On the Performance Evaluation of IaaS Cloud Services With System-Level Benchmarks

2018 ◽  
Vol 8 (1) ◽  
pp. 80-96 ◽  
Author(s):  
Sanjay P. Ahuja ◽  
Niharika Deval
Keyword(s):  
Performance Evaluation ◽  
System Parameter ◽  
Cloud Service ◽  
Cloud Services ◽  
System Level ◽  
Service Model ◽  
Network Utilization ◽  
And Storage ◽  
Cloud Users ◽  
Selection Of

Infrastructure-as-a-service is a cloud service model that allows customers to outsource computing resources such as servers and storage. This article evaluates four IaaS cloud services - Amazon EC2, Microsoft Azure, Google Compute Engine and Rackspace Cloud in a vendor-neutral approach with regards to system parameter usage including server, file I/O and network utilization. Thus, system-level benchmarking provides objective comparison of cloud providers from performance standpoint. Unixbench, Dbench and Iperf are the System-level benchmarks chosen to test the performance of server, file I/O and network respectively. In order to capture the variation in performance, the tests were performed at different times on weekdays and weekends. With each offering, the benchmarks are tested on different configurations to provide an insight to the cloud users in selection of provider followed by appropriate VM sizing according to the workload requirement. In addition to the performance evaluation, price-per-performance value of all the providers is also examined and compared.

A Credible Cloud Service Model based on Behavior Graphs and Tripartite Decision-Making Mechanism

2016 ◽  
Vol 8 (3) ◽  
pp. 38-56 ◽  
Author(s):  
Junfeng Tian ◽  
He Zhang
Keyword(s):  
Decision Making ◽  
Cloud Service ◽  
Cloud Services ◽  
Service Model ◽  
The Past ◽  
Model Based ◽  
Cloud Users ◽  
Cloud User

The credibility of cloud service is the key to the success of the application of cloud services. The dual servers of master server and backup server are applied to cloud services, which can improve the availability of cloud services. In the past, the failures between master server and backup server could be detected by heartbeat algorithm. Because of lacking cloud user's evaluation, the authors put forward a credible cloud service model based on behavior Graphs and tripartite decision-making mechanism. By the quantitative of cloud users' behaviors evidences, the construction of behavior Graphs and the judgment of behavior, they select the most credible cloud user. They combine the master server, the backup server and the selected credible cloud user to determine the credibility of cloud service by the tripartite decision-making mechanism. Finally, according to the result of credible judgment, the authors could decide whether it will be switched from the master server to the backup server.

A Credible Cloud Service Model Based on Behavior Graphs and Tripartite Decision-Making Mechanism

2019 ◽  
pp. 903-922
Author(s):  
Junfeng Tian ◽  
He Zhang
Keyword(s):  
Decision Making ◽  
Cloud Service ◽  
Cloud Services ◽  
Service Model ◽  
The Past ◽  
Model Based ◽  
Cloud Users ◽  
Cloud User

The credibility of cloud service is the key to the success of the application of cloud services. The dual servers of master server and backup server are applied to cloud services, which can improve the availability of cloud services. In the past, the failures between master server and backup server could be detected by heartbeat algorithm. Because of lacking cloud user's evaluation, the authors put forward a credible cloud service model based on behavior Graphs and tripartite decision-making mechanism. By the quantitative of cloud users' behaviors evidences, the construction of behavior Graphs and the judgment of behavior, they select the most credible cloud user. They combine the master server, the backup server and the selected credible cloud user to determine the credibility of cloud service by the tripartite decision-making mechanism. Finally, according to the result of credible judgment, the authors could decide whether it will be switched from the master server to the backup server.

Slicing, Tokenization, and Encryption Based Combinational Approach to Protect Data-at-Rest in Cloud Using TF-Sec Model

2020 ◽  
Vol 17 (12) ◽  
pp. 5296-5306
Author(s):  
N. Keerthana ◽  
Viji Vinod ◽  
Sudhakar Sengan
Keyword(s):  
Cloud Service ◽  
Service Level ◽  
Cloud Services ◽  
Erasure Coding ◽  
Cloud Data ◽  
Data Usage ◽  
Dispersion Technique ◽  
Definition Of ◽  
The Given ◽  
Cloud Users

Data in the Cloud, which applies to data as a cloud service provider (CSP), transmits stores, or manages it. The company will enforce the same definition of data usage while the data is resident within the enterprise and thus extend the required cryptographic security criteria to data collected, exchanged, or handled by CSP. The CSP Service Level Agreements cannot override the cryptographic access measures. When the data is transferred securely to CSP, it can be securely collected, distributed, and interpreted. Data at the rest position applies to data as it is processed internally in organized and in the unstructured ways like databases and file cabinets. The Data at the Rest example includes the use of cryptography for preserving the integrity of valuable data when processed. For cloud services, computing takes multiple forms from recording units, repositories, and many unstructured items. This paper presents a secure model for Data at rest. The TF-Sec model suggested is planned for use with Slicing, Tokenization, and Encryption. The model encrypts the given cloud data using AES 256 encryption, and then the encrypted block is sliced into the chunks of data fragments using HD-Slicer. Then it applies tokenization algorithm TKNZ to each chunk of data, applies erasure coding technique to tokens, applies the data dispersion technique to scramble encrypted data fragments, and allocates to storage nodes of the multiple CSP. In taking the above steps, this study aims to resolve the cloud security problems found and to guarantee the confidentiality of their data to cloud users due to encryption of data fragments would be of little benefit to a CSP.

System Benchmarking on Public Clouds

2015 ◽  
pp. 37-50
Author(s):  
Sanjay P. Ahuja ◽  
Thomas F. Furman ◽  
Kerwin E. Roslie ◽  
Jared T. Wheeler
Keyword(s):  
Service Providers ◽  
Cluster Formation ◽  
Cloud Service ◽  
Vital Role ◽  
Cloud Services ◽  
System Level ◽  
Public Cloud ◽  
Cloud Providers ◽  
Set Up ◽  
Levels Of Service

There are several public cloud providers that provide service across different cloud models such as IaaS, PaaS, and SaaS. End users require an objective means to assess the performance of the services being offered by the various cloud providers. Benchmarks have typically been used to evaluate the performance of various systems and can play a vital role in assessing performance of the different public cloud platforms in a vendor neutral manner. Amazon's EC2 Service is one of the leading public cloud service providers and offers many different levels of service. The research in this chapter focuses on system level benchmarks and looks into evaluating the memory, CPU, and I/O performance of two different tiers of hardware offered through Amazon's EC2. Using three distinct types of system benchmarks, the performance of the micro spot instance and the M1 small instance are measured and compared. In order to examine the performance and scalability of the hardware, the virtual machines are set up in a cluster formation ranging from two to eight nodes. The results show that the scalability of the cloud is achieved by increasing resources when applicable. This chapter also looks at the economic model and other cloud services offered by Amazon's EC2, Microsoft's Azure, and Google's App Engine.

Dynamic QoS-Aware Cloud Service Selection Using Best-Worst Method and Timeslot Weighted Satisfaction Scores

2020 ◽  
Author(s):  
Falak Nawaz ◽  
Naeem Khalid Janjua
Keyword(s):  
Decision Making ◽  
Service Selection ◽  
Cloud Service ◽  
Cloud Services ◽  
Satisfaction Score ◽  
Analytic Hierarchy ◽  
Hierarchy Process ◽  
Best Worst Method ◽  
Selection Of

Abstract The number of cloud services has dramatically increased over the past few years. Consequently, finding a service with the most suitable quality of service (QoS) criteria matching the user’s requirements is becoming a challenging task. Although various decision-making methods have been proposed to help users to find their required cloud services, some uncertainties such as dynamic QoS variations hamper the users from employing such methods. Additionally, the current approaches use either static or average QoS values for cloud service selection and do not consider dynamic QoS variations. In this paper, we overcome this drawback by developing a broker-based approach for cloud service selection. In this approach, we use recently monitored QoS values to find a timeslot weighted satisfaction score that represents how well a service satisfies the user’s QoS requirements. The timeslot weighted satisfaction score is then used in Best-Worst Method, which is a multi-criteria decision-making method, to rank the available cloud services. The proposed approach is validated using Amazon’s Elastic Compute Cloud (EC2) cloud services performance data. The results show that the proposed approach leads to the selection of more suitable cloud services and is also efficient in terms of performance compared to the existing analytic hierarchy process-based cloud service selection approaches.

Research on Behavior Trust Evaluation Method of Cloud Services Based on Membership Theory

2013 ◽  
Vol 427-429 ◽  
pp. 2377-2382
Author(s):  
Ying Liu ◽  
Yan Wang ◽  
Xian You Sun
Keyword(s):  
Evaluation System ◽  
Evaluation Method ◽  
Service Providers ◽  
Evaluation Model ◽  
Degree Theory ◽  
Cloud Service ◽  
Cloud Services ◽  
Trust Evaluation ◽  
Wide Range ◽  
Cloud Users

Among the wide range of cloud service providers with different performance characteristics, in order to let the cloud users find cloud services which satisfy its performance preferences and specific trust levels,it needs to establish a reasonable and scientific cloud service trust evaluation system. This paper introduces a membership degree theory into trust evaluation model. First, it designs the trust evaluation system framework of cloud services, and establishes a trust evaluation model of cloud services. Next, it calculates the trust level of cloud services with the comprehensive trust cloud center of gravity evaluation method (CCGE). Finally, the experiment results show that this model can build precise trust relationship between cloud users and cloud services based on users performance demands.

Empirical Performance Assessment of Public Clouds Using System Level Benchmarks

2013 ◽  
Vol 3 (4) ◽  
pp. 81-91 ◽  
Author(s):  
Sanjay P. Ahuja ◽  
Thomas F. Furman ◽  
Kerwin E. Roslie ◽  
Jared T. Wheeler
Keyword(s):  
Virtual Machines ◽  
Service Providers ◽  
Cluster Formation ◽  
Cloud Service ◽  
Cloud Services ◽  
System Level ◽  
Processing Unit ◽  
Central Processing ◽  
Empirical Performance ◽  
Set Up

Amazon's Elastic Compute Cloud (EC2) Service is one of the leading public cloud service providers and offers many different levels of service. This paper looks into evaluating the memory, central processing unit (CPU), and input/output I/O performance of two different tiers of hardware offered through Amazon's EC2. Using three distinct types of system benchmarks, the performance of the micro spot instance and the M1 small instance are measured and compared. In order to examine the performance and scalability of the hardware, the virtual machines are set up in a cluster formation ranging from two to eight nodes. The results show that the scalability of the cloud is achieved by increasing resources when applicable. This paper also looks at the economic model and other cloud services offered by Amazon's EC2, Microsoft's Azure, and Google's App Engine.

Digital Library Knowledge Service Model Research Based on Mobile Cloud Computing

2014 ◽  
Vol 631-632 ◽  
pp. 200-203
Author(s):  
Min Yao
Keyword(s):  
Digital Library ◽  
Mobile Cloud Computing ◽  
Cloud Service ◽  
Cloud Services ◽  
Mobile Cloud ◽  
Service Model ◽  
Knowledge Processing ◽  
Model Research ◽  
Technology Services ◽  
Knowledge Services

Digital library cloud service is the development and extension of traditional digital library knowledge services under the environment of mobile cloud. Meanwhile, it’s also the new stage of knowledge services development. Based on the analysis of mobile cloud technology services model and its advantages, taking “end”, ”tube”, ”cloud” as the main line, this paper build up digital library mobile cloud knowledge services model from three aspects: knowledge collecting, knowledge processing and knowledge using. Thus, it point out the opportunities and challenges faced by mobile cloud services and present the strategies of implementing knowledge cloud services.

Cloud Services Publication and Discovery

2017 ◽  
pp. 204-228 ◽  
Author(s):  
Yasmine M. Afify ◽  
Ibrahim F. Moawad ◽  
Nagwa. L. Badr ◽  
Mohamed F. Tolba
Keyword(s):  
Research Work ◽  
Cloud Service ◽  
Cloud Services ◽  
Software As A Service ◽  
The Internet ◽  
Registration System ◽  
Work Related ◽  
Service Publication ◽  
Art Research ◽  
Cloud Users

Cloud computing is an information technology delivery model accessed over the Internet. Its adoption rate is dramatically increasing. Diverse cloud service advertisements introduce more challenges to cloud users to locate and identify required service offers. These challenges highlight the need for a consistent cloud service registry to serve as a mediator between cloud providers and users. In this chapter, state-of-the-art research work related to cloud service publication and discovery is surveyed. Based on the survey findings, a set of key limitations are emphasized. Discussion of challenges and future requirements is presented. In order to contribute to cloud services publication and discovery area, a semantic-based system for unified Software-as-a-Service (SaaS) service advertisements is proposed. Its back-end foundation is the focus on business-oriented perspective of the SaaS services and semantics. Service registration template, guided registration model, and registration system are introduced. Additionally, a semantic similarity model for services metadata matchmaking is presented.

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