scholarly journals An Empirical Performance Evaluation of Multiple Intel Optane Solid-State Drives

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1325
Author(s):  
Jaehyun Han ◽  
Guangyu Zhu ◽  
Sangmook Lee ◽  
Yongseok Son
Keyword(s):  
Cloud Computing ◽  
Solid State ◽  
Performance Metrics ◽  
Empirical Evaluation ◽  
Evaluation Study ◽  
Cloud Services ◽  
Experimental Results ◽  
Solid State Drives ◽  
Non Volatile Memory ◽  
And Performance

Cloud computing as a service-on-demand architecture has grown in importance over the last few years. The storage subsystem in cloud computing has undergone enormous innovation to provide high-quality cloud services. Emerging Non-Volatile Memory Express (NVMe) technology has attracted considerable attention in cloud computing by delivering high I/O performance in latency and bandwidth. Specifically, multiple NVMe solid-state drives (SSDs) can provide higher performance, fault tolerance, and storage capacity in the cloud computing environment. In this paper, we performed an empirical evaluation study of performance on recent NVMe SSDs (i.e., Intel Optane SSDs) with different redundant array of independent disks (RAID) environments. We analyzed multiple NVMe SSDs with RAID in terms of different performance metrics via synthesis and database benchmarks. We anticipate that our experimental results and performance analysis will have implications for various storage systems. Experimental results showed that the software stack overhead reduced the performance by up to 75%, 52%, 76%, 91%, and 92% in RAID 0, 1, 10, 5, and 6, respectively, compared with theoretical and expected performance.

scholarly journals A Fast Optimization Method for Reliability and Performance of Cloud Services Composition Application

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Zhao Wu ◽  
Naixue Xiong ◽  
Yannong Huang ◽  
Qiong Gu ◽  
Chunyang Hu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
State Space ◽  
Generating Function ◽  
Performance Optimization ◽  
Optimization Method ◽  
Cloud Services ◽  
Services Composition ◽  
Fast Optimization ◽  
And Performance ◽  
Universal Generating Function

At present the cloud computing is one of the newest trends of distributed computation, which is propelling another important revolution of software industry. The cloud services composition is one of the key techniques in software development. The optimization for reliability and performance of cloud services composition application, which is a typical stochastic optimization problem, is confronted with severe challenges due to its randomness and long transaction, as well as the characteristics of the cloud computing resources such as openness and dynamic. The traditional reliability and performance optimization techniques, for example, Markov model and state space analysis and so forth, have some defects such as being too time consuming and easy to cause state space explosion and unsatisfied the assumptions of component execution independence. To overcome these defects, we propose a fast optimization method for reliability and performance of cloud services composition application based on universal generating function and genetic algorithm in this paper. At first, a reliability and performance model for cloud service composition application based on the multiple state system theory is presented. Then the reliability and performance definition based on universal generating function is proposed. Based on this, a fast reliability and performance optimization algorithm is presented. In the end, the illustrative examples are given.

Co-Optimizing Storage Space Utilization and Performance for Key-Value Solid State Drives

2019 ◽  
Vol 38 (1) ◽  
pp. 29-42 ◽  
Author(s):  
Yen-Ting Chen ◽  
Ming-Chang Yang ◽  
Yuan-Hao Chang ◽  
Tseng-Yi Chen ◽  
Hsin-Wen Wei ◽  
...  
Keyword(s):  
Solid State ◽  
Storage Space ◽  
Solid State Drives ◽  
Space Utilization ◽  
And Performance

scholarly journals Blockchain-enabled real-time SLA monitoring for cloud-hosted services

2021 ◽  
Author(s):  
Kashif Mehboob Khan ◽  
Junaid Arshad ◽  
Waheed Iqbal ◽  
Sidrah Abdullah ◽  
Hassan Zaib
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Service Providers ◽  
Response Times ◽  
Cloud Service ◽  
Service Level ◽  
Cloud Services ◽  
Blockchain Technology ◽  
Manual Processing ◽  
And Performance

AbstractCloud computing is an important technology for businesses and individual users to obtain computing resources over the Internet on-demand and flexibly. Although cloud computing has been adopted across diverse applications, the owners of time-and-performance critical applications require cloud service providers’ guarantees about their services, such as availability and response times. Service Level Agreements (SLAs) are a mechanism to communicate and enforce such guarantees typically represented as service level objectives (SLOs), and financial penalties are imposed on SLO violations. Due to delays and inaccuracies caused by manual processing, an automatic method to periodically verify SLA terms in a transparent and trustworthy manner is fundamental to effective SLA monitoring, leading to the acceptance and credibility of such service to the customers of cloud services. This paper presents a blockchain-based distributed infrastructure that leverages fundamental blockchain properties to achieve immutable and trustworthy SLA monitoring within cloud services. The paper carries out an in-depth empirical investigation for the scalability of the proposed system in order to address the challenge of transparently enforcing real-time monitoring of cloud-hosted services leveraging blockchain technology. This will enable all the stakeholders to enforce accurate execution of SLA without any imprecisions and delays by maintaining an immutable ledger publicly across blockchain network. The experimentation takes into consideration several attributes of blockchain which are critical in achieving optimum performance. The paper also investigates key characteristics of these factors and their impact to the behaviour of the system for further scaling it up under various cases for increased service utilization.

Rationale for Use of Cloud Computing

2015 ◽  
pp. 2166-2197
Author(s):  
Amir Zeid ◽  
Ahmed Shawish ◽  
Maria Salama
Keyword(s):  
Cloud Computing ◽  
Cost Benefit Analysis ◽  
Service Selection ◽  
Cost Benefit ◽  
Quality Parameters ◽  
Cloud Services ◽  
Computing Paradigm ◽  
Model Combining ◽  
Quality Aspects ◽  
And Performance

Cloud Computing is the most promising computing paradigm that provides flexible resource allocation on demand with the promise of realizing elastic, Internet-accessible, computing on a pay-as-you-go basis. With the growth and expansion of the Cloud services and participation of various services providers, the description of quality parameters and measurement units start to diversify and sometime contradict. Such ambiguity does not only result in the rise of various Quality of Service (QoS) interoperability problems but also in the distraction of the services consumers who find themselves unable to match quality requirements with the providers' offerings. Yet, employing the available QoS models that cover certain quality aspects while neglecting others drive consumers to perform their service selection based only on cost-benefit analysis and performance evaluation, without being able to perform subjective selection based on a comprehensive set of well-defined quality aspects. This chapter presents a novel QoS ontology that combines and defines all of the existing quality aspects in a unified way to efficiently overcome all existing diversities. Using such an ontology, a comprehensive broad QoS model combining all quality-related parameters of both service providers and consumers for different Cloud platforms is presented. The chapter also provides a mathematical model that formulates the Cloud Computing service provider selection optimization problem based on QoS guarantees. The validation of the provided model is addressed in the chapter through extensive simulation studies conducted on benchmark data of Content Delivery Network providers. The studies report the efficient matching of the model with the market-oriented different platform characteristics.

scholarly journals A Preference-Based Supervising of Virtual Machines in Cloud Environment

2018 ◽  
Vol 7 (2.24) ◽  
pp. 81
Author(s):  
B Ezhilarasi ◽  
P Padmakumari ◽  
A Umamakeswari
Keyword(s):  
Cloud Computing ◽  
Customer Satisfaction ◽  
Virtual Machines ◽  
Cloud Services ◽  
Experimental Results ◽  
Communication Overhead ◽  
Cloud Environment ◽  
Resource Monitoring ◽  
The Status

Cloud Computing is a well-known technology in today’s world. A large number of users are benefited from the cloud services. The cloud computing must provide efficient service on time for customer satisfaction. So, prominent resource monitoring and scheduling techniques are needed. To achieve the customer satisfaction and to reduce the communication overhead, a method called Resource Supervisor (RS) is proposed. The proposed algorithm assigns the preference for the tasks having highest length, monitor the status of the resource and schedule the tasks to various resources quickly. The proposed method is implemented using Cloud Simulator, and experimental results are validated by comparing RS method with existing algorithms, which provides better outcomes and reduces the communication overhead. 

Rationale for Use of Cloud Computing

2014 ◽  
pp. 24-56
Author(s):  
Amir Zeid ◽  
Ahmed Shawish ◽  
Maria Salama
Keyword(s):  
Cloud Computing ◽  
Cost Benefit Analysis ◽  
Service Selection ◽  
Cost Benefit ◽  
Quality Parameters ◽  
Cloud Services ◽  
Computing Paradigm ◽  
Model Combining ◽  
Quality Aspects ◽  
And Performance

Cloud Computing is the most promising computing paradigm that provides flexible resource allocation on demand with the promise of realizing elastic, Internet-accessible, computing on a pay-as-you-go basis. With the growth and expansion of the Cloud services and participation of various services providers, the description of quality parameters and measurement units start to diversify and sometime contradict. Such ambiguity does not only result in the rise of various Quality of Service (QoS) interoperability problems but also in the distraction of the services consumers who find themselves unable to match quality requirements with the providers’ offerings. Yet, employing the available QoS models that cover certain quality aspects while neglecting others drive consumers to perform their service selection based only on cost-benefit analysis and performance evaluation, without being able to perform subjective selection based on a comprehensive set of well-defined quality aspects. This chapter presents a novel QoS ontology that combines and defines all of the existing quality aspects in a unified way to efficiently overcome all existing diversities. Using such an ontology, a comprehensive broad QoS model combining all quality-related parameters of both service providers and consumers for different Cloud platforms is presented. The chapter also provides a mathematical model that formulates the Cloud Computing service provider selection optimization problem based on QoS guarantees. The validation of the provided model is addressed in the chapter through extensive simulation studies conducted on benchmark data of Content Delivery Network providers. The studies report the efficient matching of the model with the market-oriented different platform characteristics.

scholarly journals Memory Based Hybrid Dragonfly Optimization for Multiple Key Generation using Cloud Computing

2020 ◽  
Vol 8 (5) ◽  
pp. 4442-4451
Keyword(s):  
Cloud Computing ◽  
Optimization Algorithm ◽  
Performance Metrics ◽  
New Technology ◽  
Random Number Generator ◽  
Research Work ◽  
Image Data ◽  
Cloud Services ◽  
Key Generation ◽  
Network Access

Cloud Computing, as a new technology with enormous computing services, evolved in recent era. It is a most promising computing that gives services on - demand. The Cloud services becomes a noticeable paradigm, by it notable features like, shared pool of resources, Shared infrastructure, dynamic provisioning, network access, handled assessing forward with gassed-up, gullibility, resilience and adoptable. Likewise, it has influence, Cloud Computing has some issues like security of data transferred via the Cloud, availability of resources and its authenticity, remains as a major task of attention. A Novel Optimized Encrytion-As-A-Service is presented in this paper, with multiple keys generation methodology. The three various Key generation includes, Pseudo Random Number Generator (PRNG), Sub-optimal keys are generated from hybridization of Improved Cipher Block Chaining (ICBC) encryption algorithm and final key is from Memory based Hybrid Dragonfly Optimization Algorithm (MHDA). In turn, MHDA is the combination of Dragonfly Optimization Algorithm (DA) and Particle Swarm Optimization Algorithm (PSO) for generating the innovative key for encryption of data. MHDA gives the better performance analysis compared with DA and PSO optimization approaches. The milestone of this optimized hybridization algorithm is to reduce the time complexity and increase the quality of encryption of the data. The experimental analysis is done for Text, image data and performance metrics are evaluated for the proposed research work. Different parameter that explores the main capacity and strength of the algorithm is examined

Key Performance Information Collection Architecture Based on Cloud Computing

2014 ◽  
Vol 509 ◽  
pp. 182-188
Author(s):  
Bin Chen ◽  
Zhi Jian Wang ◽  
Rong Zhi Qi ◽  
Xin Lv
Keyword(s):  
Cloud Computing ◽  
Queuing Theory ◽  
Virtual Machines ◽  
Cloud Services ◽  
Accurate Information ◽  
Cloud Environment ◽  
Analysis Framework ◽  
Performance Information ◽  
Computing Center ◽  
And Performance

Cloud Computing has become another buzzword in recent years. Follow the popular research and use of the cloud system the performance become the bottleneck of the Newborn. More and more researches are turning their attention to analyze the performance of the cloud services. However, it is hard to extract accurate information from the different type of the cloud components such as datacenter, host, Virtual Machines (VM) in the cloud. Thus, it is significant to collect sufficient row data of the Cloud systems for the performance analysis. In this paper, we described an analysis framework to evaluate comprehensive performance guideline of cloud computing center. The analysis architecture is built based on the performance agent and server interface method (PASI), which consists of performance client (PMC), performance agent (PMA) and performance server (PMS), and we put forward a mathematical model based on the PASI information and queuing theory to forecast the idle rate and availability of the cloud environment. It is proved that the PASI architecture is correctly and effectively evaluates the performance of the cloud component and whole cloud environment.

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