MLF-DRS: A Multi-level Fair Resource Allocation Algorithm in Heterogeneous Cloud Computing Systems

Cloud computing is a novel paradigm which provides on demand, scalable and pay-as-you-use computing resources in a virtualized form. With cloud computing, users are able to access large pools of resources anywhere without any limitation. In order to use the provided facilities by the cloud in an efficient way, the management of resources is an undeniable fact that should be considered in different aspects. Among all those aspects, resource allocation has received much attentions. Given the fact that the cloud is heterogeneous, the allocation of resources has to become more sophisticated. As a first promising work to deal with that problem, Dominant Resource Fairness (DRF) has been proposed which takes into account dominant shares of users. Although DRF has a sort of desirable fairness properties, it has some limitations that have already been identified in the literature. Unfortunately, DRF and its recent developments are not intuitively fair with respect to various resource demands. In this paper, we propose a Multi-level Fair Dominant Resource Scheduling (MLF-DRS) algorithm as a new allocation model inspired by Max-Min fairness and proportionality. Unlike other works that they equalize dominant shares of different resource types which leads to starvation in the maximization of allocation for some users, our algorithm guarantees that each user receives the resources they desire for based on dominant shares. As can be deducted from the mathematical proofs, MLF-DRS provides a full utilization of resources and meets some of the desirable fair allocation properties and it is applicable to be used in a naïve extension form in the presence of multiple servers as wel

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