SLA-Oriented Resource Provisioning for Cloud Computing: Challenges, Architecture, and Solutions

Cloud computing systems promise to offer subscription-oriented, enterprise-quality computing services to users worldwide. With the increased demand for delivering services to a large number of users, they need to offer differentiated services to users and meet their quality expectations. Existing resource management systems in data centers are yet to support Service Level Agreement (SLA)-oriented resource allocation, and thus need to be enhanced to realize cloud computing and utility computing. In addition, no work has been done to collectively incorporate customer-driven service management, computational risk management, and autonomic resource management into a market-based resource management system to target the rapidly changing enterprise requirements of Cloud computing. This paper presents vision, challenges, and architectural elements of SLA-oriented resource management. The proposed architecture supports integration of marketbased provisioning policies and virtualisation technologies for flexible allocation of resources to applications. The performance results obtained from our working prototype system shows the feasibility and effectiveness of SLA-based resource provisioning in Clouds.Comment: 10 pages, 7 figures, Conference Keynote Paper: 2011 IEEE International Conference on Cloud and Service Computing (CSC 2011, IEEE Press, USA), Hong Kong, China, December 12-14, 201

Federated and autonomic management of multimedia services

Over the years, the Internet has significantly evolved in size and complexity. Additionally, the modern multimedia services it offers have considerably more stringent Quality of Service (QoS) requirements than traditional static services. These factors contribute to the ever-increasing complexity and cost to manage the Internet and its services. In the dissertation, a novel network management architecture is proposed to overcome these problems. It supports QoS-guarantees of multimedia services across the Internet, by setting up end-to-end network federations. A network federation is defined as a persistent cross-organizational agreement that enables the cooperating networks to share capabilities. Additionally, the architecture incorporates aspects from autonomic network management to tackle the ever-growing management complexity of modern communications networks. Specifically, a hierarchical approach is presented, which guarantees scalable collaboration of huge amounts of self-governing autonomic management components

Incorporating prediction models in the SelfLet framework: a plugin approach

A complex pervasive system is typically composed of many cooperating \emph{nodes}, running on machines with different capabilities, and pervasively distributed across the environment. These systems pose several new challenges such as the need for the nodes to manage autonomously and dynamically in order to adapt to changes detected in the environment. To address the above issue, a number of autonomic frameworks has been proposed. These usually offer either predefined self-management policies or programmatic mechanisms for creating new policies at design time. From a more theoretical perspective, some works propose the adoption of prediction models as a way to anticipate the evolution of the system and to make timely decisions. In this context, our aim is to experiment with the integration of prediction models within a specific autonomic framework in order to assess the feasibility of such integration in a setting where the characteristics of dynamicity, decentralization, and cooperation among nodes are important. We extend an existing infrastructure called \emph{SelfLets} in order to make it ready to host various prediction models that can be dynamically plugged and unplugged in the various component nodes, thus enabling a wide range of predictions to be performed. Also, we show in a simple example how the system works when adopting a specific prediction model from the literature

A Self-adaptive Agent-based System for Cloud Platforms

Cloud computing is a model for enabling on-demand network access to a shared pool of computing resources, that can be dynamically allocated and released with minimal effort. However, this task can be complex in highly dynamic environments with various resources to allocate for an increasing number of different users requirements. In this work, we propose a Cloud architecture based on a multi-agent system exhibiting a self-adaptive behavior to address the dynamic resource allocation. This self-adaptive system follows a MAPE-K approach to reason and act, according to QoS, Cloud service information, and propagated run-time information, to detect QoS degradation and make better resource allocation decisions. We validate our proposed Cloud architecture by simulation. Results show that it can properly allocate resources to reduce energy consumption, while satisfying the users demanded QoS

Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges

Cloud computing is offering utility-oriented IT services to users worldwide. Based on a pay-as-you-go model, it enables hosting of pervasive applications from consumer, scientific, and business domains. However, data centers hosting Cloud applications consume huge amounts of energy, contributing to high operational costs and carbon footprints to the environment. Therefore, we need Green Cloud computing solutions that can not only save energy for the environment but also reduce operational costs. This paper presents vision, challenges, and architectural elements for energy-efficient management of Cloud computing environments. We focus on the development of dynamic resource provisioning and allocation algorithms that consider the synergy between various data center infrastructures (i.e., the hardware, power units, cooling and software), and holistically work to boost data center energy efficiency and performance. In particular, this paper proposes (a) architectural principles for energy-efficient management of Clouds; (b) energy-efficient resource allocation policies and scheduling algorithms considering quality-of-service expectations, and devices power usage characteristics; and (c) a novel software technology for energy-efficient management of Clouds. We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit. The results demonstrate that Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios.Comment: 12 pages, 5 figures,Proceedings of the 2010 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2010), Las Vegas, USA, July 12-15, 201

Performance-oriented Cloud Provisioning: Taxonomy and Survey

Cloud computing is being viewed as the technology of today and the future. Through this paradigm, the customers gain access to shared computing resources located in remote data centers that are hosted by cloud providers (CP). This technology allows for provisioning of various resources such as virtual machines (VM), physical machines, processors, memory, network, storage and software as per the needs of customers. Application providers (AP), who are customers of the CP, deploy applications on the cloud infrastructure and then these applications are used by the end-users. To meet the fluctuating application workload demands, dynamic provisioning is essential and this article provides a detailed literature survey of dynamic provisioning within cloud systems with focus on application performance. The well-known types of provisioning and the associated problems are clearly and pictorially explained and the provisioning terminology is clarified. A very detailed and general cloud provisioning classification is presented, which views provisioning from different perspectives, aiding in understanding the process inside-out. Cloud dynamic provisioning is explained by considering resources, stakeholders, techniques, technologies, algorithms, problems, goals and more.Comment: 14 pages, 3 figures, 3 table