Partitioned Scheduling of Multi-Modal Mixed-Criticality Real-Time Systems on Multiprocessor Platforms

Real-time systems are becoming increasingly complex. A modern car, for example, requires a multitude of control tasks, such as braking, active suspension, and collision avoidance. These tasks not only exhibit different degrees of safety criticality but also change their criticalities as the driving mode changes. For instance, the suspension task is a critical part of the stability of the car at high speed, but it is only a comfort feature at low speed. Therefore, it is crucial to ensure timing guarantees for the system with respect to the tasks’ criticalities, not only within each mode but also during mode changes. This paper presents a partitioned multi-processor scheduling scheme for multi-modal mixed-criticality real-time systems. Our scheme consists of a packing algorithm and a scheduling algorithm for each processor that take into account both mode changes and criticalities. The packing algorithm maximizes the schedulable utilization across modes using the sustained criticality of each task, which captures the overall criticality of the task across modes. The scheduling algorithm combines Rate-Monotonic scheduling with a mode transition enforcement mechanism that relies on the transitional zero-slack instants of tasks to control low-criticality tasks during mode changes, so as to preserve the schedulability of high-criticality tasks. We also present an implementation of our scheduler in the Linux operating system, as well as an experimental evaluation to illustrate its practicality. Our evaluation shows that our scheme can provide close to twice as much tolerance to overloads (ductility) compared to a mode-agnostic scheme

Climbing Up Cloud Nine: Performance Enhancement Techniques for Cloud Computing Environments

With the transformation of cloud computing technologies from an attractive trend to a business reality, the need is more pressing than ever for efficient cloud service management tools and techniques. As cloud technologies continue to mature, the service model, resource allocation methodologies, energy efficiency models and general service management schemes are not yet saturated. The burden of making this all tick perfectly falls on cloud providers. Surely, economy of scale revenues and leveraging existing infrastructure and giant workforce are there as positives, but it is far from straightforward operation from that point. Performance and service delivery will still depend on the providers’ algorithms and policies which affect all operational areas. With that in mind, this thesis tackles a set of the more critical challenges faced by cloud providers with the purpose of enhancing cloud service performance and saving on providers’ cost. This is done by exploring innovative resource allocation techniques and developing novel tools and methodologies in the context of cloud resource management, power efficiency, high availability and solution evaluation. Optimal and suboptimal solutions to the resource allocation problem in cloud data centers from both the computational and the network sides are proposed. Next, a deep dive into the energy efficiency challenge in cloud data centers is presented. Consolidation-based and non-consolidation-based solutions containing a novel dynamic virtual machine idleness prediction technique are proposed and evaluated. An investigation of the problem of simulating cloud environments follows. Available simulation solutions are comprehensively evaluated and a novel design framework for cloud simulators covering multiple variations of the problem is presented. Moreover, the challenge of evaluating cloud resource management solutions performance in terms of high availability is addressed. An extensive framework is introduced to design high availability-aware cloud simulators and a prominent cloud simulator (GreenCloud) is extended to implement it. Finally, real cloud application scenarios evaluation is demonstrated using the new tool. The primary argument made in this thesis is that the proposed resource allocation and simulation techniques can serve as basis for effective solutions that mitigate performance and cost challenges faced by cloud providers pertaining to resource utilization, energy efficiency, and client satisfaction

Cognitive-Aware Network Virtualization Hypervisor for Efficient Resource Provisioning in Software Defined Cloud Networks

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonIntegration of different technologies forms an integral part of modern network engineering and 5G technology deployment. Although Software Defined Networking (SDN) and Network Functions Virtualization (NFV) function well independently, integrating these two technologies present the cooperate advantages to service providers and service users. Operations of cloud computing technologies have been enhanced with the advent of SDN and NFV for efficient solutions deployment and infrastructure management in Software Defined Cloud Datacentre Networks (SDCDCN) where dynamic controllability is indispensable for elastic service provision. The provisioning of joint compute and network resources enabled by SDCN is essential to enforce reasonable Service Level Agreements (SLAs) stating the Quality of Service (QoS) while saving energy consumption and resource wastage. This thesis presents a Cognitive- Aware Network virtualization Hypervisor which was developed from merging the programmable dynamic network control attributes of SDN and the network slicing attributes of NFV to provision joint compute and network resources in SDCDCN for QoS fulfilment and energy efficiency. It focuses on the techniques for allocating Virtual Network Requests on physical hosts and switches considering SLA, QoS, and energy efficiency aspects. The thesis advances the state-of the-art with the following key contributions: A modelling and simulation environment for Software Defined Cloud Datacentre Networks abstracting functionalities and behaviours of virtual and physical network resources. The second is a novel dynamic overbooking algorithm for energy efficiency and SLA enforcement with the migration of virtual machines and network flows. Finally, a performance-aware intelligent overbooking for predicting network resource usage and performance for the next defined time interval considering multiple performance indexes

Multi-agent Contracting and Reconfiguration in Competitive Environments using Acquaintance Models

Cooperation of agents in competitive environments is more complicated than in collaborative environments. Both replanning and reconfiguration play a crucial role in cooperation, and introduce a means for implementating a system flexibility. The concepts of commitments, decommitments with penalties and subcontracting may facilitate effective reconfiguration and replanning. Agents in competitive environments are fully autonomous and selfinterested. Therefore the setting of penalties and profit computation cannot be provided centrally. Both the costs and the gain differ from agent to agent with respect to contracts already agreed and resources load. This paper proposes an acquaintance model for contracting in competitive environments and introduces possibilities of reconfigurating in competitive environments as a means of decommitment optimization with respect to resources load and profit maximization. The presented algorithm for contract price setting does not use any centralized knowledge and provides results corresponding to a realistic environment. A simple customerprovider scenario proves this algorithm in competitive contracting.

PROV-TE: A Provenance-Driven Diagnostic Framework for Task Eviction in Data Centers

Cloud Computing allows users to control substantial computing power for complex data processing, generating huge and complex data. However, the virtual resources requested by users are rarely utilized to their full capacities. To mitigate this, providers often perform over-commitment to maximize profit, which can result in node overloading and consequent task eviction. This paper presents a novel framework that mines the huge and growing historical usage data generated by Cloud data centers to identify the causes of overloads. Provenance modelling is applied to add contextual meaning to the data, and the PROV-TE diagnostic framework provides algorithms to efficiently identify the causality of task eviction. Using simulation to reflect real world scenarios, our results demonstrate a precision and recall of the diagnostic algorithms of 83% and 90% respectively. This demonstrates a high level of accuracy of the identification of causes

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