Cost-effective feature placement of customizable multi-tenant applications in the cloud

Cloud computing technologies can be used to more flexibly provision application resources. By exploiting multi-tenancy, instances can be shared between users, lowering the cost of providing applications. A weakness of current cloud offerings however, is the difficulty of creating customizable applications that retain these advantages. In this article, we define a feature-based cloud resource management model, making use of Software Product Line Engineering techniques, where applications are composed of feature instances using a service-oriented architecture. We focus on how resources can be allocated in a cost-effective way within this model, a problem which we refer to as the feature placement problem. A formal description of this problem, that can be used to allocate resources in a cost-effective way, is provided. We take both the cost of failure to place features, and the cost of using servers into account, making it possible to take energy costs or the cost of public cloud infrastructure into consideration during the placement calculation. Four algorithms that can be used to solve the feature placement problem are defined. We evaluate the algorithm solutions, comparing them with the optimal solution determined using an integer linear problem solver, and evaluating the execution times of the algorithms, making use of both generated inputs and a use case based on three applications. We show that, using our approach a higher degree of multi-tenancy can be achieved, and that for the considered scenarios, taking the relationships between features into account and using application-oriented placement performs 25-40 % better than a purely feature-oriented placement

Optimal deployment of components of cloud-hosted application for guaranteeing multitenancy isolation

One of the challenges of deploying multitenant cloud-hosted services that are designed to use (or be integrated with) several components is how to implement the required degree of isolation between the components when there is a change in the workload. Achieving the highest degree of isolation implies deploying a component exclusively for one tenant; which leads to high resource consumption and running cost per component. A low degree of isolation allows sharing of resources which could possibly reduce cost, but with known limitations of performance and security interference. This paper presents a model-based algorithm together with four variants of a metaheuristic that can be used with it, to provide near-optimal solutions for deploying components of a cloud-hosted application in a way that guarantees multitenancy isolation. When the workload changes, the model based algorithm solves an open multiclass QN model to determine the average number of requests that can access the components and then uses a metaheuristic to provide near-optimal solutions for deploying the components. Performance evaluation showed that the obtained solutions had low variability and percent deviation when compared to the reference/optimal solution. We also provide recommendations and best practice guidelines for deploying components in a way that guarantees the required degree of isolation

Proposing Optimus Scheduler Algorithm for Virtual Machine Placement Within a Data Center

With the evolution of the Internet, we are witnessing the birth of an increasing number of applications that rely on the network; what was previously executed on the user's computers as stand-alone programs has been redesigned to be executed on servers with permanent connections to the Internet, making the information available from any device that has network access. Instead of buying a copy of a program, users can now pay to obtain access to it through the network, which is one of the models of cloud computing, Software as a Service (SaaS). The continuous growth of Internet bandwidth has also given rise to new multimedia applications, such as social networks and video over the Internet; and to complete this new paradigm, mobile platforms provide the ubiquity of information that allows people to stay connected. Service providers may own servers and data centers or, alternatively, may contract infrastructure providers that use economies of scale to offer access to servers as a service in the cloud computing model, i.e., Infrastructure as a Service (IaaS). As users become more dependent on cloud services and mobile platforms increase the ubiquity of the cloud, the quality of service becomes increasingly important. A fundamental metric that defines the quality of service is the delay of the information as it travels between the user computers and the servers, and between the servers themselves. Along with the quality of service and the costs, the energy consumption and the CO2 emissions are fundamental considerations in regard to planning cloud computing networks. In this research work, an Optimus Scheduler algorithm to be proposed for Add, Remove or Resize an application by using Tabu Search Algorithm

Placement of software-as-a-service components in cloud computing environment

Cloud computing is an emerging paradigm in which information technology resources are provided over the internet as a service to users. Software-as-a-Service (SaaS) is offered by cloud, which can be delivered in a composite form, consisting of a set of application and data components, that works together to deliver higher-level functional software. SaaS components are placed on top of the virtual machines (VMs) in cloud computing environment, which are deployed on physical or storage servers. The SaaS placement is an NP-hard problem. The research problem refers to how a SaaS component is placed on virtual machine to optimize its performance while satisfying the SaaS resource and response time constraints with service level agreement (SLA) constraints. This thesis presents SaaS placement problem as an optimization problem, to maximize the profit of the SaaS providers. Intractability nature of the SaaS placement problem leads to the use of genetic algorithms to obtain sub-optimal solution for SaaS component placement on virtual machine. A suitable codification scheme for SaaS component placement has been proposed for the genetic algorithm. The performance of proposed genetic algorithm has been compared with first-fit randomized algorithm (First-fit RA) by varying number of virtual machines and SaaS components by using in-house simulator. Performance of proposed genetic algorithm has been found to be better in comparison to First-fit RA

Cicada: Predictive Guarantees for Cloud Network Bandwidth

In cloud-computing systems, network-bandwidth guarantees have been shown to improve predictability of application performance and cost. Most previous work on cloud-bandwidth guarantees has assumed that cloud tenants know what bandwidth guarantees they want. However, application bandwidth demands can be complex and time-varying, and many tenants might lack sufficient information to request a bandwidth guarantee that is well-matched to their needs. A tenant's lack of accurate knowledge about its future bandwidth demands can lead to over-provisioning (and thus reduced cost-efficiency) or under-provisioning (and thus poor user experience in latency-sensitive user-facing applications). We analyze traffic traces gathered over six months from an HP Cloud Services datacenter, finding that application bandwidth consumption is both time-varying and spatially inhomogeneous. This variability makes it hard to predict requirements. To solve this problem, we develop a prediction algorithm usable by a cloud provider to suggest an appropriate bandwidth guarantee to a tenant. The key idea in the prediction algorithm is to treat a set of previously observed traffic matrices as "experts" and learn online the best weighted linear combination of these experts to make its prediction. With tenant VM placement using these predictive guarantees, we find that the inter-rack network utilization in certain datacenter topologies can be more than doubled

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