scholarly journals Energy-Efficient Cloud Service Selection and Recommendation Based on QoS for Sustainable Smart Cities

2021 ◽  
Vol 11 (20) ◽  
pp. 9394
Author(s):  
Preeti Sirohi ◽  
Fahd N. Al-Wesabi ◽  
Haya Mesfer Alshahrani ◽  
Piyush Maheshwari ◽  
Amit Agarwal ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Recommendation System ◽  
Service Providers ◽  
Smart Cities ◽  
Service Selection ◽  
Cloud Service ◽  
Heat Emission ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Cloud Servers

The growing demand for cloud technology brings several cloud service providers and their diverse list of services in the market, putting a challenge for the user to select the best service from the inventory of available services. Therefore, a system that understands the user requirements and finds a suitable service according to user-customized requirements is a challenge. In this paper, we propose a new cloud service selection and recommendation system (CS-SR) for finding the optimal service by considering the user’s customized requirements. In addition, the service selection and recommendation system will consider both quantitative and qualitative quality of service (QoS) attributes in service selection. The comparison is made between proposed CS-SR with three existing approaches analytical hierarchy process (A.H.P.), efficient non-dominated sorting-sequential search (ENS-SS), and best-worst method (B.W.M.) shows that CR-SR outperforms the above approaches in two ways (i) reduce the total execution time and (ii) energy consumption to find the best service for the user. The proposed cloud service selection mechanism facilitates reduced energy consumption at cloud servers, thereby reducing the overall heat emission from a cloud data center.

scholarly journals SECURE DEPENDABLE SELECTIVE STORAGE SERVICES AND SUPPORT FOR DYNAMIC DATA OPERATIONS IN CLOUD COMPUTING

2013 ◽  
pp. 35-40
Author(s):  
VINITHA S P ◽  
GURUPRASAD E
Keyword(s):  
Cloud Computing ◽  
Service Providers ◽  
Distributed Storage ◽  
Large Data ◽  
Cloud Service ◽  
Dynamic Data ◽  
Cloud Data ◽  
Data Auditing ◽  
Cloud Servers ◽  
Selective Storage

Cloud computing has been envisioned as the next generation architecture of IT enterprise. It moves the application software and databases to the centralized large data centers where management of data and services may not be fully trustworthy. This unique paradigm brings out many new security challenges like, maintaining correctness and integrity of data in cloud. Integrity of cloud data may be lost due to unauthorized access, modification or deletion of data. Lacking of availability of data may be due to the cloud service providers (CSP), in order to increase their margin of profit by reducing the cost, CSP may discard rarely accessed data without detecting in timely fashion. To overcome above issues, flexible distributed storage, token utilizing, signature creations used to ensure integrity of data, auditing mechanism used assists in maintaining the correctness of data and also locating, identifying of server where exactly the data has been corrupted and also dependability and availability of data achieved through distributed storage of data in cloud. Further in order to ensure authorized access to cloud data a admin module has been proposed in our previous conference paper, which prevents unauthorized users from accessing data and also selective storage scheme based on different parameters of cloud servers proposed in previous paper, in order to provide efficient storage of data in the cloud. In order to provide more efficiency in this paper dynamic data operations are supported such as updating, deletion and addition of data.

scholarly journals Service Association Factor (SAF) for Cloud Service Selection and Recommendation

2020 ◽  
Vol 49 (1) ◽  
pp. 113-126
Author(s):  
Imran Mujaddid Rabbani ◽  
Muhammad Aslam ◽  
Ana Maria Martinez Enriquez ◽  
Zeeshan Qudeer
Keyword(s):  
Recommendation System ◽  
Service Providers ◽  
Service Selection ◽  
Cloud Service ◽  
Base System ◽  
End User ◽  
Cloud Models ◽  
Science Domain ◽  
Quality Of Service Parameters

Cloud computing is one of the leading technology in IT and computer science domain. Business IT infrastructures are equipping themselves with modern regime of clouds. In the presence of several opportunities, selection criteria decision becomes vital when there is no supporting information available. Global clouds also need evaluation and assessment from its users that what they think about and how new ones could make their selection as per their needs. Recommended systems were built to propose best services using customer's feedback, applying quality of service parameters, assigning scores, trust worthiness and clustering in different forms and models. These techniques did not record and use interrelationships between the services that is true impact of service utilization. In the proposed approach, service association factor calculates value of interrelations among services used by the end user. An intelligent leaning based recommendation system is developed for assisting users to select services on their respective preferences. This technique is evaluated on leading service providers and results show that learning base system performs well on all types of cloud models.

A Novel Multi-Dimension Resource Recycling Mechanism for Cloud Data Centers

2015 ◽  
Vol 7 (2) ◽  
pp. 50-64 ◽  
Author(s):  
Hong-Yi Chang ◽  
Tu-Liang Lin ◽  
Cheng-Kai Huang
Keyword(s):  
Resource Allocation ◽  
Cloud Service ◽  
Allocation Mechanism ◽  
Cloud Data Center ◽  
Resource Allocation Algorithm ◽  
Cloud Data ◽  
Resource Recycling ◽  
Efficient Resource ◽  
Cloud Data Centers ◽  
Cloud Servers

Cloud servers can be started with ineffective resource arrangement, and extra costs are produced if unnecessary servers are started. This is a substantial fee for the cloud service provider. Therefore, each cloud data center needs an efficient resource allocation mechanism to prevent unnecessary cloud servers from being started. In general, resource allocation problems can be classified into either one-dimension or multi-dimension aspects. In 2013, the authors have proposed a multi-dimension resource allocation algorithm that can improve the utilization of cloud servers by as much as 97%. However, most of previous studies are more concerned with solving the resource allocation problem. In fact, after a period of time, the applications will finish their jobs, and resources been occupied on the servers will be released, thus decreasing the utilization of cloud servers. Therefore, this paper proposes a novel multi-dimension resource recycling algorithm to optimize the server resource again, to recycle the excess cloud resources and to reduce the unnecessary operating cloud servers.

scholarly journals Virtual Machine Customization Using Resource Using Prediction for Efficient Utilization of Resources in IaaS Public Clouds

2021 ◽  
Vol 6 (2) ◽  
pp. 170-182
Author(s):  
Derdus Kenga ◽  
Vincent Omwenga ◽  
Patrick Ogao
Keyword(s):  
Energy Consumption ◽  
Resource Utilization ◽  
Virtual Machines ◽  
Service Providers ◽  
Cloud Service ◽  
Cloud Data ◽  
Efficient Resource ◽  
Server Utilization ◽  
Auto Scaling ◽  
Cloud Users

The main cause of energy wastage in cloud data centres is the low level of server utilization. Low server utilization is a consequence of allocating more resources than required for running applications. For instance, in Infrastructure as a Service (IaaS) public clouds, cloud service providers (CSPs) deliver computing resources in the form of virtual machines (VMs) templates, which the cloud users have to choose from. More often, inexperienced cloud users tend to choose bigger VMs than their application requirements. To address the problem of inefficient resources utilization, the existing approaches focus on VM allocation and migration, which only leads to physical machine (PM) level optimization. Other approaches use horizontal auto-scaling, which is not a visible solution in the case of IaaS public cloud. In this paper, we propose an approach of customizing user VM’s size to match the resources requirements of their application workloads based on an analysis of real backend traces collected from a VM in a production data centre. In this approach, a VM is given fixed size resources that match applications workload demands and any demand that exceeds the fixed resource allocation is predicted and handled through vertical VM auto-scaling. In this approach, energy consumption by PMs is reduced through efficient resource utilization. Experimental results obtained from a simulation on CloudSim Plus using GWA-T-13 Materna real backend traces shows that data center energy consumption can be reduced via efficient resource utilization

scholarly journals Secured Service Chains Tuned Resource Sharing using Nsdp

2019 ◽  
Vol 8 (10) ◽  
pp. 2195-2198
Keyword(s):  
Resource Sharing ◽  
Keyword Search ◽  
Service Providers ◽  
Cloud Service ◽  
Cloud Data ◽  
Security Breaches ◽  
Private Data ◽  
Cloud Servers ◽  
Compound Concepts ◽  
E Mail

In the current network area, cloud service providers offer infinite storage space and computing power for users to manage their data in the cloud. To enjoy these services, individuals or organizations store their private data on cloud servers. However, in the case of security breaches, users’ private data stored in the cloud are no longer safer. When users outsource their data to cloud servers, they expect complete privacy of their data stored in the cloud storage. To enjoy these services, individuals or organizations store their private data on cloud servers. The semantic-based keyword search over encrypted cloud data becomes of paramount importance. Protecting the privacy and data of users has remained a very crucial problem for cloud servers Additionally, the existing approaches only process them as single words, the flexibility of the encryption policy and the description of users’ rights, and it changes from a one-one to one-many scenario during the encryption and decryption phases, calculation method to measure the semantic similarity between compound concepts. Keyword has been widely used in many scenarios, particularly in cloud computing. In this Project in the proposed scheme we use the trusted authority to generate the trapdoor .The generated trapdoor will be send to the user's e-mail ID, the user will search

scholarly journals Energy-Aware Virtual Machine Clustering for Consolidation in Multi-tenant IaaS Public Clouds

2019 ◽  
pp. 1123-1136
Author(s):  
Kenga Mosoti Derdus ◽  
Vincent Oteke Omwenga ◽  
Patrick Job Ogao
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Service Providers ◽  
Cloud Service ◽  
Resource Usage ◽  
Energy Aware ◽  
Allocation Policy ◽  
Cloud Data ◽  
Vm Allocation ◽  
Vm Consolidation

Cloud computing has gained a lot of interest from both small and big academic and commercial organizations because of its success in delivering service on a pay-as-you-go basis. Moreover, many users (organizations) can share server computing resources, which is made possible by virtualization. However, the amount of energy consumed by cloud data centres is a major concern. One of the major causes of energy wastage is the inefficient utilization of resources. For instance, in IaaS public clouds, users select Virtual Machine (VM) sizes set beforehand by the Cloud Service Providers (CSPs) without the knowledge of the kind of workloads to be executed in the VM. More often, the users overprovision the resources, which go to waste. Additionally, the CSPs do not have control over the types of applications that are executed and thus VM consolidation is performed blindly. There have been efforts to address the problem of energy consumption by efficient resource utilization through VM allocation and migration. However, these techniques lack collection and analysis of active real cloud traces from the IaaS cloud. This paper proposes an architecture for VM consolidation through VM profiling and analysis of VM resource usage and resource usage patterns, and a VM allocation policy. We have implemented our policy on CloudSim Plus cloud simulator and results show that it outperforms Worst Fit, Best Fit and First Fit VM allocation algorithms. Energy consumption is reduced through efficient consolidation that is informed by VM resource consumption.

scholarly journals Energy-aware and Carbon-efficient VM Placement Optimization in Cloud Datacenters Using Evolutionary Computing Methods

2022 ◽  
Author(s):  
Tahereh Abbasi-khazaei ◽  
Mohammad Hossein Rezvani
Keyword(s):  
Energy Consumption ◽  
Service Providers ◽  
Cloud Service ◽  
Computing Methods ◽  
Energy Costs ◽  
Vm Placement ◽  
Energy Aware ◽  
Carbon Footprints ◽  
Cloud Data ◽  
Cloud Data Centers

Abstract One of the most important concerns of cloud service providers is balancing renewable and fossil energy consumption. On the other hand, the policy of organizations and governments is to reduce energy consumption and greenhouse gas emissions in cloud data centers. Recently, a lot of research has been conducted to optimize the Virtual Machine (VM) placement on physical machines to minimize energy consumption. Many previous studies have not considered the deadline and scheduling of IoT tasks. Therefore, the previous modelings are mainly not well-suited to the IoT environments where requests are time-constraint. Unfortunately, both the sub-problems of energy consumption minimization and scheduling fall into the category of NP-hard issues. In this study, we propose a multi-objective VM placement to joint minimizing energy costs and scheduling. After presenting a modified memetic algorithm, we compare its performance with baseline methods as well as state-of-the-art ones. The simulation results on the CloudSim platform show that the proposed method can reduce energy costs, carbon footprints, SLA violations, and the total response time of IoT requests.

scholarly journals Adaptive Secure Cross-Cloud Data Collaboration with Identity-Based Cryptography and Conditional Proxy Re-Encryption

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Qinlong Huang ◽  
Yue He ◽  
Wei Yue ◽  
Yixian Yang
Keyword(s):  
Service Providers ◽  
Cloud Service ◽  
Security And Privacy ◽  
Broadcast Encryption ◽  
Cloud Data ◽  
Identity Based ◽  
Identity Based Cryptography ◽  
And Performance ◽  
Cryptographic Techniques ◽  
Data Collaboration

Data collaboration in cloud computing is more and more popular nowadays, and proxy deployment schemes are employed to realize cross-cloud data collaboration. However, data security and privacy are the most serious issues that would raise great concerns from users when they adopt cloud systems to handle data collaboration. Different cryptographic techniques are deployed in different cloud service providers, which makes cross-cloud data collaboration to be a deeper challenge. In this paper, we propose an adaptive secure cross-cloud data collaboration scheme with identity-based cryptography (IBC) and proxy re-encryption (PRE) techniques. We first present a secure cross-cloud data collaboration framework, which protects data confidentiality with IBC technique and transfers the collaborated data in an encrypted form by deploying a proxy close to the clouds. We then provide an adaptive conditional PRE protocol with the designed full identity-based broadcast conditional PRE algorithm, which can achieve flexible and conditional data re-encryption among ciphertexts encrypted in identity-based encryption manner and ciphertexts encrypted in identity-based broadcast encryption manner. The extensive analysis and experimental evaluations demonstrate the well security and performance of our scheme, which meets the secure data collaboration requirements in cross-cloud scenarios.

scholarly journals A Switch Based Resource Management Method for Energy Optimization in Cloud Data Center

2021 ◽  
pp. 85-91
Author(s):  
Shally Vats ◽  
Sanjay Kumar Sharma ◽  
Sunil Kumar
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Energy Requirement ◽  
Energy Optimization ◽  
Cloud Service ◽  
Optimization Method ◽  
Level Energy ◽  
Cloud Data Center ◽  
Vm Migration ◽  
Cloud Data

Proliferation of large number of cloud users steered the exponential increase in number and size of the data centers. These data centers are energy hungry and put burden for cloud service provider in terms of electricity bills. There is environmental concern too, due to large carbon foot print. A lot of work has been done on reducing the energy requirement of data centers using optimal use of CPUs. Virtualization has been used as the core technology for optimal use of computing resources using VM migration. However, networking devices also contribute significantly to the responsible for the energy dissipation. We have proposed a two level energy optimization method for the data center to reduce energy consumption by keeping SLA. VM migration has been performed for optimal use of physical machines as well as switches used to connect physical machines in data center. Results of experiments conducted in CloudSim on PlanetLab data confirm superiority of the proposed method over existing methods using only single level optimization.

Quantum Key Distribution Approach for Secure Authentication of Cloud Servers

2021 ◽  
Vol 11 (3) ◽  
pp. 19-32
Author(s):  
Shahin Fatima ◽  
Shish Ahmad
Keyword(s):  
Cloud Computing ◽  
Quantum Key Distribution ◽  
Feasible Solution ◽  
Service Providers ◽  
Cloud Service ◽  
Key Distribution ◽  
Distribution Center ◽  
Proposed Model ◽  
Cloud Servers ◽  
Secure Authentication

Cloud computing has become a feasible solution for virtualization of cloud resources. Although it has many prospective to hold individuals by providing many benefits to organizations, still there are security loopholes to outsource data. To ensure the ‘security' of data in cloud computing, quantum key cryptography is introduced. Quantum cryptography makes use of quantum mechanics and qubits. The proposed method made use of quantum key distribution with Kerberos to secure the data on the cloud. The paper discussed the model for quantum key distribution which makes use of Kerberos ticket distribution center for authentication of cloud service providers. The proposed model is compared with quantum key distribution and provides faster computation by producing less error rate.

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