Design and evaluation of a scalable hierarchical application component placement algorithm for cloud resource allocation

In the context of cloud systems, mapping application components to a set of physical servers and assigning resources to those components is challenging. For large-scale clouds, traditional resource allocation systems, which rely on a centralized management paradigm, become ineffective and inefficient. Therefore, there is an essential need of providing new management solutions that scale well with the size of large cloud systems. In this paper a distributed and hierarchical component placement algorithm is presented, evaluated and compared to a centralized algorithm. Each application is represented as a collection of interacting services, and multiple service types with differing placement characteristics are considered. Our evaluations show that the proposed algorithm is at least 84.65 times faster and offers better scalability compared with a central approach, while the percentage of servers used and fully placed applications remains close to that of the centralized algorithm

Algorithms for efficient data management of component-based applications in cloud environments

Cloud environments face a growing demand for application hosting, and applications consisting of multiple data-sources and storage components. The need to ensure service level agreements for these types of applications creates important challenges for cloud infrastructure providers. The main contribution of this paper is an optimal cost-effective model and two algorithms to map component-based data oriented applications to cloud platforms. The first algorithm is based on an Integer Linear Programming formulation and minimizes an objective function, taking into account the capacities of the available nodes and links, as well as the customer requirements. This algorithm is able to obtain the optimal solution, but shows a limited scalability. For this reason a heuristic algorithm is designed to solve the scalability issue. The experimental results thoroughly compare the execution times and obtained node usage for both algorithms

VNF-P: a model for efficient placement of virtualized network functions

Network Functions Virtualization (NFV) is an up-coming paradigm where network functionality is virtualized and split up into multiple building blocks that can be chained together to provide the required functionality. This approach increases network flexibility and scalability as these building blocks can be allocated and reallocated at runtime depending on demand. The success of this approach depends on the existence and performance of algorithms that determine where, and how these building blocks are instantiated. In this paper, we present and evaluate a formal model for resource allocation of virtualized network functions within NFV environments, a problem we refer to as Virtual Network Function Placement (VNF-P). We focus on a hybrid scenario where part of the services may be provided by dedicated physical hardware, and where part of the services are provided using virtualized service instances. We evaluate the VNF-P model using a small service provider scenario and two types of service chains, and evaluate its execution speed. We find that the algorithms finish in 16 seconds or less for a small service provider scenario, making it feasible to react quickly to changing demand