An Approach of SLA Violation Prediction and QoS Optimization using Regression Machine Learning Techniques

Along with the acceptance of Service-Oriented Architecture (SOA) as a promising style of software design, the role that Quality of Service (QoS) plays in the success of SOA-based software systems has become much more significant than ever before. When QoS is documented as a Service-Level Agreement (SLA), it specifies the commitment between a service provider and a client, as well as monetary penalties in case of any SLA violations. To avoid and reduce the situations that may cause SLA violations, service providers need tools to intuitively analyze if their service design provokes SLA violations and to automatically guide them preventing SLA violations. Due to the dynamic nature of service interaction during the operation of SOA-based software systems, the avoidance of SLA violations requires prompt detection of potential violations before prevention takes place at real-time. To overcome the low latency time in practice, this thesis research develops an approach of using Machine Learning techniques to not only predict SLA violations but also prevent them by means of optimization. This research discusses the algorithm and framework, along with the results of the experiments, which will help to examine its usefulness for service providers working on the construction and refinement of services

QoS Implementation with Triple-Metric Based Active Queue Management for Military Networks

For supporting Quality of Service (QoS) in a military network, applications of triple-metric priority of performance, importance, and urgency as well as autonomous and lightweight implementation are required. In the previous study, we analyzed a Korean military network’s QoS implementation in the perspective of the triple-metric, and presented some improvements in the simplification of service classes of Differentiated Services (DiffServ). To extend the simplified DiffServ in the previous research, this paper proposes Active Queue Management (AQM) algorithms to process the traffic of each service class differently based on importance and urgency, and shows the feasibility through some experiments

Energy and performance-optimized scheduling of tasks in distributed cloud and edge computing systems

Infrastructure resources in distributed cloud data centers (CDCs) are shared by heterogeneous applications in a high-performance and cost-effective way. Edge computing has emerged as a new paradigm to provide access to computing capacities in end devices. Yet it suffers from such problems as load imbalance, long scheduling time, and limited power of its edge nodes. Therefore, intelligent task scheduling in CDCs and edge nodes is critically important to construct energy-efficient cloud and edge computing systems. Current approaches cannot smartly minimize the total cost of CDCs, maximize their profit and improve quality of service (QoS) of tasks because of aperiodic arrival and heterogeneity of tasks. This dissertation proposes a class of energy and performance-optimized scheduling algorithms built on top of several intelligent optimization algorithms. This dissertation includes two parts, including background work, i.e., Chapters 3–6, and new contributions, i.e., Chapters 7–11. 1) Background work of this dissertation. Chapter 3 proposes a spatial task scheduling and resource optimization method to minimize the total cost of CDCs where bandwidth prices of Internet service providers, power grid prices, and renewable energy all vary with locations. Chapter 4 presents a geography-aware task scheduling approach by considering spatial variations in CDCs to maximize the profit of their providers by intelligently scheduling tasks. Chapter 5 presents a spatio-temporal task scheduling algorithm to minimize energy cost by scheduling heterogeneous tasks among CDCs while meeting their delay constraints. Chapter 6 gives a temporal scheduling algorithm considering temporal variations of revenue, electricity prices, green energy and prices of public clouds. 2) Contributions of this dissertation. Chapter 7 proposes a multi-objective optimization method for CDCs to maximize their profit, and minimize the average loss possibility of tasks by determining task allocation among Internet service providers, and task service rates of each CDC. A simulated annealing-based bi-objective differential evolution algorithm is proposed to obtain an approximate Pareto optimal set. A knee solution is selected to schedule tasks in a high-profit and high-quality-of-service way. Chapter 8 formulates a bi-objective constrained optimization problem, and designs a novel optimization method to cope with energy cost reduction and QoS improvement. It jointly minimizes both energy cost of CDCs, and average response time of all tasks by intelligently allocating tasks among CDCs and changing task service rate of each CDC. Chapter 9 formulates a constrained bi-objective optimization problem for joint optimization of revenue and energy cost of CDCs. It is solved with an improved multi-objective evolutionary algorithm based on decomposition. It determines a high-quality trade-off between revenue maximization and energy cost minimization by considering CDCs’ spatial differences in energy cost while meeting tasks’ delay constraints. Chapter 10 proposes a simulated annealing-based bees algorithm to find a close-to-optimal solution. Then, a fine-grained spatial task scheduling algorithm is designed to minimize energy cost of CDCs by allocating tasks among multiple green clouds, and specifies running speeds of their servers. Chapter 11 proposes a profit-maximized collaborative computation offloading and resource allocation algorithm to maximize the profit of systems and guarantee that response time limits of tasks are met in cloud-edge computing systems. A single-objective constrained optimization problem is solved by a proposed simulated annealing-based migrating birds optimization. This dissertation evaluates these algorithms, models and software with real-life data and proves that they improve scheduling precision and cost-effectiveness of distributed cloud and edge computing systems