deployment. Experiments were carried out based on following parameters i.e. average response time; resource utilization, SLA violation rate and load balancing. The experimental results demonstrate that this method can reduce the wastage of resources and reduces the traffic upto 44.89% and 58.49% in the network.

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Optimizing the Migration of Virtual Machines in Cloud Data Centers

International Journal of Embedded and Real-Time Communication Systems ◽

10.4018/ijertcs.289200 ◽

2022 ◽

Vol 13 (1) ◽

pp. 0-0

Keyword(s):

Data Centers ◽

Bin Packing ◽

Virtual Machines ◽

Service Level Agreement ◽

Service Level ◽

Virtual Machine Migration ◽

Cloud Data ◽

Time Simulation ◽

Cloud Data Centers ◽

Dynamic Resource

Dynamic resource allocation of cloud data centers is implemented with the use of virtual machine migration. Selected virtual machines (VM) should be migrated on appropriate destination servers. This is a critical step and should be performed according to several criteria. It is proposed to use the criteria of minimum resource wastage and service level agreement violation. The optimization problem of the VM placement according to two criteria is formulated, which is equivalent to the well-known main assignment problem in terms of the structure, necessary conditions, and the nature of variables. It is suggested to use the Hungarian method or to reduce the problem to a closed transport problem. This allows the exact solution to be obtained in real time. Simulation has shown that the proposed approach outperforms widely used bin-packing heuristics in both criteria.

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