Achieving High Reliability and Efficiency in Maintaining Large-Scale Storage Systems through Optimal Resource Provisioning and Data Placement

With the explosive increase in the amount of data being generated by various applications, large-scale distributed and parallel storage systems have become common data storage solutions and been widely deployed and utilized in both industry and academia. While these high performance storage systems significantly accelerate the data storage and retrieval, they also bring some critical issues in system maintenance and management. In this dissertation, I propose three methodologies to address three of these critical issues. First, I develop an optimal resource management and spare provisioning model to minimize the impact brought by component failures and ensure a highly operational experience in maintaining large-scale storage systems. Second, in order to cost-effectively integrate solid-state drives (SSD) into large-scale storage systems, I design a holistic algorithm which can adaptively predict the popularity of data objects by leveraging temporal locality in their access pattern and adjust their placement among solid-state drives and regular hard disk drives so that the data access throughput as well as the storage space efficiency of the large-scale heterogeneous storage systems can be improved. Finally, I propose a new checkpoint placement optimization model which can maximize the computation efficiency of large-scale scientific applications while guarantee the endurance requirements of the SSD-based burst buffer in high performance hierarchical storage systems. All these models and algorithms are validated through extensive evaluation using data collected from deployed large-scale storage systems and the evaluation results demonstrate our models and algorithms can significantly improve the reliability and efficiency of large-scale distributed and parallel storage systems

Using a Novel Hierarchical Coloured Petri Net to Model and Optimise Fleet Spare Inventory, Cannibalisation and Preventive Maintenance

Spare part availability is crucial to restoring inoperative platforms to the working state. Platforms failing during operation undergo corrective maintenance to replace failed components with spares. To reduce the frequency of this unplanned, corrective maintenance, platforms are inspected periodically and degraded components preventively replaced. Maintenance delays occur when spares are unavailable but cannibalisation can reduce these delays by allowing working components to be removed from inoperative platforms and used to restore other inoperative platforms. Fleets can be deployed across multiple bases that are served by one or more depots. Failed components that cannot be repaired at a base are sent to a depot for repair, along with associated requests for spares, which are satisfied by depot inventories.The management of fleet corrective and preventive maintenance, cannibalisation, spare inventories, provision of spares to bases and depots, and response of the depot to spare requests is a complex problem for fleet maintenance managers and critical to ensuring acceptable fleet performance. This paper presents a novel hierarchical coloured Petri net (HCPN) model of a fleet spare inventory system, which accounts for these issues alongside fleet deployment and mission-oriented operation. The application of the model is demonstrated using case studies of two example fleets

Framework for Evaluation of Strategies for Pooling of Repairable Spare Parts

Background: The ability to quickly provide parts for the supply of advanced technical systems in equipment-intensive industries (such as airlines and nuclear power plants) is critical to the systems overall performance. In order to maintain a targeted system availability large quantities of spare parts are often required which in turn results in excessive inventory costs. Seeing as inventory systems often account for a large proportion of a business‟ costs a tough issue faced by companies in these industries is how to reduce the total inventory cost without having a negative impact on the system availability. An approach that may successfully deal with such a problem is pooling. Pooling refers to an arrangement in which multiple owners of the same type of technical systems cooperate by sharing their inventories. Purpose: The theoretical purpose of the thesis is to emphasize different pooling strategies and to identify and assess the characteristics of the strategies. The practical purpose of the thesis is to develop a robust method that facilitates a fair comparison of considered strategies. The objective is thus to develop a generic model that evaluates soft values (here, referred to as soft aspects) for each strategy, and also, to put the soft aspects in relation to the annual cost of a strategy in a final model. iv Methodology: The initial phase of the thesis was dedicated to a desk study review of current literature within the field of study. Recently published scientific articles, papers authored by consultants at Systecon, and literature used in courses at the Faculty of Engineering at Lund University lay the basis for the theoretical framework. The framework developed is derived from discussions with the supervisors in connection with interviews carried out with; relevant Systecon customers and company representatives at two trade fairs, Offshore Wind 2009 and Nordic Rail 2009. Conclusion: This thesis presents a framework for evaluation of strategies (stand alone, ad hoc cooperation, cooperative pooling, and commercial pooling) for pooling of repairable spare parts. Characteristics of all strategies are emphasized and assessed. From the characteristics, which are provided in Table 5.3, a model to evaluate soft values of each strategy is derived. The model, named evaluation of soft values, is provided in Table 5.4 and Table 5.5. Also, a methodical approach to derive a final strategy is provided in section 5.7. To make sure that a decision-maker is well aware of how the model should be applied, a fictitious case study is build up in where every step of the decision making process is thoroughly described. Furthermore, in the case study a final model that facilitates the derivation of a best strategy is presented. By means of a specified weighting coefficient and properly chosen set of scales, the final model provides with a final strategy. The outcome of the final model is based on the outcomes of the cost models and the outcomes of the evaluation of soft values model

Prognostics-Based Two-Operator Competition for Maintenance and Service Part Logistics

Prognostics and timely maintenance of components are critical to the continuing operation of a system. By implementing prognostics, it is possible for the operator to maintain the system in the right place at the right time. However, the complexity in the real world makes near-zero downtime difficult to achieve partly because of a possible shortage of required service parts. This is realistic and quite important in maintenance practice. To coordinate with a prognostics-based maintenance schedule, the operator must decide when to order service parts and how to compete with other operators who also need the same parts. This research addresses a joint decision-making approach that assists two operators in making proactive maintenance decisions and strategically competing for a service part that both operators rely on for their individual operations. To this end, a maintenance policy involving competition in service part procurement is developed based on the Stackelberg game-theoretic model. Variations of the policy are formulated for three different scenarios and solved via either backward induction or genetic algorithm methods. Unlike the first two scenarios, the possibility for either of the operators being the leader in such competitions is considered in the third scenario. A numerical study on wind turbine operation is provided to demonstrate the use of the joint decision-making approach in maintenance and service part logistics

Trading reliability targets within a supply chain using Shapley's value

The development of complex systems involves a multi-tier supply chain, with each organisation allocated a reliability target for their sub-system or component part apportioned from system requirements. Agreements about targets are made early in the system lifecycle when considerable uncertainty exists about the design detail and potential failure modes. Hence resources required to achieve reliability are unpredictable. Some types of contracts provide incentives for organisations to negotiate targets so that system reliability requirements are met, but at minimum cost to the supply chain. This paper proposes a mechanism for deriving a fair price for trading reliability targets between suppliers using information gained about potential failure modes through development and the costs of activities required to generate such information. The approach is based upon Shapley's value and is illustrated through examples for a particular reliability growth model, and associated empirical cost model, developed for problems motivated by the aerospace industry. The paper aims to demonstrate the feasibility of the method and discuss how it could be extended to other reliability allocation models

OR in Spare Parts Management:A Review

Abstract Spare parts are held to reduce the consequences of equipment downtime, playing an important role in achieving the desired equipment availability at a minimum economic cost. In this paper, a framework for OR in spare parts management is presented, based on the product lifecycle process and including the objectives, main tasks, and OR disciplines for supporting spare parts management. Based on the framework, a systematic literature review of OR in spare parts management is undertaken, and then a comprehensive investigation of each OR discipline's contribution is given. The gap between theory and practice of spare parts management is investigated from the perspective of software integration, maintenance management information systems and adoption of new OR methods in software. Finally, as the result of this review, an extended version of the framework is proposed and a set of future research directions is discussed

ERA: A Framework for Economic Resource Allocation for the Cloud

Cloud computing has reached significant maturity from a systems perspective, but currently deployed solutions rely on rather basic economics mechanisms that yield suboptimal allocation of the costly hardware resources. In this paper we present Economic Resource Allocation (ERA), a complete framework for scheduling and pricing cloud resources, aimed at increasing the efficiency of cloud resources usage by allocating resources according to economic principles. The ERA architecture carefully abstracts the underlying cloud infrastructure, enabling the development of scheduling and pricing algorithms independently of the concrete lower-level cloud infrastructure and independently of its concerns. Specifically, ERA is designed as a flexible layer that can sit on top of any cloud system and interfaces with both the cloud resource manager and with the users who reserve resources to run their jobs. The jobs are scheduled based on prices that are dynamically calculated according to the predicted demand. Additionally, ERA provides a key internal API to pluggable algorithmic modules that include scheduling, pricing and demand prediction. We provide a proof-of-concept software and demonstrate the effectiveness of the architecture by testing ERA over both public and private cloud systems -- Azure Batch of Microsoft and Hadoop/YARN. A broader intent of our work is to foster collaborations between economics and system communities. To that end, we have developed a simulation platform via which economics and system experts can test their algorithmic implementations

Spare parts nonavailability: the identification of impediments to spares acquisition

The primary intent of this research effort is to provide an identification and analysis of impediments to the acquisition of spare parts. The focus of the research was in the area of Army rotary wing aviation spare parts. The researcher delved into both pre-nonavailability and post nonavailability issues and sought to establish responsibility for their resolution. It looked at such factors as those general factors shaping the procurement environment, and information gathered through archival research, interviews, questionnaires and selected component case analysis. It was apparent from the research that a myriad of causes of spares nonavailability exist. Some of these are controllable and some are outside the realm of reasonable control by those in the acquisition community. It also discussed the aggressive, iterative use of risk management to apply limited resources to those areas with demand the most attention due to their relative program impact should difficulties in sustainability be encountered.http://archive.org/details/sparepartsnonava00wynnCaptain, United States ArmyApproved for public release; distribution is unlimited