A Novel Admission Control Model in Cloud Computing

With the rapid development of Cloud computing technologies and wide adopt of Cloud services and applications, QoS provisioning in Clouds becomes an important research topic. In this paper, we propose an admission control mechanism for Cloud computing. In particular we consider the high volume of simultaneous requests for Cloud services and develop admission control for aggregated traffic flows to address this challenge. By employ network calculus, we determine effective bandwidth for aggregate flow, which is used for making admission control decision. In order to improve network resource allocation while achieving Cloud service QoS, we investigate the relationship between effective bandwidth and equivalent capacity. We have also conducted extensive experiments to evaluate performance of the proposed admission control mechanism

A secure and scalable communication framework for inter-cloud services

A lot of contemporary cloud computing platforms offer Infrastructure-as-a-Service provisioning model, which offers to deliver basic virtualized computing resources like storage, hardware, and networking as on-demand and dynamic services. However, a single cloud service provider does not have limitless resources to offer to its users, and increasingly users are demanding the features of extensibility and inter-operability with other cloud service providers. This has increased the complexity of the cloud ecosystem and resulted in the emergence of the concept of an Inter-Cloud environment where a cloud computing platform can use the infrastructure resources of other cloud computing platforms to offer a greater value and flexibility to its users. However, there are no common models or standards in existence that allows the users of the cloud service providers to provision even some basic services across multiple cloud service providers seamlessly, although admittedly it is not due to any inherent incompatibility or proprietary nature of the foundation technologies on which these cloud computing platforms are built. Therefore, there is a justified need of investigating models and frameworks which allow the users of the cloud computing technologies to benefit from the added values of the emerging Inter-Cloud environment. In this dissertation, we present a novel security model and protocols that aims to cover one of the most important gaps in a subsection of this field, that is, the problem domain of provisioning secure communication within the context of a multi-provider Inter-Cloud environment. Our model offers a secure communication framework that enables a user of multiple cloud service providers to provision a dynamic application-level secure virtual private network on top of the participating cloud service providers. We accomplish this by taking leverage of the scalability, robustness, and flexibility of peer-to-peer overlays and distributed hash tables, in addition to novel usage of applied cryptography techniques to design secure and efficient admission control and resource discovery protocols. The peer-to-peer approach helps us in eliminating the problems of manual configurations, key management, and peer churn that are encountered when setting up the secure communication channels dynamically, whereas the secure admission control and secure resource discovery protocols plug the security gaps that are commonly found in the peer-to-peer overlays. In addition to the design and architecture of our research contributions, we also present the details of a prototype implementation containing all of the elements of our research, as well as showcase our experimental results detailing the performance, scalability, and overheads of our approach, that have been carried out on actual (as opposed to simulated) multiple commercial and non-commercial cloud computing platforms. These results demonstrate that our architecture incurs minimal latency and throughput overheads for the Inter-Cloud VPN connections among the virtual machines of a service deployed on multiple cloud platforms, which are 5% and 10% respectively. Our results also show that our admission control scheme is approximately 82% more efficient and our secure resource discovery scheme is about 72% more efficient than a standard PKI-based (Public Key Infrastructure) scheme

A Game-Theoretic Approach for Runtime Capacity Allocation in MapReduce

Nowadays many companies have available large amounts of raw, unstructured data. Among Big Data enabling technologies, a central place is held by the MapReduce framework and, in particular, by its open source implementation, Apache Hadoop. For cost effectiveness considerations, a common approach entails sharing server clusters among multiple users. The underlying infrastructure should provide every user with a fair share of computational resources, ensuring that Service Level Agreements (SLAs) are met and avoiding wastes. In this paper we consider two mathematical programming problems that model the optimal allocation of computational resources in a Hadoop 2.x cluster with the aim to develop new capacity allocation techniques that guarantee better performance in shared data centers. Our goal is to get a substantial reduction of power consumption while respecting the deadlines stated in the SLAs and avoiding penalties associated with job rejections. The core of this approach is a distributed algorithm for runtime capacity allocation, based on Game Theory models and techniques, that mimics the MapReduce dynamics by means of interacting players, namely the central Resource Manager and Class Managers

Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

In the current cloud business environment, the cloud provider (CP) can provide a means for offering the required quality of service (QoS) for multiple classes of clients. We consider the cloud market where various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances can be provisioned and then leased to clients with QoS guarantees. Unlike existing works, we propose a novel Service Level Agreement (SLA) framework for cloud computing, in which a price control parameter is used to meet QoS demands for all classes in the market. The framework uses reinforcement learning (RL) to derive a VM hiring policy that can adapt to changes in the system to guarantee the QoS for all client classes. These changes include: service cost, system capacity, and the demand for service. In exhibiting solutions, when the CP leases more VMs to a class of clients, the QoS is degraded for other classes due to an inadequate number of VMs. However, our approach integrates computing resources adaptation with service admission control based on the RL model. To the best of our knowledge, this study is the first attempt that facilitates this integration to enhance the CP's profit and avoid SLA violation. Numerical analysis stresses the ability of our approach to avoid SLA violation while maximizing the CP’s profit under varying cloud environment conditions