Intelligent Load Balancing in Cloud Computer Systems

Cloud computing is an established technology allowing users to share resources on a large scale, never before seen in IT history. A cloud system connects multiple individual servers in order to process related tasks in several environments at the same time. Clouds are typically more cost-effective than single computers of comparable computing performance. The sheer physical size of the system itself means that thousands of machines may be involved. The focus of this research was to design a strategy to dynamically allocate tasks without overloading Cloud nodes which would result in system stability being maintained at minimum cost. This research has added the following new contributions to the state of knowledge: (i) a novel taxonomy and categorisation of three classes of schedulers, namely OS-level, Cluster and Big Data, which highlight their unique evolution and underline their different objectives; (ii) an abstract model of cloud resources utilisation is specified, including multiple types of resources and consideration of task migration costs; (iii) a virtual machine live migration was experimented with in order to create a formula which estimates the network traffic generated by this process; (iv) a high-fidelity Cloud workload simulator, based on a month-long workload traces from Google's computing cells, was created; (v) two possible approaches to resource management were proposed and examined in the practical part of the manuscript: the centralised metaheuristic load balancer and the decentralised agent-based system. The project involved extensive experiments run on the University of Westminster HPC cluster, and the promising results are presented together with detailed discussions and a conclusion

Optimizing task allocation for edge compute micro-clusters

There are over 30 billion devices at the network edge. This is largely driven by the unprecedented growth of the Internet-of-Things (IoT) and 5G technologies. These devices are being used in various applications and technologies, including but not limited to smart city systems, innovative agriculture management systems, and intelligent home systems. Deployment issues like networking and privacy problems dictate that computing should occur close to the data source at or near the network edge. Edge and fog computing are recent decentralised computing paradigms proposed to augment cloud services by extending computing and storage capabilities to the network’s edge to enable executing computational workloads locally. The benefits can help to solve issues such as reducing the strain on networking backhaul, improving network latency and enhancing application responsiveness. Many edge and fog computing deployment solutions and infrastructures are being employed to deliver cloud resources and services at the edge of the network — for example, cloudless and mobile edge computing. This thesis focuses on edge micro-cluster platforms for edge computing. Edge computing micro-cluster platforms are small, compact, and decentralised groups of interconnected computing resources located close to the edge of a network. These micro-clusters can typically comprise a variety of heterogeneous but resource-constrained computing resources, such as small compute nodes like Single Board Computers (SBCs), storage devices, and networking equipment deployed in local area networks such as smart home management. The goal of edge computing micro-clusters is to bring computation and data storage closer to IoT devices and sensors to improve the performance and reliability of distributed systems. Resource management and workload allocation represent a substantial challenge for such resource-limited and heterogeneous micro-clusters because of diversity in system architecture. Therefore, task allocation and workload management are complex problems in such micro-clusters. This thesis investigates the feasibility of edge micro-cluster platforms for edge computation. Specifically, the thesis examines the performance of micro-clusters to execute IoT applications. Furthermore, the thesis involves the evaluation of various optimisation techniques for task allocation and workload management in edge compute micro-cluster platforms. This thesis involves the application of various optimisation techniques, including simple heuristics-based optimisations, mathematical-based optimisation and metaheuristic optimisation techniques, to optimise task allocation problems in reconfigurable edge computing micro-clusters. The implementation and performance evaluations take place in a configured edge realistic environment using a constructed micro-cluster system comprised of a group of heterogeneous computing nodes and utilising a set of edge-relevant applications benchmark. The research overall characterises and demonstrates a feasible use case for micro-cluster platforms for edge computing environments and provides insight into the performance of various task allocation optimisation techniques for such micro-cluster systems

A Descriptive Literature Review and Classification of Cloud Computing Research

We present a descriptive literature review and classification scheme for cloud computing research. This includes 205 refereed journal articles published since the inception of cloud computing research. The articles are classified based on a scheme that consists of four main categories: technological issues, business issues, domains and applications, and conceptualising cloud computing. The results show that although current research is still skewed towards technological issues, new research themes regarding social and organisational implications are emerging. This review provides a reference source and classification scheme for IS researchers interested in cloud computing, and to indicate under-researched areas as well as future directions

Probabilistic QoS-aware Placement of VNF chains at the Edge

Deploying IoT-enabled Virtual Network Function (VNF) chains to Cloud-Edge infrastructures requires determining a placement for each VNF that satisfies all set deployment requirements as well as a software-defined routing of traffic flows between consecutive functions that meets all set communication requirements. In this article, we present a declarative solution, EdgeUsher, to the problem of how to best place VNF chains to Cloud-Edge infrastructures. EdgeUsher can determine all eligible placements for a set of VNF chains to a Cloud-Edge infrastructure so to satisfy all of their hardware, IoT, security, bandwidth, and latency requirements. It exploits probability distributions to model the dynamic variations in the available Cloud-Edge infrastructure, and to assess output eligible placements against those variations

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

Contextually and identity aware 5G services

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThe fifth generation (5G) mobile networks aim to be ten times faster than the existing 4G connection, whilst providing low latency, and flexibility. Hence, various alterations are planned to the existing network infrastructure to be able to reach the 5G expected performance levels. The main technologies that were used, to ensure high performance, flexible network, and efficient resource allocation, are Software Defined Network and Network Function Virtualization. As these technologies are replacing the device-based architecture with, a service-based architecture. This thesis provides a design of location database interactive web interface and interactive mobile application. The implementation of real time video streaming location server, the streaming system's performance parameters demonstrated a high level of QoS (0.07ms jitter and 9.53ms delay). In regard to experimental examination, it measured the localisation coverage, accuracy measurements and a highly scalable security solution. The localisation coverage and accuracy measurements were achieved through the mmWave and VLC link transmitters. The proposed simulated annealing algorithm aimed at data optimisation for location measurements accuracy showed results of the average location error of x and y which showed significant improvement from x= 22.5 and y=21.6 to x=11.09 and y= 11.63. The proposed indoor location security solution showed significant results, as it provides a high scalability solution using the VNF. The solution showed that it was not 100% effective, as some of the fake discover packets still reached the DHCP server. This was due to the high load of traffic passing through the network. Nonetheless, 90% of the fake DHCP discover packets never reached the DHCP server because the scripts began blocking all fake discover packets after realising it was an attack. This conveys that the proposed system was able to run successfully without crashing or overloading the controller. Overall, the main challenges facing 5G have been addressed with their proposed solutions, which showed promising results. Conclusively showing that there is a lot more space for technological advancements to support the future of mobile networks.European Union’s Horizon 2020 research program - the Internet of Radio-Light (IoRL) project H2020-ICT 761992

Architecting the deployment of cloud-hosted services for guaranteeing multitenancy isolation.

In recent years, software tools used for Global Software Development (GSD) processes (e.g., continuous integration, version control and bug tracking) are increasingly being deployed in the cloud to serve multiple users. Multitenancy is an important architectural property in cloud computing in which a single instance of an application is used to serve multiple users. There are two key challenges of implementing multitenancy: (i) ensuring isolation either between multiple tenants accessing the service or components designed (or integrated) with the service; and (ii) resolving trade-offs between varying degrees of isolation between tenants or components. The aim of this thesis is to investigate how to architect the deployment of cloud-hosted service while guaranteeing the required degree of multitenancy isolation. Existing approaches for architecting the deployment of cloud-hosted services to serve multiple users have paid little attention to evaluating the effect of the varying degrees of multitenancy isolation on the required performance, resource consumption and access privilege of tenants (or components). Approaches for isolating tenants (or components) are usually implemented at lower layers of the cloud stack and often apply to the entire system and not to individual tenants (or components). This thesis adopts a multimethod research strategy to providing a set of novel approaches for addressing these problems. Firstly, a taxonomy of deployment patterns and a general process, CLIP (CLoud-based Identification process for deployment Patterns) was developed for guiding architects in selecting applicable cloud deployment patterns (together with the supporting technologies) using the taxonomy for deploying services to the cloud. Secondly, an approach named COMITRE (COmponent-based approach to Multitenancy Isolation Through request RE-routing) was developed together with supporting algorithms and then applied to three case studies to empirically evaluate the varying degrees of isolation between tenants enabled by multitenancy patterns for three different cloud-hosted GSD processes, namely-continuous integration, version control, and bug tracking. After that, a synthesis of findings from the three case studies was carried out to provide an explanatory framework and new insights about varying degrees of multitenancy isolation. Thirdly, a model-based decision support system together with four variants of a metaheuristic solution was developed for solving the model to provide an optimal solution for deploying components of a cloud-hosted application with guarantees for multitenancy isolation. By creating and applying the taxonomy, it was learnt that most deployment patterns are related and can be implemented by combining with others, for example, in hybrid deployment scenarios to integrate data residing in multiple clouds. It has been argued that the shared component is better for reducing resource consumption while the dedicated component is better in avoiding performance interference. However, as the experimental results show, there are certain GSD processes where that might not necessarily be so, for example, in version control, where additional copies of the files are created in the repository, thus consuming more disk space. Over time, performance begins to degrade as more time is spent searching across many files on the disk. Extensive performance evaluation of the model-based decision support system showed that the optimal solutions obtained had low variability and percent deviation, and were produced with low computational effort when compared to a given target solution

Contribution to multi-domain network slicing : resource orchestration framework and algorithms

5G/6G services and applications, in the context of the eMBB, mMTC and uRLLC network slicing framework, whose network infrastructure requirements may span beyond the coverage area of a single Infrastructure Provider (InP), are envisaged to be supported by leasing resources from multiple InPs. A challenging aspect for a Service Provider (SP) is how to obtain an optimal set of InPs on which to provision the requests and the particular substrate nodes and links within each InP on which to map the different VNFs and virtual links of the service requests, respectively, for a seamless, reliable and cost-effective orchestration of service requests. Existing works in this area either perform service mapping in uncoordinated manner, do not incorporate service reliability or do so from the perspective of stateless VNFs. Also they assume full information disclosure, or are based on exact approaches, which considerations are not well suited for future network scenarios characterized by delay sensitive mission critical applications and resource constrained networks. This thesis contributes to the above challenge by breaking the multi-domain service orchestration problem into two interlinked sub-problems that are solved in a coordinated manner: (1) Request splitting/partitioning (sub-problem 1), involving obtaining a subset of InPs and the corresponding inter-domain links on which to provision the different VNFs and virtual links of the service request; (2) Intra-domain VNF orchestration (sub-problem 2), involving obtaining the intra-domain nodes and links to provision the VNFs and virtual links of the sub-SFC associated with each InP. In this way, the thesis sets out four key targets that are necessary to align with the mission critical and delay sensitive use-cases envisaged in 5G and future networks in terms of service deployment cost and QoS: (1) coordinated mapping of service requests, with a view of realizing better utilization of the substrate resources; (2) survivability and fault-tolerant orchestration of service requests, to tame both QoS violations and the penalties from such violations; (3) limited disclosure of InP internal information, in order adhere to the privacy requirements InPs, and (4) achieving all the above targets in polynomial time. In order to realize the above targets, the thesis sought for solution techniques that are: (1) able to incorporate information learned in the previous solutions search space and historical mapping decisions, hence, resulting in acceptable performance even in scenarios of limited information exposure and fuzzy environments; (2) robust and less problem specific, hence, can be tailored to different optimization objectives, network topologies and service request constraints, thus enabling to deal with requests with either chained topologies or with bifurcated paths; (3) capable of dealing with an optimization problem that is jointly affected by multiple attributes, since in practice, the service deployment cost is jointly affected by multiple conflicting costs; (4) able to realize near-optimal solutions in practical run-times, thus rendering well suited approaches for delay sensitive and resource constrained scenarios. Three different algorithms namely, an RL, Genetic Algorithm (GA) and a fully distributed multi-stage graph-based algorithms are proposed for sub-problem 1. In addition, five different algorithms based on GA, Harmony search, RL, and multi-stage graph approach are proposed for sub-problem 2. Finally, in order to guide the implementation and adherence of the thesis proposals to the four main targets of the thesis, an architectural framework is proposed, aligned with the ETSI NFV-MANO architectural framework. Overall, the simulations results proved that the thesis proposals are optimized in terms of request acceptance ratios, mapping cost and execution time, hence, rendering such proposals well suited for 5G and future scenarios.Els serveis que es poden presentar en el marc de la tecnologia de “slicing” de xarxa de 5G/6G, com ara eMBB, mMTC o uRLLC, es possible que no els pugui oferir un sol proveïdor d’infraestructura (InP) degut a les limitacions que pot tenir la seva xarxa, i per tant que faci necessària la cooperació de múltiples InPs. En aquest cas, el primer repte que afronta el Proveïdor de Servei (SP) que rep la sol·licitud de desplegament es determinar el conjunt òptim de InPs que hi han d’intervenir i en concret els nodes i enllaços de cada un d’ells que s’han d’utilitzar per al mapatge de les diferents VNFs i enllaços virtuals de la sol·licitud. Els treballs que existeixen en aquesta àrea duen a terme el mapatge del servei be sigui de manera no coordinada, o no incorporen la fiabilitat, o ho fan des de la perspectiva de VNFs sense estat. També, pressuposen la divulgació total de la informació, o estan basats en metodologies exactes que fa que no siguin idonis per a escenaris de xarxes del futur, caracteritzats per aplicacions de missió critica, sensibles al retard i sobre xarxes amb recursos limitats. Aquesta tesi contribueix a afrontar aquests reptes dividint el problema d’orquestració de serveis multi domini en dos subproblemes relacionats, que es resolen de manera coordinada. (1) Divisió / partició de la sol·licitud de servei (sub-problema 1), que implica l'obtenció d'un subconjunt d'InPs i els enllaços interdomini corresponents sobre els quals proporcionar les diferents VNF i enllaços virtuals de la sol·licitud de servei; (2) Orquestració VNF intradomini (sub-problema 2), que implica l'obtenció dels nodes i enllaços intradomini per aprovisionar les VNF i enllaços virtuals dels sub-SFC associats a cada InP. D'aquesta manera, la tesi estableix quatre objectius clau que són necessaris per alinear-se amb els casos d'ús de missió crítica i sensibles al retard previstos en 5G i xarxes futures en termes de cost de desplegament del servei i QoS: (1) mapatge coordinat de les sol·licituds de servei, amb l'objectiu de realitzar una millor utilització dels recursos del substrat; (2) orquestració de les sol·licituds de servei contemplant la supervivència del servei en situacions de fallides, minimitzant les violacions de la QoS i les sancions derivades d'aquestes violacions; (3) divulgació limitada de la informació interna de l’InP, per tal d'adherir-se als requisits de privadesa dels InPs, i (4) aconseguir tots els objectius anteriors en temps polinòmic. Per tal de realitzar els objectius anteriors, la tesi busca solucions que siguin: (1) capaces d'incorporar informació apresa en les solucions anteriors de l'espai de cerca i decisions de mapatge històric, donant lloc a un rendiment acceptable fins i tot en escenaris d'exposició limitada a la informació i entorns difusos; (2) robustes i menys dependents dels problemes específics, i per tant, que es poden adaptar a diferents objectius d'optimització, topologies de xarxa i restriccions de sol·licitud de servei, permetent així fer front a sol·licituds amb cadenes de funcions de topologies molt diverses; (3) capaces de fer front a un problema d'optimització de múltiples atributs, ja que a la pràctica, el cost de desplegament del servei depèn de múltiples costos; (4) capaces de trobar solucions gairebé òptimes en temps suficientment breus, resultant així adequades a escenaris sensibles al retard i amb limitació de recursos. La tesi proposa tres algorismes diferents per al sub-problema 1: un algorisme de RL, un algorisme genètic (GA) i un algorisme multi etapa basat en grafs i completament distribuït. A més, es proposen cinc algorismes diferents basats en l'enfocament de grafs, un algorisme GA, un algorisme de cerca d’harmonia, un algorisme de RL i un algorisme multi-etapa per al sub-problema 2. Finalment, per tal de guiar la implementació i l'adhesió de les propostes als quatre objectius principals de la tesi, es proposa...Postprint (published version

Proceedings of the Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015) Krakow, Poland

Proceedings of: Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015). Krakow (Poland), September 10-11, 2015