SLA-aware placement of multi-virtual machine elastic services in compute clouds

Breitgand, David; Epstein, Amir

doi:10.1109/inm.2011.5990687

Cited by 55 publications

(50 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our approach is similar to application component placement approaches [20,21,22,23], where applications consisting of multiple components, represented as a set of Virtual Machines (VMs), are placed within a datacenter taking the relation between components into account. These approaches typically focus on colocation, anti-colocation and other placement constraints used to impact application security, performance, and reliability.…”

Section: Related Workmentioning

confidence: 99%

Allocating resources for customizable multi-tenant applications in clouds using dynamic feature placement

Moens

Dhoedt

Turck

2015

Future Generation Computer Systems

View full text Add to dashboard Cite

Multi-tenancy, where multiple end users make use of the same application instance, is often used in clouds to reduce hosting costs. A disadvantage of multi-tenancy is however that it makes it difficult to create customizable applications, as all end users use the same application instance. In this article, we describe an approach for the development and management of highly customizable multi-tenant cloud applications. We apply software product line engineering techniques to cloud applications, and use an approach where applications are composed of multiple interacting components, referred to as application features. Using this approach, multiple features can be shared between different applications. Allocating resources for these feature-based applications is complex, as relations between components must be taken into account, and is referred to as the feature placement problem.In this article, we describe dynamic feature placement algorithms that minimize migrations between subsequent invocations, and evaluate them in dynamic scenarios where applications are added and removed throughout the evaluation scenario. We find that the developed algorithm achieves a low cost, while resulting in few resource migrations. In our evaluations, we observe that adding migration-awareness to the management algorithms reduces the number of instance migrations by more than 77% and reduces the load moved between instances by more than 96% when compared to a static management approach. Despite this reduction in number of migrations, a cost that is on average less than 3% more than the optimal cost is achieved.

show abstract

Section: Related Workmentioning

confidence: 99%

Allocating resources for customizable multi-tenant applications in clouds using dynamic feature placement

Moens

Dhoedt

Turck

2015

Future Generation Computer Systems

View full text Add to dashboard Cite

show abstract

“…Biran et al [5] formulated the network-aware VM placement problem as a Min Cut Ratio-aware VM Placement problem that considers not only local physical resources but also network resources, and introduced heuristics to solve it. In addition to these, there are also studies focusing on the SLA aspect [6,8,22], the contention between VMs [11,13], and the temperature in the data center [18]. However, the IT managers' preferences are not considered in the literature.…”

Section: Related Workmentioning

confidence: 99%

SOC: Satisfaction-Oriented Virtual Machine Consolidation in Enterprise Data Centers

Ventresque

Murphy

et al. 2014

Int J Parallel Prog

View full text Add to dashboard Cite

“…Whalley et al [19] extended a Virtual Machine (VM) management system to take into account the complexities of software licensing. In a similar way, Breitgand et al [20] added the consideration of Service Level Agreements (SLAs) to the placement problem. The consideration of energy consumption and carbon emissions was added in [21] using a system that works in parallel with existing datacenter brokering systems.…”

Section: Application Placementmentioning

confidence: 99%

Cost-Effective Feature Placement of Customizable Multi-Tenant Applications in the Cloud

et al. 2013

View full text Add to dashboard Cite

Cloud computing technologies can be used to more flexibly provision application resources. By exploiting multi-tenancy, instances can be shared between users, lowering the cost of providing applications. A weakness of current cloud offerings however, is the difficulty of creating customizable applications that retain these advantages. In this article, we define a feature-based cloud resource management model, making use of Software Product Line Engineering (SPLE) techniques, where applications are composed of feature instances using a service-oriented architecture. We focus on how resources can be allocated in a cost-effective way within this model, a problem which we refer to as the feature placement problem. A formal description of this problem, that can be used to allocate resources in a cost-effective way, is provided. We take both the cost of failure to place features, and the cost of using servers into account, making it possible to take energy costs or the cost of public cloud infrastructure into consideration during the placement calculation. Four algorithms that can be used to solve the feature placement problem are defined. We evaluate the algorithm solutions, comparing them with the optimal solution determined using an integer linear problem solver, and evaluating the execution times of the algorithms, making use of both generated inputs and a use case based on three applications. We show that, using our approach a higher degree of multi-tenancy can be achieved, and that for the considered scenarios, taking the relationships between features into account and using application-oriented placement performs 25% to 40% better than a purely feature-oriented placement.

show abstract

SLA-aware placement of multi-virtual machine elastic services in compute clouds

Cited by 55 publications

References 23 publications

Allocating resources for customizable multi-tenant applications in clouds using dynamic feature placement

Allocating resources for customizable multi-tenant applications in clouds using dynamic feature placement

SOC: Satisfaction-Oriented Virtual Machine Consolidation in Enterprise Data Centers

Cost-Effective Feature Placement of Customizable Multi-Tenant Applications in the Cloud

Contact Info

Product

Resources

About