Design and evaluation of a hierarchical application placement algorithm in large scale clouds

Abstract

As the requirements and scale of cloud environments increase, scalable management of the cloud is needed. Central ized solutions lack scalability and fully distributed management systems only have a limited overview of the system. One of the often-studied problems in cloud environments is the application placement problem, used to decide where application instances are instantiated and how many resources to allocate to the instances. In this paper a general approach is introduced for using centralized cloud resource management algorithms in a hierarchical context, increasing the scalability of the manage ment system while maintaining a high placement quality. The management system itself is executed on the cloud, further increasing scalability and robustness. The proposed method uses aggregation techniques to generate input values for a centralized application placement algorithm which is run in all management nodes. Decoupling ensures management nodes can function independently. Subsequently, we compare the performance of hierarchical application placement method with that of a fully centralized algorithm. The results show that a solution, within 5% of the optimum placement when using the centralized algorithm, can be achieved hierarchically in less than 25 % of the time needed for execution of the centralized algorithm.

Shared resource network-aware impact determination algorithms for service workflow deployment with partial cloud offloading

“…Application placement is used to determine the location of applications within networks [10,11,12] or clouds [13,14,15], taking into account the demand for each application. Application placement is used to coordinate applications.…”

“…Finally, a constraint is defined for every group in the hierarchy, linking the capacity assigned to its children to its own capacity. For this we use an expression based on that of Equation (15), but rather than defining it for the total set of commodities C it is used within individual groups. The resulting expression is shown in Equation (18).…”

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

“…Our approach generalizes these inputs, as done in [27], but goes further by allowing for the definition of multiple resources. This is achieved by making use of concepts we previously described in [28], enabling the management of highvariability applications with heterogeneous resource demands.…”

“…We have previously made a similar distinction between resources in [28], and a similar approach was used in [27]. Within the model, we define Γ as the collection of all resource types, and we use Γ s and Γ s to denote strict and non-strict resource types respectively.…”

“…The presented algorithms still make use of a centralized approach, implying they could become a bottleneck as the size of the cloud increases. To address this, techniques such as those we presented in [28] could be used to increase the scalability of the algorithms in larger clouds, by reusing the centralized algorithms within a hierarchically structured management infrastructure.…”