The cloud computing is an Internet-based computing emerging as a new architecture which aims to give reliable, customizable and QoS guaranteed dynamic environment for end-users. As multi-tenancy is one of the key features of cloud computing where service providers and users have scalable and economic benefits on same cloud platforms. In cloud computing environment the execution process requires resource management due to the processing capability is high to the resource ratio. The aim of the system is to handle resource management by executing scientific workflows. The locating and assigning of free resources is handled through the Cloud-based Workflow Scheduling Algorithm (CWSA) policy. The simulation results shows that the scheduling algorithm improves the performance of scientific workflows and helps in minimization of workflow completion time, tardiness, execution cost and use of idle resources of cloud using simulator Workflowsim.

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.

SUMMARYCloud computing is a technology that enables elastic, on-demand resource provisioning, allowing application developers to build highly scalable systems. Multi-tenancy, the hosting of multiple customers by a single application instance, leads to improved efficiency, improved scalability, and less costs. While these technologies make it possible to create many new applications, legacy applications can also benefit from the added flexibility and cost savings of cloud computing and multi-tenancy. In this article, we describe the steps required to migrate existing applications to a public cloud environment, and the steps required to add multi-tenancy to these applications. We present a generic approach and verify this approach by means of two case studies, a commercial medical communications software package mainly used within hospitals for nurse call systems and a schedule planner for managing medical appointments. Both case studies are subject to stringent security and performance constraints, which need to be taken into account during the migration. In our evaluation, we estimate the required investment costs and compare them to the long-term benefits of the migration.

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Abstract-The rise of cloud computing and its elastic, ondemand 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.