Optimization of the Distribution of Hash Calculation Tasks Flow at a Priori Given Complexity

2021 ◽  
Vol 27 (5) ◽  
pp. 242-248
Author(s):  
P. E. Golosov ◽  
◽  
I. M. Gostev ◽  
Keyword(s):  
Parallel Computing ◽  
A Priori ◽  
Service Level Agreement ◽  
Service Level ◽  
Cloud Services ◽  
Computational Process ◽  
Task Distribution ◽  
Block Chain ◽  
Computationally Intensive ◽  
Computational Resources

The increasing number of computationally intensive tasks induced by digital economy development (within the framework of implementing block-chain solutions, distributed ledgers, etc.) requires more and more computational resources. At the same time users tend to move the computational process to the cloud in order to minimize costs, and the owners of cloud services are forced to look for solutions to improve their efficiency. In this paper we consider approaches that allow optimize the use of parallel computing resources for incoming sets of resource-intensive tasks, analyze different approaches to the strategy of assigning tasks to computing resources. The results of modelling experiments are provided taking into account task distribution, parameterized by execution time limit within modeling the service level agreement with the user.

SLA (Service Level Agreement) Driven Orchestration Based New Methodology for Cloud Computing Services

2013 ◽  
Vol 660 ◽  
pp. 196-201 ◽  
Author(s):  
Muhammad Irfan ◽  
Zhu Hong ◽  
Nueraimaiti Aimaier ◽  
Zhu Guo Li
Keyword(s):  
Cloud Computing ◽  
Computer Science ◽  
Service Providers ◽  
Service Level Agreement ◽  
Cloud Service ◽  
Service Level ◽  
Cloud Services ◽  
Computing Services ◽  
Cloud Service Providers ◽  
Cloud Computing Services

Cloud Computing is not a revolution; it’s an evolution of computer science and technology emerging by leaps and bounds, in order to merge all computer science tools and technologies. Cloud Computing technology is hottest to do research and explore new horizons of next generations of Computer Science. There are number of cloud services providers (Amazon EC2), Rackspace Cloud, Terremark and Google Compute Engine) but still enterprises and common users have a number of concerns over cloud service providers. Still there is lot of weakness, challenges and issues are barrier for cloud service providers in order to provide cloud services according to SLA (Service Level agreement). Especially, service provisioning according to SLAs is core objective of each cloud service provider with maximum performance as per SLA. We have identified those challenges issues, as well as proposed new methodology as “SLA (Service Level Agreement) Driven Orchestration Based New Methodology for Cloud Computing Services”. Currently, cloud service providers are using “orchestrations” fully or partially to automate service provisioning but we are trying to integrate and drive orchestration flows from SLAs. It would be new approach to provision cloud service and deliver cloud service as per SLA, satisfying QoS standards.

scholarly journals An Effiecient Approach for Resource Auto-Scaling in Cloud Environments

2016 ◽  
Vol 6 (5) ◽  
pp. 2415
Author(s):  
Bahar Asgari ◽  
Mostafa Ghobaei Arani ◽  
Sam Jabbehdari
Keyword(s):  
Service Level Agreement ◽  
Service Level ◽  
Resource Provisioning ◽  
Cloud Services ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Sla Violation ◽  
Markov Decision ◽  
Cloud Environments ◽  
Auto Scaling

<p>Cloud services have become more popular among users these days. Automatic resource provisioning for cloud services is one of the important challenges in cloud environments. In the cloud computing environment, resource providers shall offer required resources to users automatically without any limitations. It means whenever a user needs more resources, the required resources should be dedicated to the users without any problems. On the other hand, if resources are more than user’s needs extra resources should be turn off temporarily and turn back on whenever they needed. In this paper, we propose an automatic resource provisioning approach based on reinforcement learning for auto-scaling resources according to Markov Decision Process (MDP). Simulation Results show that the rate of Service Level Agreement (SLA) violation and stability that the proposed approach better performance compared to the similar approaches.</p>

scholarly journals A Review on Various Applications of Reputation Based Trust Management

2021 ◽  
Vol 15 (10) ◽  
pp. 87
Author(s):  
Govindaraj Ramya ◽  
Govindaraj Priya ◽  
Chowdhury Subrata ◽  
Dohyeun Kim ◽  
Duc Tan Tran ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Management System ◽  
Trust Management ◽  
Service Level Agreement ◽  
Service Level ◽  
Peer To Peer ◽  
Cloud Services ◽  
Management Systems ◽  
Significant Challenge ◽  
The Future

<p class="0abstract">The extremely vibrant, scattered, and non–transparent nature of cloud computing formulate trust management a significant challenge. According to scholars the trust and security are the two issues that are in the topmost obstacles for adopting cloud computing. Also, SLA (Service Level Agreement) alone is not necessary to build trust between cloud because of vague and unpredictable clauses. Getting feedback from the consumers is the best way to know the trustworthiness of the cloud services, which will help them improve in the future. Several researchers have stated the necessity of building a robust management system and suggested many ideas to manage trust based on consumers' feedback. This paper has reviewed various reputation-based trust management systems, including trust management in cloud computing, peer-to-peer system, and Adhoc system. </p>

rSLA: A Service Level Agreement Language for Cloud Services

2016 ◽  
Author(s):  
Samir Tata ◽  
Mohamed Mohamed ◽  
Takashi Sakairi ◽  
Nagapramod Mandagere ◽  
Obinna Anya ◽  
...  
Keyword(s):  
Service Level Agreement ◽  
Service Level ◽  
Cloud Services

scholarly journals A Novel Probabilistic Strategy for Delay Corrected Allocation in Shared Resource Systems

2017 ◽  
Vol 17 (2) ◽  
pp. 83-96
Author(s):  
G. Arun Kumar ◽  
Aravind Sundaresan ◽  
Snehanshu Saha ◽  
Bidisha Goswami ◽  
Shakti Mishra
Keyword(s):  
Resource Allocation ◽  
Learning Strategy ◽  
Service Providers ◽  
A Priori ◽  
Peak Load ◽  
Service Level Agreement ◽  
Service Level ◽  
Cloud Provider ◽  
Average Delay ◽  
Priori Information

Abstract Cloud computing offers scalable services to the user where computing resources are owned by a cloud provider. The resources are offered to clients on pay-per-use basis. However, since multiple clients share the cloud’s resources, they could potentially interfere with each others’ task during peak load instances. The environment changes every instant of time with a new set of job requests demanding resource while another set of jobs relieving another set of resources. A major challenge among the service providers is to maintain a balance without compromising Service Level Agreement (SLA). In case of peak load, when each client strives for a particular resource in minimal time, the resource allocation problem becomes more challenging. The important issue is to fulfil the SLA criterion without delaying the resource allocation. The paper proposes a n-player game-based Machine learning strategy that would forecast outcome using a priori information available and measure/estimate existing parameters such as utilization and delay in an optimal load-balanced paradigm. The simulation validates the conclusion of the theorem by showing that average delay is low and stays in that range as the number of job requests increase. In future, we shall extend this work to multi-resource, multi-user environment.

An archetype for mitigating the security threats in multi-cloud environment by implementing tree-based next-generation firewalls

2021 ◽  
pp. 1-12
Author(s):  
Muhammad Iftikhar Hussain ◽  
Jingsha He ◽  
Nafei Zhu ◽  
Zulfiqar Ali Zardari ◽  
Fahad Razque ◽  
...  
Keyword(s):  
Cloud Model ◽  
Service Level Agreement ◽  
Service Level ◽  
Specific Model ◽  
Cloud Services ◽  
Next Generation ◽  
Cloud Platform ◽  
End User ◽  
Heterogeneous Cloud ◽  
Multi Cloud

Cloud computing on-demand dynamicity in nature of end-user that leads towards a hybrid cloud model deployment is called a multi-cloud. Multi-cloud is a multi-tenant and multi-vendor heterogeneous cloud platform in terms of services and security under a defined SLA (service level agreement). The diverse deployment of the multi-cloud model leads to rise in security risks. In this paper, we define a multi-cloud model with hybridization of vendor and security to increase the end-user experience. The proposed model has a heterogeneous cloud paradigm with a combination of firewall tracts to overcome rising security issues. The proposed work consists of three steps, firstly, all incoming traffic from the consumer end into five major groups called ambient. Secondly, design a next-generation firewall (NGFW) topology with a mixture of tree-based and demilitarized zone (DMZ) implications. Test implementation of designed topology performed by using a simple DMZ technique in case of vendor-specific model and NGFW on hybrid vendor based multi-cloud model. Furthermore, it also defines some advantages of NGFW to overcome these concerns. The proposed work is helpful for the new consumer to define their dynamic secure cloud services under a single SLA before adopting a multi-cloud platform. Finally, results are compared in terms of throughput and CPU utilization in both cases.

scholarly journals Priority-Based Virtual Machine Selection Algorithm in Cloud Computing

2019 ◽  
Vol 8 (3) ◽  
pp. 1457-1462
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Cloud Service ◽  
Service Level ◽  
Cloud Services ◽  
Cloud Service Provider

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.

Carrier-Grade Distributed Cloud Computing

2013 ◽  
pp. 264-281
Author(s):  
Dapeng Wang ◽  
Jinsong Wu
Keyword(s):  
Cloud Computing ◽  
Service Level Agreement ◽  
Cost Effective ◽  
Cloud Service ◽  
Service Level ◽  
Cloud Services ◽  
Cloud Infrastructure ◽  
Key Issues ◽  
Security Network ◽  
Distributed Cloud

This chapter discusses and surveys the concepts, demands, requirements, solutions, opportunities, challenges, and future perspectives and potential of Carrier Grade Cloud Computing (CGCC). This chapter also introduces a carrier grade distributed cloud computing architecture and discusses the benefits and advantages of carrier grade distributed cloud computing. Unlike independent cloud service providers, telecommunication operators may integrate their conventional communications networking capabilities with the new cloud infrastructure services to provide inexpensive and high quality cloud services together with their deep understandings of, and strong relationships with, individual and enterprise customers. The relevant design requirements and challenges may include the performance, scalability, service-level agreement management, security, network optimization, and unified management. The relevant key issues in CGCC designs may include cost effective hardware and software configurations, distributed infrastructure deployment models, and operation processes.

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