New data structures, models, and algorithms for real-time resource management

Real-time resource management is the core and critical task in real-time systems. This dissertation explores new data structures, models, and algorithms for real-time resource management. At first, novel data structures, i.e., a class of Testing Interval Trees (TITs), are proposed to help build efficient scheduling modules in real-time systems. With a general data structure, i.e., the TIT* tree, the average costs of the schedulability tests in a wide variety of real-time systems can be reduced. With the Testing Interval Tree for Vacancy analysis (TIT-V), the complexities of the schedulability tests in a class of parallel/distributed real-time systems can be effectively reduced from 0(m²nlogn) to 0(mlogn+mlogm), where m is the number of processors and n is the number of tasks. Similarly, with the Testing Interval Tree for Release time and Laxity analysis (TIT-RL), the complexity of the online admission control in a uni-processor based real-time system can be reduced from 0(n²) to 0(nlogn), where n is the number of tasks. The TIT-RL tree can also be applied to a class of parallel/distributed real-time systems. Therefore, the TIT trees are effective approaches to efficient real-time scheduling modules. Secondly, a new utility accrual model, i.e., UAM+, is established for the resource management in real-time distributed systems. UAM+ is constructed based on the timeliness of computation and communication. Most importantly, the interplay between computation and communication is captured and characterized in the model. Under UAM+, resource managers are guided towards maximizing system-wide utility by exploring the interplay between computation and communication. This is in sharp contrast to traditional approaches that attempt to meet the timing constraints on computation and communication separately. To validate the effectiveness of UAM+, a resource allocation algorithm called IAUASA is developed. Simulation results reveal that IAUASA is far superior to two other resource allocation algorithms that are developed according to traditional utility accrual model and traditional idea. Furthermore, an online algorithm called IDRSA is also developed under UAM+, and a Dynamic Deadline Adjustment (DDA) technique is incorporated into IDRSA algorithm to explore the interplay between computation and communication. The simulation results show that the performance of IDRSA is very promising, especially when the interplay between computation and communication is tight. Therefore, the new utility accrual model provides a more effective approach to the resource allocation in distributed real-time systems. Thirdly, a general task model, which adapts the concept of calculus curve from the network calculus domain, is established for those embedded real-time systems with random event/task arrivals. Under this model, a prediction technique based on history window and calculus curves is established, and it provides the foundation for dynamic voltage-frequency scaling in those embedded real-time systems. Based on this prediction technique, novel energy-efficient algorithms that can dynamically adjust the operating voltage-frequency according to the predicted workload are developed. These algorithms aim to reduce energy consumption while meeting hard deadlines. They can accommodate and well adapt to the variation between the predicted and the actual arrivals of tasks as well as the variation between the predicted and the actual execution times of tasks. Simulation results validate the effectiveness of these algorithms in energy saving

Adaptive Mid-term and Short-term Scheduling of Mixed-criticality Systems

A mixed-criticality real-time system is a real-time system having multiple tasks classified according to their criticality. Research on mixed-criticality systems started to provide an effective and cost efficient a priori verification process for safety critical systems. The higher the criticality of a task within a system and the more the system should guarantee the required level of service for it. However, such model poses new challenges with respect to scheduling and fault tolerance within real-time systems. Currently, mixed-criticality scheduling protocols severely degrade lower criticality tasks in case of resource shortage to provide the required level of service for the most critical ones. The actual research challenge in this field is to devise robust scheduling protocols to minimise the impact on less critical tasks. This dissertation introduces two approaches, one short-term and the other medium-term, to appropriately allocate computing resources to tasks within mixed-criticality systems both on uniprocessor and multiprocessor systems. The short-term strategy consists of a protocol named Lazy Bailout Protocol (LBP) to schedule mixed-criticality task sets on single core architectures. Scheduling decisions are made about tasks that are active in the ready queue and that have to be dispatched to the CPU. LBP minimises the service degradation for lower criticality tasks by providing to them a background execution during the system idle time. After, I refined LBP with variants that aim to further increase the service level provided for lower criticality tasks. However, this is achieved at an increased cost of either system offline analysis or complexity at runtime. The second approach, named Adaptive Tolerance-based Mixed-criticality Protocol (ATMP), decides at runtime which task has to be allocated to the active cores according to the available resources. ATMP permits to optimise the overall system utility by tuning the system workload in case of shortage of computing capacity at runtime. Unlike the majority of current mixed-criticality approaches, ATMP allows to smoothly degrade also higher criticality tasks to keep allocated lower criticality ones

Linking Research and Policy: Assessing a Framework for Organic Agricultural Support in Ireland

This paper links social science research and agricultural policy through an analysis of support for organic agriculture and food. Globally, sales of organic food have experienced 20% annual increases for the past two decades, and represent the fastest growing segment of the grocery market. Although consumer interest has increased, farmers are not keeping up with demand. This is partly due to a lack of political support provided to farmers in their transition from conventional to organic production. Support policies vary by country and in some nations, such as the US, vary by state/province. There have been few attempts to document the types of support currently in place. This research draws on an existing Framework tool to investigate regionally specific and relevant policy support available to organic farmers in Ireland. This exploratory study develops a case study of Ireland within the framework of ten key categories of organic agricultural support: leadership, policy, research, technical support, financial support, marketing and promotion, education and information, consumer issues, inter-agency activities, and future developments. Data from the Irish Department of Agriculture, Fisheries and Food, the Irish Agriculture and Food Development Authority (Teagasc), and other governmental and semi-governmental agencies provide the basis for an assessment of support in each category. Assessments are based on the number of activities, availability of information to farmers, and attention from governmental personnel for each of the ten categories. This policy framework is a valuable tool for farmers, researchers, state agencies, and citizen groups seeking to document existing types of organic agricultural support and discover policy areas which deserve more attention

Cloud Workload Allocation Approaches for Quality of Service Guarantee and Cybersecurity Risk Management

It has become a dominant trend in industry to adopt cloud computing --thanks to its unique advantages in flexibility, scalability, elasticity and cost efficiency -- for providing online cloud services over the Internet using large-scale data centers. In the meantime, the relentless increase in demand for affordable and high-quality cloud-based services, for individuals and businesses, has led to tremendously high power consumption and operating expense and thus has posed pressing challenges on cloud service providers in finding efficient resource allocation policies. Allowing several services or Virtual Machines (VMs) to commonly share the cloud\u27s infrastructure enables cloud providers to optimize resource usage, power consumption, and operating expense. However, servers sharing among users and VMs causes performance degradation and results in cybersecurity risks. Consequently, how to develop efficient and effective resource management policies to make the appropriate decisions to optimize the trade-offs among resource usage, service quality, and cybersecurity loss plays a vital role in the sustainable future of cloud computing. In this dissertation, we focus on cloud workload allocation problems for resource optimization subject to Quality of Service (QoS) guarantee and cybersecurity risk constraints. To facilitate our research, we first develop a cloud computing prototype that we utilize to empirically validate the performance of different proposed cloud resource management schemes under a close to practical, but also isolated and well-controlled, environment. We then focus our research on the resource management policies for real-time cloud services with QoS guarantee. Based on queuing model with reneging, we establish and formally prove a series of fundamental principles, between service timing characteristics and their resource demands, and based on which we develop several novel resource management algorithms that statically guarantee the QoS requirements for cloud users. We then study the problem of mitigating cybersecurity risk and loss in cloud data centers via cloud resource management. We employ game theory to model the VM-to-VM interdependent cybersecurity risks in cloud clusters. We then conduct a thorough analysis based on our game-theory-based model and develop several algorithms for cybersecurity risk management. Specifically, we start our cybersecurity research from a simple case with only two types of VMs and next extend it to a more general case with an arbitrary number of VM types. Our intensive numerical and experimental results show that our proposed algorithms can significantly outperform the existing methodologies for large-scale cloud data centers in terms of resource usage, cybersecurity loss, and computational effectiveness

Maximising microprocessor reliability through game theory and heuristics

PhD ThesisEmbedded Systems are becoming ever more pervasive in our society, with most routine daily tasks now involving their use in some form and the market predicted to be worth USD 220 billion, a rise of 300%, by 2018. Consumers expect more functionality with each design iteration, but for no detriment in perceived performance. These devices can range from simple low-cost chips to expensive and complex systems and are a major cost driver in the equipment design phase. For more than 35 years, designers have kept pace with Moore's Law, but as device size approaches the atomic limit, layouts are becoming so complicated that current scheduling techniques are also reaching their limit, meaning that more resource must be reserved to manage and deliver reliable operation. With the advent of many-core systems and further sources of unpredictability such as changeable power supplies and energy harvesting, this reservation of capability may become so large that systems will not be operating at their peak efficiency. These complex systems can be controlled through many techniques, with jobs scheduled either online prior to execution beginning or online at each time or event change. Increased processing power and job types means that current online scheduling methods that employ exhaustive search techniques will not be suitable to define schedules for such enigmatic task lists and that new techniques using statistic-based methods must be investigated to preserve Quality of Service. A new paradigm of scheduling through complex heuristics is one way to administer these next levels of processor effectively and allow the use of more simple devices in complex systems; thus reducing unit cost while retaining reliability a key goal identified by the International Technology Roadmap for Semi-conductors for Embedded Systems in Critical Environments. These changes would be beneficial in terms of cost reduction and system exibility within the next generation of device. This thesis investigates the use of heuristics and statistical methods in the operation of real-time systems, with the feasibility of Game Theory and Statistical Process Control for the successful supervision of high-load and critical jobs investigated. Heuristics are identified as an effective method of controlling complex real-time issues, with two-person non-cooperative games delivering Nash-optimal solutions where these exist. The simplified algorithms for creating and solving Game Theory events allow for its use within small embedded RISC devices and an increase in reliability for systems operating at the apex of their limits. Within this Thesis, Heuristic and Game Theoretic algorithms for a variety of real-time scenarios are postulated, investigated, refined and tested against existing schedule types; initially through MATLAB simulation before testing on an ARM Cortex M3 architecture functioning as a simplified automotive Electronic Control Unit.Doctoral Teaching Account from the EPSRC

MANAGING QUERY AND UPDATE TRANSACTIONS UNDER QUALITY CONTRACTS IN WEB-DATABASES

In modern Web-database systems, users typically perform read-only queries, whereas all write-only data updates are performed in the background, concurrently with queries.For most of these services to be successful and their users to be kept satisfied, two criteria need to be met: user requests must be answered in a timely fashion and must return fresh data. This is relatively easy when the system is lightly loaded and, as such, both queries and updates can be executed quickly. However, this goal becomes practically hard to achieve in real systems due to the high volumes of queries and updates, especially in periods of flash crowds. In this work, we argue it is beneficial to allow users to specify their preferences and let the system optimize towards satisfying user preferences, instead of simply improving the average case. We believe that this user-centric approach will empower the system to gracefully deal with a broader spectrum of workloads.Towards user-centric web-databases, we propose a Quality Contracts framework to help users express their preferences over multiple quality specifications. Moreover, we propose a suite of algorithms to effectively perform load balancing and scheduling for both queries and updates according to user preferences. We evaluate the proposed framework and algorithms through a simulation with real traces from disk accesses and from a stock information website. Finally, to increase the applicability of Quality Contracts enhanced Web-database systems, we propose an algorithm to help users adapt to the Web-database system behavior and maximize their query success ratio

System Support for Distributed Energy Management in Modular Operating Systems

This thesis proposes a novel approach for managing energy in modular operating systems. Our approach enables energy awareness if the resource-management subsystem is distributed among multiple operating-system modules. There are four key achievements: a model for modularization-aware energy management; the support for exposed and distributed energy accounting and allocation; the use of different energy-management interaction protocols; and, finally, the support virtualization of energy effects