Network bandwidth aware dynamic automated framework for Virtual Machine Live Migration in cloud environments

Live migration is a very important feature of virtualisation, a running VM can be seamlessly moved between different physical hosts. The source VM’s CPU state, storage, memory and network resources can be completely moved to a target host without disrupting the users or running applications. Live VM migration is an extremely powerful tool in many key scenarios such as load balancing, online maintenance, proactive fault tolerance and power management. There are four steps involved in the live VM migration, the setup stage, memory transfer stage, VM storage transfer stage and the network clean up stage. The most important part of live VM migration is transferring the main memory state of the VM from the source to the destination host which can consume a significant amount of network bandwidth in a short period of time. Modern cloud based data centres generate a significant amount of network traffic apart from VM live migration traffic. If VM migration occurs during a peak time, VM migration and user traffic will compete for network bandwidth, then the data centre’s network may not have enough resources to support both VM migration and demands of application users, which would create a bottleneck in the network. Therefore, this research presents a centralised, bandwidth aware, dynamic, and automated framework for live VM migration in Cloud environments. The proposed framework adopted a heuristic approach, and it provides guaranteed bandwidth for VM live migration by controlling user traffic on the network while scheduling live VM migration in an efficient manner. The framework consists with two main components, The Central Controller and the Local Controller. The Local Controller is responsible for collecting resources usage data from VMs and PMs however the Central Controller makes global management decisions. The Central Controller is based on four algorithms which are called a migration policy. The migration policy contains the following algorithms: the host overloaded detection, host underloaded detection, VM selection and VM placement algorithms which are proposed in this research. The proposed migration policy has been implemented in CloudSim and evaluated against two benchmark migration policies in CloudSim. Five evaluation metrics have been used in the simulation to evaluate the performance of the proposed migration policy. The results reveal that the proposed migration policy outperformed the two benchmark policies

Adaptive Alert Management for Balancing Optimal Performance among Distributed CSOCs using Reinforcement Learning

Large organizations typically have Cybersecurity Operations Centers (CSOCs) distributed at multiple locations that are independently managed, and they have their own cybersecurity analyst workforce. Under normal operating conditions, the CSOC locations are ideally staffed such that the alerts generated from the sensors in a work-shift are thoroughly investigated by the scheduled analysts in a timely manner. Unfortunately, when adverse events such as increase in alert arrival rates or alert investigation rates occur, alerts have to wait for a longer duration for analyst investigation, which poses a direct risk to organizations. Hence, our research objective is to mitigate the impact of the adverse events by dynamically and autonomously re-allocating alerts to other location(s) such that the performances of all the CSOC locations remain balanced. This is achieved through the development of a novel centralized adaptive decision support system whose task is to re-allocate alerts from the affected locations to other locations. This re-allocation decision is non-trivial because the following must be determined: (1) timing of a re-allocation decision, (2) number of alerts to be re-allocated, and (3) selection of the locations to which the alerts must be distributed. The centralized decision-maker (henceforth referred to as agent) continuously monitors and controls the level of operational effectiveness-LOE (a quantified performance metric) of all the locations. The agent's decision-making framework is based on the principles of stochastic dynamic programming and is solved using reinforcement learning (RL). In the experiments, the RL approach is compared with both rule-based and load balancing strategies. By simulating real-world scenarios, learning the best decisions for the agent, and applying the decisions on sample realizations of the CSOC's daily operation, the results show that the RL agent outperforms both approaches by generating (near-) optimal decisions that maintain a balanced LOE among the CSOC locations. Furthermore, the scalability experiments highlight the practicality of adapting the method to a large number of CSOC locations

Context-aware distribution of fog applications using deep reinforcement learning

Fog computing is an emerging paradigm that aims to meet the increasing computation demands arising from the billions of devices connected to the Internet. Offloading services of an application from the Cloud to the edge of the network can improve the overall latency of the application since it can process data closer to user devices. Diverse Fog nodes ranging from Wi-Fi routers to mini-clouds with varying resource capabilities makes it challenging to determine which services of an application need to be offloaded. In this paper, a context-aware mechanism for distributing applications across the Cloud and the Fog is proposed. The mechanism dynamically generates (re)deployment plans for the application to maximise the performance efficiency of the application by taking operational conditions, such as hardware utilisation and network state, and running costs into account. The mechanism relies on deep Q-networks to generate a distribution plan without prior knowledge of the available resources on the Fog node, the network condition, and the application. The feasibility of the proposed context-aware distribution mechanism is demonstrated on two use-cases, namely a face detection application and a location-based mobile game. The benefits are increased utility of dynamic distribution by 50% and 20% for the two use-cases respectively when compared to a static distribution approach used in existing research.Publisher PDFPeer reviewe

Investigating into Cloud Resource Management Mechanisms

Driven by the rapid growth of the demand for efficient and economical computational power, cloud computing has led the world into a new era. It delivers computing resources as services, whereby shared resources are provided to cloud users over the network in order to offer dynamic flexible resource provisioning for reliable and guaranteed services by using pay-as-you-use pricing model. Since multiple cloud users can request cloud resources simultaneously, cloud resource management mechanisms must operate in an efficient manner to satisfy demand of cloud users. Therefore, investigating cloud resource management mechanisms to achieve cloud resource efficiency is one of key elements that benefits both cloud providers and users. In this thesis, we present cloud resource management mechanisms for two different cloud infrastructures, i.e. virtual machine-based (VM-based) and application-based infrastructure. The VM-based infrastructure is an infrastructure that provides multi-tenancy for cloud users at VM-level, i.e. each cloud user directly controls their VMs in the cloud environment. The application-based infrastructure provides multi-tenancy at application level, in the other word, each cloud user directly control their applications in the cloud environment. For the VM-based infrastructure, we introduce two heuristics metrics to capture multi-dimensional characteristics of logical machines. By using a multivariate probabilistic model, we develop an algorithm to improve resource utilisation for the VM-based infrastructure. We then designed and implemented an application-based infrastructure called Elastic Application Container system (EAC system) to support multi-tenant cloud use. Based on the characteristics of the application-based and the VM-based infrastructure, we developed auto-scaling algorithms that can automatically scale cloud resources in the EAC system. In general, the cloud resource management mechanisms proposed in this thesis aims to investigate resource management mechanisms for cloud resource utilisation in the VM-based infrastructure and to provide suitable cloud resource provisioning mechanisms for the application-based infrastructure.Imperial Users Onl

Towards lightweight, low-latency network function virtualisation at the network edge

Communication networks are witnessing a dramatic growth in the number of connected mobile devices, sensors and the Internet of Everything (IoE) equipment, which have been estimated to exceed 50 billion by 2020, generating zettabytes of traffic each year. In addition, networks are stressed to serve the increased capabilities of the mobile devices (e.g., HD cameras) and to fulfil the users' desire for always-on, multimedia-oriented, and low-latency connectivity. To cope with these challenges, service providers are exploiting softwarised, cost-effective, and flexible service provisioning, known as Network Function Virtualisation (NFV). At the same time, future networks are aiming to push services to the edge of the network, to close physical proximity from the users, which has the potential to reduce end-to-end latency, while increasing the flexibility and agility of allocating resources. However, the heavy footprint of today's NFV platforms and their lack of dynamic, latency-optimal orchestration prevents them from being used at the edge of the network. In this thesis, the opportunities of bringing NFV to the network edge are identified. As a concrete solution, the thesis presents Glasgow Network Functions (GNF), a container-based NFV framework that allocates and dynamically orchestrates lightweight virtual network functions (vNFs) at the edge of the network, providing low-latency network services (e.g., security functions or content caches) to users. The thesis presents a powerful formalisation for the latency-optimal placement of edge vNFs and provides an exact solution using Integer Linear Programming, along with a placement scheduler that relies on Optimal Stopping Theory to efficiently re-calculate the placement following roaming users and temporal changes in latency characteristics. The results of this work demonstrate that GNF's real-world vNF examples can be created and hosted on a variety of hosting devices, including VMs from public clouds and low-cost edge devices typically found at the customer's premises. The results also show that GNF can carefully manage the placement of vNFs to provide low-latency guarantees, while minimising the number of vNF migrations required by the operators to keep the placement latency-optimal