Cost-Effective Resource Configurations for Multi-Tenant Database Systems in Public Clouds

2015 ◽  
Vol 5 (2) ◽  
pp. 1-22 ◽  
Author(s):  
Rizwan Mian ◽  
Patrick Martin ◽  
Farhana Zulkernine ◽  
Jose Luis Vazquez-Poletti
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Cost Effective ◽  
Database Systems ◽  
Service Level ◽  
Database System ◽  
Resource Provisioning ◽  
Cost Models ◽  
Service Level Agreements ◽  
Expected Cost

Cloud computing is a promising paradigm for deploying applications due to its large resource offerings on a pay-as-you-go basis. This paper examines the problem of determining the most cost-effective provisioning of a multi-tenant database system as a service over public clouds. The authors formulate the problem of resource provisioning, and then define a framework to solve it. Their framework uses heuristic based algorithms to select cost-effective configurations. The algorithms can optionally balance resource costs against penalties incurred from the violation of Service Level Agreements (SLAs) or opt for non SLA violating configurations. The specific resource demands on the virtual machines for a workload and SLAs are accounted for by the performance and cost models, which are used to predict performance and expected cost respectively. The work validates our approach experimentally using workloads based on standard TPC database benchmarks in the Amazon EC2 cloud.

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

Modeling And Analyzing Cloud Auto-Scaling Mechanism Using Stochastic Well-Formed Coloured Nets

2021 ◽  
Author(s):  
Mohamed M. Ould Deye ◽  
Mamadou Thiongane ◽  
Mbaye Sene
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Service Level ◽  
The State ◽  
Service Level Agreements ◽  
Auto Scaling

Auto-scaling is one of the most important features in Cloud computing. This feature promises cloud computing customers the ability to best adapt the capacity of their systems to the load they are facing while maintaining the Quality of Service (QoS). This adaptation will be done automatically by increasing or decreasing the amount of resources being leveraged against the workload’s resource demands. There are two types and several techniques of auto-scaling proposed in the literature. However, regardless the type or technique of auto-scaling used, over-provisioning or under-provisioning problem is often observed. In this paper, we model the auto-scaling mechanism with the Stochastic Well-formed coloured Nets (SWN). The simulation of the SWN model allows us to find the state of the system (the number of requests to be dispatched, the idle times of the started resources) from which the auto-scaling mechanism must be operated in order to minimize the amount of used resources without violating the service-level agreements (SLA).

Information Stewardship in Cloud Computing

2012 ◽  
pp. 185-202
Author(s):  
David Pym ◽  
Martin Sadler
Keyword(s):  
Mathematical Model ◽  
Cloud Computing ◽  
System Design ◽  
Service Providers ◽  
Cost Savings ◽  
Service Level ◽  
Information Services ◽  
Significant Part ◽  
Service Level Agreements ◽  
Economic Technology

Cloud computing ecosystems of service providers and consumers will become a significant part of the way information services are provided, allowing more agile coalitions, cost savings and improved service delivery. Existing approaches to information security do not readily extend to this complex multi-party world. The authors argue for a mathematical model-based framework for the analysis and management of information stewardship that makes explicit both the expectations and responsibilities of cloud stakeholders and the design assumptions of systems. Such a framework supports integrated economic, technology, and behavioural analyses, so providing a basis for a better understanding of the interplay between preferences, policies, system design, regulations, and Service Level Agreements. The authors suggest approaches to constructing economic, technology, and behavioural models and discuss the challenges in integrating them.

Using Obstacles for Systematically Modeling, Analysing, and Mitigating Risks in Cloud Adoption

2013 ◽  
pp. 275-296 ◽  
Author(s):  
Shehnila Zardari ◽  
Funmilade Faniyi ◽  
Rami Bahsoon
Keyword(s):  
Cloud Computing ◽  
Cost Effective ◽  
Service Level ◽  
Management Framework ◽  
Cloud Adoption ◽  
Risk Management Framework ◽  
Cost Implications ◽  
Effective Service ◽  
Cloud User

In this chapter, the authors motivate the need for a systematic approach to cloud adoption from the risk perspective. The enormous potential of cloud computing for improved and cost-effective service delivery for commercial and academic purposes has generated unprecedented interest in its adoption. However, a potential cloud user faces numerous risks regarding service requirements, cost implications of failure, and uncertainty about cloud providers’ ability to meet service level agreements. Hence, the authors consider two perspectives of a case study to identify risks associated with cloud adoption. They propose a risk management framework based on the principle of GORE (Goal-Oriented Requirements Engineering). In this approach, they liken risks to obstacles encountered while realising cloud user goals, therefore proposing cloud-specific obstacle resolution tactics for mitigating identified risks. The proposed framework shows benefits by providing a principled engineering approach to cloud adoption and empowering stakeholders with tactics for resolving risks when adopting the cloud.

Cloud-Based Platforms and Infrastructures

2017 ◽  
pp. 47-90
Author(s):  
Marcus Tanque
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Cloud Service ◽  
Resource Provisioning ◽  
Virtual Networks ◽  
Network Resources ◽  
Computing Services ◽  
Service Models ◽  
Cloud Computing Services ◽  
Shared Network

Cloud computing consists of three fundamental service models: infrastructure-as-a-service, platform-as-a service and software-as-a-service. The technology “cloud computing” comprises four deployment models: public cloud, private cloud, hybrid cloud and community cloud. This chapter describes the six cloud service and deployment models, the association each of these services and models have with physical/virtual networks. Cloud service models are designed to power storage platforms, infrastructure solutions, provisioning and virtualization. Cloud computing services are developed to support shared network resources, provisioned between physical and virtual networks. These solutions are offered to organizations and consumers as utilities, to support dynamic, static, network and database provisioning processes. Vendors offer these resources to support day-to-day resource provisioning amid physical and virtual machines.

Optimal Management of Cloud Centers with Different Arrival Modes for Cloud Computing Environment

2012 ◽  
Vol 2 (3) ◽  
pp. 86-97
Author(s):  
Veena Goswami ◽  
Sudhansu Shekhar Patra ◽  
G. B. Mund
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Finite Buffer ◽  
Web Browser ◽  
Computing Paradigm ◽  
Computing Services ◽  
Multi Server

Cloud computing is a new computing paradigm in which information and computing services can be accessed from a Web browser by clients. Understanding of the characteristics of computer service performance has become critical for service applications in cloud computing. For the commercial success of this new computing paradigm, the ability to deliver guaranteed Quality of Services (QoS) is crucial. Based on the Service level agreement, the requests are processed in the cloud centers in different modes. This paper analyzes a finite-buffer multi-server queuing system where client requests have two arrival modes. It is assumed that each arrival mode is serviced by one or more Virtual machines, and both the modes have equal probabilities of receiving service. Various performance measures are obtained and optimal cost policy is presented with numerical results. The genetic algorithm is employed to search the optimal values of various parameters for the system.

A Secured Real Time Scheduling Model for Cloud Hypervisor

2016 ◽  
Vol 6 (4) ◽  
pp. 97-110
Author(s):  
Rekha Kashyap ◽  
Deo Prakash Vidyarthi
Keyword(s):  
Cloud Computing ◽  
Success Rate ◽  
Real Time ◽  
Virtual Machines ◽  
Resource Provisioning ◽  
Real Time Scheduling ◽  
Scheduling Model ◽  
Physical Resources ◽  
Time Scheduling ◽  
Deadline Constraints

Virtualization is critical to cloud computing and is possible through hypervisors, which maps the Virtual machines((VMs) to physical resources but poses security concerns as users relinquish physical possession of their computation and data. Good amount of research is initiated for resource provisioning on hypervisors, still many issues need to be addressed for security demanding and real time VMs. First work SRT-CreditScheduler (Secured and Real-time), maximizes the success rate by dynamically prioritizing the urgency and the workload of VMs but ensures highest security for all. Another work, SA-RT-CreditScheduler (Security-aware and Real-time) is a dual objective scheduler, which maximizes the success rate of VMs in best possible security range as specified by the VM owner. Though the algorithms can be used by any hypervisor, for the current work they have been implemented on Xen hypervisor. Their effectiveness is validated by comparing it with Xen's, Credit and SEDF scheduler, for security demanding tasks with stringent deadline constraints.

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