Reliability models for HPC applications and a Cloud economic model

With the enormous number of computing resources in HPC and Cloud systems, failures become a major concern. Therefore, failure behaviors such as reliability, failure rate, and mean time to failure need to be understood to manage such a large system efficiently. This dissertation makes three major contributions in HPC and Cloud studies. First, a reliability model with correlated failures in a k-node system for HPC applications is studied. This model is extended to improve accuracy by accounting for failure correlation. Marshall-Olkin Multivariate Weibull distribution is improved by excess life, conditional Weibull, to better estimate system reliability. Also, the univariate method is proposed for estimating Marshall-Olkin Multivariate Weibull parameters of a system composed of a large number of nodes. Then, failure rate, and mean time to failure are derived. The model is validated by using log data from Blue Gene/L system at LLNL. Results show that when failures of nodes in the system have correlation, the system becomes less reliable. Secondly, a reliability model of Cloud computing is proposed. The reliability model and mean time to failure and failure rate are estimated based on a system of k nodes and s virtual machines under four scenarios: 1) Hardware components fail independently, and software components fail independently; 2) software components fail independently, and hardware components are correlated in failure; 3) correlated software failure and independent hardware failure; and 4) dependent software and hardware failure. Results show that if the failure of the nodes and/or software in the system possesses a degree of dependency, the system becomes less reliable. Also, an increase in the number of computing components decreases the reliability of the system. Finally, an economic model for a Cloud service provider is proposed. This economic model aims at maximizing profit based on the right pricing and rightsizing in the Cloud data center. Total cost is a key element in the model and it is analyzed by considering the Total Cost of Ownership (TCO) of the Cloud

On the Economics of Cloud Markets

Cloud computing is a paradigm that has the potential to transform and revolutionalize the next generation IT industry by making software available to end-users as a service. A cloud, also commonly known as a cloud network, typically comprises of hardware (network of servers) and a collection of softwares that is made available to end-users in a pay-as-you-go manner. Multiple public cloud providers (ex., Amazon) co-existing in a cloud computing market provide similar services (software as a service) to its clients, both in terms of the nature of an application, as well as in quality of service (QoS) provision. The decision of whether a cloud hosts (or finds it profitable to host) a service in the long-term would depend jointly on the price it sets, the QoS guarantees it provides to its customers, and the satisfaction of the advertised guarantees. In this paper, we devise and analyze three inter-organizational economic models relevant to cloud networks. We formulate our problems as non co-operative price and QoS games between multiple cloud providers existing in a cloud market. We prove that a unique pure strategy Nash equilibrium (NE) exists in two of the three models. Our analysis paves the path for each cloud provider to 1) know what prices and QoS level to set for end-users of a given service type, such that the provider could exist in the cloud market, and 2) practically and dynamically provision appropriate capacity for satisfying advertised QoS guarantees.Comment: 7 pages, 2 figure

When Mobile Blockchain Meets Edge Computing

Blockchain, as the backbone technology of the current popular Bitcoin digital currency, has become a promising decentralized data management framework. Although blockchain has been widely adopted in many applications, e.g., finance, healthcare, and logistics, its application in mobile services is still limited. This is due to the fact that blockchain users need to solve preset proof-of-work puzzles to add new data, i.e., a block, to the blockchain. Solving the proof-of-work, however, consumes substantial resources in terms of CPU time and energy, which is not suitable for resource-limited mobile devices. To facilitate blockchain applications in future mobile Internet of Things systems, multiple access mobile edge computing appears to be an auspicious solution to solve the proof-of-work puzzles for mobile users. We first introduce a novel concept of edge computing for mobile blockchain. Then, we introduce an economic approach for edge computing resource management. Moreover, a prototype of mobile edge computing enabled blockchain systems is presented with experimental results to justify the proposed concept.Comment: Accepted by IEEE Communications Magazin

Notes on Cloud computing principles

This letter provides a review of fundamental distributed systems and economic Cloud computing principles. These principles are frequently deployed in their respective fields, but their inter-dependencies are often neglected. Given that Cloud Computing first and foremost is a new business model, a new model to sell computational resources, the understanding of these concepts is facilitated by treating them in unison. Here, we review some of the most important concepts and how they relate to each other

Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services

The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms