A Novel Hybrid Optimization-Based Approach for Efficient Development of Business-Applications in Cloud

Author(s):  
Soumia Zertal ◽  
Mohamed Batouche ◽  
Zakaria Laboudi

The requests of the companies for the development and deployment of their business-applications in Cloud become more complex so that, sometimes, a one single service cannot carry out the target task on its own. Hence, a user-request is provided as a composite service. On another note, the number of available services is significantly increasing. Therefore, the authors would need to find the optimal cloud service-compositions that satisfy the quality of service values as well as user requirements. The methods proposed in literature for composing cloud services do not consider the composition and deployment constraints of candidate cloud services. This paper presents a novel optimization-based approach for building business-application in Cloud. The proposed approach combines the particle swarm optimization algorithm with some principles of ant colony optimization algorithm to deal with multiple QoS parameters, but also to satisfy the composition and deployment constraints of cloud services. The experimental results show the efficiency of the method for all tests instances.

2020 ◽  
Author(s):  
Falak Nawaz ◽  
Naeem Khalid Janjua

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.


Author(s):  
Huaming Wu ◽  
Qiushi Wang ◽  
Katinka Wolter

Recently, there emerge a variety of clouds in sky and thus, several similar cloud services (from different cloud venders) can be provided to a mobile end device. The goal of cloud-path selection is to find an optimal cloud-path pair between the mobile device and a cloud among a certain class of clouds that provide the same service, in order to carry out the offloaded computation tasks. It is easy to choose the optimal cloud-path to save execution time incurred by offloading program to cloud when considering only one factor. However, there are many Quality of Service (QoS)-based criteria such as performance, bandwidth, financial, security and availability that need to be considered when making final decisions. In this paper, a multiple criteria decision analysis approach based on the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS) in a fuzzy environment is proposed to decide which cloud is the most suitable one for offloading. The AHP is used to determine the weights of the criteria for cloud-path selection, while fuzzy TOPSIS is to obtain the final ranking of alternative clouds. The numerical analysis is performed to evaluate the model. Furthermore, a method based on historical data of the mobile device’s experiences is used to evaluate the importance weights of the alternative cloud service, when it is challenge to measure and acquire the parameters of criteria timely in practical systems.


2012 ◽  
Vol 433-440 ◽  
pp. 3577-3583
Author(s):  
Yan Zhang ◽  
Hao Wang ◽  
Yong Hua Zhang ◽  
Yun Chen ◽  
Xu Li

To overcome the defect of the classical ant colony algorithm’s slow convergence speed, and its vulnerability to local optimization, the authors propose Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining to solve Traveling Salesman Problem according to the characteristics of natural ant colony multi-group and pheromone updating features of ant colony algorithm, combined with OpenMP parallel programming idea. The new algorithm combines three different pheromone updating methods to make a new declining pheromone updating method. It effectively reduces the impact of pheromone on the non-optimal path in the ants parade loop to subsequent ants and improves the parade quality of subsequent ants. It makes full use of multi-core CPU's computing power and improves the efficiency significantly. The new algorithm is compared with ACO through experiments. The results show that the new algorithm has faster convergence rate and better ability of global optimization than ACO.


2019 ◽  
Vol 8 (4) ◽  
pp. 12622-12626

In sighting the distinct patterns of processing capability in a cloud service is pedantic to enhance the resource management and operable conditions of the servers without compromising the Quality of Service is important. Simulations and models based on practicable parameters are required to understand the impact of the load on new system designs and policies. The proposed scheme and analysis provides a requirement for designing new systems which will be lessaffected by process loads. Classifying, analysis and improving (CAI) is done using real-time data center logs and simulations are done based on user requests and data center configurations. Simulations are created using cloudsim framework. Various simulations are done to provide a comprehensive result to improve the resource allocation for the system.


Author(s):  
Ute Riemann

Business processes are not only variable, they are dynamic as well. A key benefit of BPM is the ability to adjust processes accordingly in response to changing market requirements. In parallel to BPM, enterprise cloud computing technology has emerged to provide a more cost effective solution to businesses and services while making use of inexpensive computing solutions, which combines pervasive, internet, and virtualization technologies (). Despite the slow start the business benefits of cloud computing are as such that the transition of BPM to the cloud is now underway. Cloud services refer to the operation of a virtualized, automated, and service-oriented IT landscape that allows the flexible provision and usage-based invoicing of resources, services, and applications via a network or the Internet. The generic term “X-as-a-Service” summarized the business models delivering almost everything as a service. BPM in the cloud is often regarded as a SaaS application. More recently, BPM is being regarded as a PaaS as it facilitates the creation and deployment of applications, in this case business process solutions. The PaaS landscape is the least developed of the four cloud based software delivery models previously discussed. PaaS vendors, such as IBM, Oracle, Microsoft delivered an application platform with managed cloud infrastructure services however more recently the PaaS market has begun to evolve to include other middleware capabilities including process management. BPM PaaS is the delivery of BPM technology as a service via a cloud service provider. In order to be classified as a PaaS a BPM suite requires the following capabilities: the architecture should be multi-tenant, it should be hosted off premise and it should offer elasticity and metering by use capabilities. When we refer to BPM in the cloud what we are really referring to is a combination of BPM PaaS and BPaaS (Business Process as a Service). Business Process as a Service (BPaaS) is a set of pre-defined business processes that allows the execution of customized business processes in the cloud. BPaaS is a complete pre-integrated BPM platform hosted in the cloud and delivered as a service, for the development and execution of general-purpose business process application. Although such a service harbors an economic potential, questions that need to be answered are as follows: Can an individual and company-specific business process supported by a standardized cloud solution, or should we protect process creativity and competitive differentiation by allowing the company to design the processes individually and solely support basic data flows and structures? Does it make sense to take a software solution “out of the box” that handles both data and process in a cloud environment, or would this hinder the creativity of business (process) development leading to a lower quality of processes and consequently to a decrease in the competitive positioning of a company? How to manage the inherent compliance and security topic. Within a completely integrated business application system, all required security aspects can be implemented as safeguards with just enough money. Within the cloud, however, advanced standards and identity prove is required to monitor and measure information exchange across the federation. Thereby there seems to be no need for developing new protocols, but a standardized way to collect and evaluate the collected information.


Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 462 ◽  
Author(s):  
Gao Tilei ◽  
Li Tong ◽  
Yang Ming ◽  
Jiang Rong

The popularity of cloud computing has made cloud services gradually become the leading computing model nowadays. The trustworthiness of cloud services depends mainly on construction processes. The trustworthiness measurement of cloud service construction processes (CSCPs) is crucial for cloud service developers. It can help to find out the causes of failures and to improve the development process, thereby ensuring the quality of cloud service. Herein, firstly, a trustworthiness hierarchy model of CSCP was proposed, and the influential factors of the processes were identified following the international standard ISO/IEC 12207 of the software development process.Further, a method was developed combined with the theory of information entropy and the concept of trustworthiness. It aimed to calculate the risk uncertainty and risk loss expectation affecting trustworthiness. Also, the trustworthiness of cloud service and its main construction processes were calculated. Finally, the feasibility of the measurement method were verified through a case study, and through comparing with AHP and CMM/CMMI methods, the advantages of this method were embodied.


2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Jiangtao Zhang ◽  
Lingmin Zhang ◽  
Hejiao Huang ◽  
Xuan Wang ◽  
Chonglin Gu ◽  
...  

Distributed cloud has been widely adopted to support service requests from dispersed regions, especially for large enterprise which requests virtual desktops for multiple geodistributed branch companies. The cloud service provider (CSP) aims to deliver satisfactory services at the least cost. CSP selects proper data centers (DCs) closer to the branch companies so as to shorten the response time to user request. At the same time, it also strives to cut cost considering both DC level and server level. At DC level, the expensive long distance inter-DC bandwidth consumption should be reduced and lower electricity price is sought. Inside each tree-like DC, servers are trying to be used as little as possible so as to save equipment cost and power. In nature, there is a noncooperative relation between the DC level and server level in the selection. To attain these objectives and capture the noncooperative relation, multiobjective bilevel programming is used to formulate the problem. Then a unified genetic algorithm is proposed to solve the problem which realizes the selection of DC and server simultaneously. The extensive simulation shows that the proposed algorithm outperforms baseline algorithm in both quality of service guaranteeing and cost saving.


2020 ◽  
Vol 47 (5) ◽  
pp. 384-397
Author(s):  
Tatyana V. Masharova ◽  
◽  
Elena A. Mikhlyakova ◽  
Vladimir Y. Krukovskiy ◽  
Guiyun Yang ◽  
...  

The problem and the aim of the study. New challenges and requirements of society and the state to the e-education system necessitate the formation of students' skills for active information interaction and cognitive activity in the digital space. The authors propose to use cloud services for organizing the educational process, e-learning participants integration and communication, expanding didactic tools and improving the quality of graduate training in the electronic educational environment. Research methods. The main methods are theoretical and methodological analysis and generalization of fundamental scientific works on the research problem, processing of the results of control events and the content of the Google Classroom cloud service (assigned tasks, announcements, instructions, links to additional resources). The pedagogical experiment involved 52 students (35% of girls and 65% of boys) from the Law Institute of Vyatka State University. The G sign criterion is used as a statistical processing method. Results. The features of the organization of e-learning in a digital school have been formulated to enhance information interaction based on the use of cloud technologies through the Google Classroom service: the integration of students and teachers into a single information and educational space, expanding the possibilities of learning and communication; "synchronization" of educational and cognitive activities in different digital resources; the orientation of the service content to the peculiarities of the modern student’s perception, the ability to work in the format of mobile applications activates cognitive interest, research activities and communication practice are supported. The empirical value Gemp=3<Gcr=10 (for p=0.01) obtained during the experiment confirms that the shift towards improving the quality of educational results after using the Google Classroom cloud service is not accidental. Conclusion. The use of cloud services in organizing information interaction in the e-learning system will improve the quality of educational results while providing a set of conditions: enhancing cognition, connecting students to information interaction in new ways, expanding the boundaries of the educational environment, modifying the roles of participants in the didactic process, and using mobile applications.


2019 ◽  
Vol 8 (3) ◽  
pp. 1457-1462

Cloud computing technology has gained the attention of researchers in recent years. Almost every application is using cloud computing in one way or another. Virtualization allows running many virtual machines on a single physical computer by sharing its resources. Users can store their data on datacenter and run their applications from anywhere using the internet and pay as per service level agreement documents accordingly. It leads to an increase in demand for cloud services and may decrease the quality of service. This paper presents a priority-based selection of virtual machines by cloud service provider. The virtual machines in the cloud datacenter are configured as Amazon EC2 and algorithm is simulated in cloud-sim simulator. The results justify that proposed priority-based virtual machine algorithm shortens the makespan, by 11.43 % and 5.81 %, average waiting time by 28.80 % and 24.50%, and cost of using the virtual machine by 21.24% and 11.54% as compared to FCFS and ACO respectively, hence improving quality of service.


Author(s):  
Richard Otuka

Presently, SMEs are finding it difficult to adopt cloud services for their businesses due to various service providers offering similar services. In addition, little work has been carried out in regards to the cloud services adoption process by SMEs. In this chapter, the authors propose CLOUDSME, a novel framework that aids in the adoption process of SaaS cloud services. Accordingly, they implement a decision support system, which includes an ontology of cloud services knowledge within the proposed framework. Analytical hierarchical process (AHP) is used to determine the weight of each cloud service attribute, and a benchmark is set to determine the acceptability of each cloud service based on its ability to meet the acceptable benchmark for each criteria. It can also help in a healthy competition to improve the quality of service among cloud service providers. The CLOUDSME semantic model will guide SME owners in answering user requirements towards decision making in the cloud service adoption process.


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