An execution environment as a service for adaptive long-running workflows

Purpose This paper aims to present Long4Cloud (long-running workflows execution environment for cloud), a distributed and adaptive LRW execution environment delivered “as a service” solution. Design/methodology/approach LRWs last for hours, days or even months and their duration open the possibility of changes in business rules, service interruptions or even alterations of formal regulations of the business before the workflow completion. These events can lead to problems such as loss of intermediary results or exhaustion of computational resources used to manage the workflow execution. Existing solutions face those problems by merely allowing the replacement (at runtime) of services associated with activities of the LRW. Findings LONG4Cloud extends the previous works in two main aspects, namely, the inclusion of dynamic reconfiguration capabilities and the adoption of an “as a service” delivery mode. The reconfiguration mechanism uses quiescence principles, data and state management and provides multiple adaptive strategies. Long4Cloud also adopts a scenario-based analysis to decide the adaptation to be performed. Events such as changes in business rules or service failures trigger reconfigurations supported by the environment. These features have been put together in a solution delivered “as a service” that takes advantage of cloud elasticity and allows to better allocate cloud resources to fit into the demands of LRWs. Originality/value The original contribution of Long4Cloud is to incorporate adaptive capabilities into the LRW execution environment as an effective way to handle the specificities of this kind of workflow. Experiments using current data of a Brazilian health insurance company were carried out to evaluate Long4Cloud and show performance gains in the execution of LRWs submitted to the proposed environment.

Enhancing QoE based on Machine Learning in Cloud infrastructure

The cloud computing paradigm has recently attracted many industries and academic attention. It provides network access on demand and offers applications, platforms, or access to a shared pool of hardware and software resources. For traditional deployment, the user reserves the most required resources. However, this system does not guarantee an optimal use of resources and is not profitable for users. The characteristic feature of the elasticity of the cloud Computing gives the Cloud the ability to perform an automatic up / down scale resources proportional to demand. However, classical deployment only considers the use of resources based on alarm, and does not consider the quality perceived by the end user. The aim of this paper is to set up a private IAAS Cloud infrastructure and complete it by supervision tools so we could optimize the management of the cloud elasticity based on users’ point of view or QoE. We have also used a Machine learning algorithm to predict the load charge of the physical machines of the cloud so that providers could manage efficiently their data centers.

Design and evaluation of a biologically-inspired cloud elasticity framework

The elasticity in cloud is essential to the effective management of computational resources as it enables readjustment at runtime to meet application demands. Over the years, researchers and practitioners have proposed many auto-scaling solutions using versatile techniques ranging from simple if-then-else based rules to sophisticated optimisation, control theory and machine learning based methods. However, despite an extensive range of existing elasticity research, the aim of implementing an efficient scaling technique that satisfies the actual demands is still a challenge to achieve. The existing methods suffer from issues like: (1) the lack of adaptability and static scaling behaviour whilst considering completely fixed approaches; (2) the burden of additional computational overhead, the inability to cope with the sudden changes in the workload behaviour and the preference of adaptability over reliability at runtime whilst considering the fully dynamic approaches; and (3) the lack of considering uncertainty aspects while designing auto-scaling solutions. In this paper, we aim to address these issues using a holistic biologically-inspired feedback switch controller. This method utilises multiple controllers and a switching mechanism, implemented using fuzzy system, that realises the selection of suitable controller at runtime. The fuzzy system also facilitates the design of qualitative elasticity rules. Furthermore, to improve the possibility of avoiding the oscillatory behaviour (a problem commonly associated with switch methodologies), this paper integrates a biologically-inspired computational model of action selection. Lastly, we identify seven different kinds of real workload patterns and utilise them to evaluate the performance of the proposed method against the state-of-the-art approaches. The obtained computational results demonstrate that the proposed method results in achieving better performance without incurring any additional cost in comparison to the state-of-the-art approaches.

On the Elasticity of Parallel Components in a Cloud of High Performance Computing Services

Cloud computing offers virtually unlimited set of resources and flexibility to allocate them through elasticity. But cloud limitations, such as the complexity of configuration and environment dynamicity, may jeopardizes the assurance of QoS requirements. HPC Shelf is a cloud of HPC services that employs a component-oriented architecture to describe hardware and software resources of parallel computing systems. We design a framework for HPC Shelf that employ cloud elasticity concepts for keeping the values of QoS metrics of parallel computing systems inside an acceptable range, enabling adaptations to fulfill the QoS contract restrictions. In our evaluation, using a linear algebra application, we show how HPC Shelf takes advantage of cloud elasticity to reinforce QoS requirements, rectifying assumptions from ill-defined QoS models.

Peer to Peer Cloud Providers Federation

The increasing demand of the cloud services and with the emergence of many could service providers, the need for cloud federation is inevitable. In cloud federation, many could services providers are collaborating with each other to improve the resources usage, cost, quality of service they provide. To form this federation a management framework is required to facilitate the communication between these providers. This framework can be centralized or distributed, distributed Peer to Peer cloud federation improve extensibility, scalability and fault-tolerant. On the other hand, it is challenging in term of complexity, security and manageability of the federation. In this paper we propose a fully distributed P2P Cloud Federation (PPCF) architecture. PPCF provide a way to connect heterogenous cloud providers to share resources and improve the cloud elasticity. The architecture combines different software technologies to fulfil the cloud federation requirements.