REACT: A Solidarity-based Elastic Service Resource Reallocation Strategy for Multi-access Edge Computing

The Multi-access Edge Computing (MEC) paradigm promises to enhance network flexibility and scalability through resource virtualization. MEC allows telecom operators to fulfill the stringent and heterogeneous requirements of 5G applications via service deployment at the edge of the mobile network. However, current solutions to support MEC struggle to provide resource elasticity since MEC infrastructures have limited resources. The coexistence of many heterogeneous services on the distributed MEC infrastructure makes the resource scarcity problem even more challenging than it already is in traditional networks. Services need distinct resource provisioning patterns due to their diverse requirements, and we may not assume an extensive MEC infrastructure that can accommodate an arbitrary number of services. To address these aspects, we present REACT: a MEC-suppoRted sElfadaptive elAstiCiTy mechanism that leverages resource provisioning among different services running on a shared MEC environment. REACT adopts an adaptive and solidarity-based strategy to redistribute resources from over-provisioned services to under-provisioned services in MEC environments. REACT is an alternative strategy to avoid service migration due to resource scarcity. Real testbed results show that REACT outperforms Kubernetes’ elasticity strategy by accomplishing up to 18.88% more elasticity events, reducing service outages by up to 95.1%, reducing elasticity attempts by up to 95.36%, and reducing over-provisioned resources by up to 33.88%, 38.41%, and 73% for CPU cycles, RAM and bandwidth resources, respectively. Finally, REACT reduces response time by up to 15.5%

A manifesto for future generation cloud computing: research directions for the next decade

The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing

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