Autonomous service composition in symbiotic networks

Part 2: PhD Workshop: Autonomic Network and Service ManagementInternational audienceTo cope with the ever-growing number of wired and wireless networks, we introduce the notion of so-called symbiotic networks. These networks seamlessly operate across layers and over network boundaries, resulting in improved scalability, dependability, and energy efficiency. This particular Ph.D. research focuses on software services operating in such symbiotic networks. When two or more networks merge, the services provided on them may be combined into a service composition that is much more than the sum of its parts. Driven by two distinct use cases, we aim to enable fully autonomous service composition and resource provisioning. For the first use case, an in-building over-the-top service platform, we describe a software architecture and a set of generic resource provisioning algorithms. The second use case, which focuses on wireless body area networks, will allow us to expand our research domain into highly dynamic symbiotic network environments, where services appear and disappear more frequently

Design of an autonomous software platform for future symbiotic service management

Nowadays, public as well as private communication infrastructures are all contending for the same limited amount of bandwidth. To optimally share network resources, symbiotic networks have been proposed, which cross logical and physical boundaries to improve the reliability, scalability, and energy efficiency of the network as a whole as well as its constituents. This paper focuses on software services in such symbiotic networks. We propose a platform for the intelligent composition of services provided by symbiotically connected parties, resulting in novel cooperation opportunities. The platform harvests Semantic Web technology to describe services in a highly expressive manner, and constructs service compositions using SeCoA, our tunable best-first search algorithm. The resulting compositions are then enacted via CaPI, a reconfigurable middleware infrastructure. By means of an illustrative scenario, we provide further insight into the platform's functioning

Resource management in a containerized cloud : status and challenges

Cloud computing heavily relies on virtualization, as with cloud computing virtual resources are typically leased to the consumer, for example as virtual machines. Efficient management of these virtual resources is of great importance, as it has a direct impact on both the scalability and the operational costs of the cloud environment. Recently, containers are gaining popularity as virtualization technology, due to the minimal overhead compared to traditional virtual machines and the offered portability. Traditional resource management strategies however are typically designed for the allocation and migration of virtual machines, so the question arises how these strategies can be adapted for the management of a containerized cloud. Apart from this, the cloud is also no longer limited to the centrally hosted data center infrastructure. New deployment models have gained maturity, such as fog and mobile edge computing, bringing the cloud closer to the end user. These models could also benefit from container technology, as the newly introduced devices often have limited hardware resources. In this survey, we provide an overview of the current state of the art regarding resource management within the broad sense of cloud computing, complementary to existing surveys in literature. We investigate how research is adapting to the recent evolutions within the cloud, being the adoption of container technology and the introduction of the fog computing conceptual model. Furthermore, we identify several challenges and possible opportunities for future research

Design and evaluation of a hierarchical multi-tenant data management framework for cloud applications

Cloud computing is a technology that enables elastic, on-demand resource provisioning. Migrating applications to the cloud can increase their elasticity, allowing them to adapt to workload changes by dynamically allocating resources. In a multi-tenant application multiple client organizations, each referred to as tenants, make use of one or more shared application instances. These shared instances must however behave like a private instance by guaranteeing both data separation and performance isolation for every tenant. In order to achieve high scalability, a multi-tenant application running on the elastic cloud requires a flexible and scalable architecture for both the computational resources and the storage resources. In this paper we present and evaluate the design of a data management framework which can be used to extend existing multi-tenant cloud applications in order to achieve high scalability of the storage resources. We describe the most important components, and discuss important design choices. The framework invokes data allocation algorithms in order to find a feasible allocation of tenant data resulting in a minimal operating cost and a maximal performance, while taking no more than 10 ms to execute

Scalable user data management in multi-tenant cloud environments

The rise of cloud computing and its elastic, on-demand resource provisioning introduces the need for a flexible and scalable multi-tenant architecture. In a multi-tenant application every tenant (client) makes use of shared application instances, but each tenant typically has its own user data. The shared application instance behaves like a private instance by guaranteeing both data separation and performance separation for every tenant. As the number of tenants increases, the amount of data grows. A scalable solution for the storage is needed, allowing tenant data to be divided over multiple database instances, but taking into account performance isolation and custom data assurance policies. In this paper we introduce an abstraction layer for achieving high scalability for the storage of tenant data. This layer uses data allocation algorithms to determine an acceptable allocation of tenant data to different databases. We describe a mathematical model for the allocation of tenant data which can be optimized using existing linear programming techniques, and introduce the BDAA-n and FDAA, two algorithms that will find an optimal allocation of data by iterating over the possible permutations. The proposed solutions are evaluated based on their flexibility, complexity and efficiency. The flexibility of the BDAA and FDAA makes them easy to customize and extend to fit most scenarios, but the algorithms will achieve best results for tenants with a limited number of subtenants. Linear programming is an alternative for tenants with a higher number of subtenants, but the customizability of the algorithm for specific use cases is limited due to the need for linear functions

Symbiotic service composition in distributed sensor networks

To cope with the evergrowing number of colocated networks and the density they exhibit, we introduce symbiotic networks-networks that intelligently share resources and autonomously adapt to the dynamicity thereof. By allowing the software services provided in such networks to operate in an equally symbiotic manner, new opportunities for the so-called service compositions arise, which take advantage of the multitude of services and combine them to achieve goals set out by the individual networks. To accommodate services in large-scale symbiotic networks, including wireless sensor networks, we propose a software platform which autonomously constructs and orchestrates such compositions. Furthermore, upon changes in the infrastructure, the platform responds by adapting compositions to reflect the changed context. To enable the interaction between services offered by arbitrary partners, the platform deploys ontologies to achieve a common vocabulary and semantic rules to express the policies imposed by the networks involved. By applying the platform to typical scenarios from the field of sensor-augmented cargo transportation and logistics, we illustrate its applicability and, through performance evaluation, show a significant increase in process efficiency. Additionally, by means of a generic problem generator, we quantify the scalability of our platform and show the importance of an appropriate priority function, one of the core constituents of our service composition approach

A simulation tool for evaluating the constraint-based allocation of storage resources for multi-tenant cloud applications

Cloud computing is closely related to multi-tenancy, as it relies on resources that are shared among multiple clients. The provisioning and management of storage resources for cloud applications is an interesting research topic, as reallocation of data over time should be minimised, and the developed strategy should guarantee both data separation and performance isolation for every tenant. In this demo, we present a simulation tool for evaluating and comparing different data allocation strategies. Evaluation using real implementations can be very expensive and time consuming, and is not always possible, due to the scale and complexity of the infrastructure on which they are intended to run. The simulator aids as a tool for inexpensive and rapid evaluation of new techniques, and to validate and finetune new data allocation strategies