Rule-based SLA management for revenue maximisation in cloud computing markets

This paper introduces several Business Rules for maximising the revenue of Providers in Cloud Computing Markets. These rules apply in both negotiation and execution time, and enforce the achievement of Business-Level Objectives by establishing a bidirectional data flow between market and resource layers. The experiments demonstrate that the revenue is maximized by using both resource data when negotiating, and economic information when managing the resources.Postprint (published version

Business-driven resource allocation and management for data centres in cloud computing markets

Cloud Computing markets arise as an efficient way to allocate resources for the execution of tasks and services within a set of geographically dispersed providers from different organisations. Client applications and service providers meet in a market and negotiate for the sales of services by means of the signature of a Service Level Agreement that contains the Quality of Service terms that the Cloud provider has to guarantee by managing properly its resources. Current implementations of Cloud markets suffer from a lack of information flow between the negotiating agents, which sell the resources, and the resource managers that allocate the resources to fulfil the agreed Quality of Service. This thesis establishes an intermediate layer between the market agents and the resource managers. In consequence, agents can perform accurate negotiations by considering the status of the resources in their negotiation models, and providers can manage their resources considering both the performance and the business objectives. This thesis defines a set of policies for the negotiation and enforcement of Service Level Agreements. Such policies deal with different Business-Level Objectives: maximisation of the revenue, classification of clients, trust and reputation maximisation, and risk minimisation. This thesis demonstrates the effectiveness of such policies by means of fine-grained simulations. A pricing model may be influenced by many parameters. The weight of such parameters within the final model is not always known, or it can change as the market environment evolves. This thesis models and evaluates how the providers can self-adapt to changing environments by means of genetic algorithms. Providers that rapidly adapt to changes in the environment achieve higher revenues than providers that do not. Policies are usually conceived for the short term: they model the behaviour of the system by considering the current status and the expected immediate after their application. This thesis defines and evaluates a trust and reputation system that enforces providers to consider the impact of their decisions in the long term. The trust and reputation system expels providers and clients with dishonest behaviour, and providers that consider the impact of their reputation in their actions improve on the achievement of their Business-Level Objectives. Finally, this thesis studies the risk as the effects of the uncertainty over the expected outcomes of cloud providers. The particularities of cloud appliances as a set of interconnected resources are studied, as well as how the risk is propagated through the linked nodes. Incorporating risk models helps providers differentiate Service Level Agreements according to their risk, take preventive actions in the focus of the risk, and pricing accordingly. Applying risk management raises the fulfilment rate of the Service-Level Agreements and increases the profit of the providerPostprint (published version

Maximising revenue in cloud computing markets by means of economically enhanced SLA management

This paper proposes a bidirectional communication between market brokers and resource managers in Cloud Computing Markets. This communication is implemented by means of an Economically Enhanced Resource Manager (EERM), that supports the negotiation process by deciding which tasks can be allocated or not, and under which economic and technical conditions. The EERM also uses the economic information that collects from market layers to manage the resources accordingly to concrete BLOs. This paper shows several Business Policies and Rules for maximizing the revenue of a Cloud Provider that sells its services and resources in a market. Their validity is demonstrated through several experiments that shown how the application of these rules can have a positive influence in the revenue and minimize the violations of Service-Level Agreements.Preprin

Cloud Oriented Business Process Outsourcing using Business Rule Management

The development of cloud services is rapidly transforming IT outsourcing. Highly standardized services are offered in elastic ways. Pricing models are shifting away from up front investments, allowing pay per use. In business process outsourcing (BPO), these business models are still less common and BPO services are often still implemented on a per customer basis, often heavily based on customer’s existing practices. This paper presents a framework for using the field of business rule management (BRM) to develop BPO offerings that exhibit cloud properties. The framework specifies ways in which business rules can be used to parameterize the different aspects of a BPO service. The framework is applied in three practical scenarios, which are evaluated for their cloud characteristics by interviewing experts from outsourcing customers, outsourcing suppliers and consultants

Cloud Service Brokerage: A systematic literature review using a software development lifecycle

Cloud Service Brokerage (CSB) is an emerging technology that has become popular with cloud computing. CSB is a middleman providing value added services, developed using standard software development lifecycle, from cloud providers to consumers. This paper provides a systematic literature review on this topic, covering 41 publications from 2009 to 2015. The paper aims to provide an overview of CSB research status, and give suggestions on how CSB research should proceed. A descriptive analysis reveals a lack of contributions from the Information Systems discipline. A software development lifecycle analysis uncovers a severe imbalance of research contributions across the four stages of software development: design, develop, deploy, and manage. The majority of research contributions are geared toward the design stage with a minimal contribution in the remaining stages. As such, we call for a balanced research endeavor across the cycle given the equal importance of each stage within the CSB paradigm

A framework for exchange-based trading of cloud computing commodities

Cloud computing is a paradigm for using IT services with characteristics such as flexible and scalable service usage, on-demand availability, and pay-as-you-go billing. Respective services are called cloud services and their nature usually motivates a differentiation in three layers: Infrastructure as a Service (IaaS) for cloud services offering functionality of hardware resources in a virtualised way, Platform as a Service (PaaS) for services acting as execution platforms, and Software as a Service (SaaS) representing applications provided in a cloud computing way. Any of these services is offered with the illusion of unlimited scalability. The infinity gained by this illusion implies the need for some kind of regulation mechanism to manage sup- ply and demand. Today’s static pricing mechanisms are limited in their capabilities to adapt to dynamic characteristics of cloud environments such as changing workloads. The solution is a dy- namic pricing approch compareable to today’s exchanges. This requires comparability of cloud services and the need of standardised access to avoid vendor lock-in. To achieve comparability, a classification for cloud services is introcuced, where classes of cloud services representing tradable goods are expressed by the minimum requirements for a certain class. The main result of this work is a framework for exchange-based trading of cloud com- puting commodities, which is composed of four core components derived from existing ex- change market places. An exchange component takes care of accepting orders from buyers and sellers and determines the price for the goods. A clearing component is responsible for the fi- nancial closing of a trade. The settlement component takes care of the delivery of the cloud service. A rating component monitors cloud services and logs service level agreement breaches to calculate provider ratings, especially for reliability, which is an important factor in cloud computing. The framework establishes a new basis for using cloud services and more advanced business models. Additionally, an overview of selected economic aspects including ideas for derivative financial instruments like futures, options, insurances, and more complex ones is pro- vided. A first version of the framework is currently being implemented and in use at Deutsche Bo ̈rse Cloud Exchange AG.Cloud Computing repra ̈sentiert eine neue Art von IT-Diensten mit bestimmten Eigenschaften wie Flexibilita ̈t, Skalierbarkeit, sta ̈ndige Verfu ̈gbarkeit und nutzungsbezogene (pay-as-you-go) Abrechnung. IT-Dienste, die diese Eigenschaften besitzen, werden als Cloud Dienste bezeichnet und lassen sich in drei Ebenen einteilen: Infrastructure as a Service (IaaS), womit virtuelle Hardware Ressourcen zur Verfu ̈gung gestellt werden, Platform as a Service (PaaS), das eine Ausfu ̈hrungsumgebung darstellt und Software as a Service (SaaS), welches das Anbieten von Applikationen als Cloud Dienst bezeichnet. Cloud Dienste werden mit der Illusion unendlicher Skalierbarkeit angeboten. Diese Unendlichkeit erfordert einen Mechanismus, der in der Lage ist, Angebot und Nachfrage zu regulieren. Derzeit eingesetzte Preisbildungsmechanismen sind in ihren Mo ̈glichkeiten beschra ̈nkt sich auf die Dynamik in Cloud Umgebungen, wie schnell wechselnde Bedarfe an Ressourcen, einzustellen. Eine mo ̈gliche Lo ̈sung stellt ein dynamischer Preisbildungsmechanismus dar, der auf dem Modell heutiger Bo ̈rsen beruht. Dieser erfordert die Standardisierung und Vergleichbarkeit von Cloud Diensten und eine standardisierte Art des Zugriffs. Um die Vergleichbarkeit von Cloud Diensten zu erreichen, werden diese in Klassen eingeteilt, die jeweils ein am Bo ̈rsenplatz handelbares Gut darstellen. Das Ergebnis dieser Arbeit ist ein Rahmenwerk zum bo ̈rsenbasierten Handel von Cloud Computing Commodities, welches aus vier Kernkomponenten besteht, die von existieren- den Bo ̈rsen und Rohstoffhandeslpla ̈tzen abgeleitet werden ko ̈nnen. Die Bo ̈rsenkomponente nimmt Kauf- und Verkaufsorders entgegen und bestimmt die aktuellen Preise der handelbaren Cloud Rohstoffe. Die Clearing Komponente stellt die finanzielle Abwicklung eines Gescha ̈ftes sicher, das Settlement ist fu ̈r die tatsa ̈chliche Lieferung zusta ̈ndig und die Rating Komponente u ̈berwacht die Cloud Dienste im Hinblick auf die Nichteinhaltung von Service Level Agree- ments und vor allem deren Zuverla ̈ssigkeit, die einen wichtigen Faktor im Cloud Computing darstellt. Das Rahmenwerk begru ̈ndet eine neue Basis fu ̈r die Cloudnutzung und ermo ̈glicht fort- geschrittenere Gescha ̈ftsmodelle. In der Arbeit wird weiters ein U ̈berblick u ̈ber o ̈konomis- che Aspekte wie Ideen zu derivaten Finanzinstrumenten auf Cloud Computing Commodities gegeben. Dieses Rahmenwerk wird derzeit an der Deutsche Bo ̈rse Cloud Exchange AG imple- mentiert und bereits in einer ersten Version eingesetzt

A Game-Theoretic Approach to Strategic Resource Allocation Mechanisms in Edge and Fog Computing

With the rapid growth of Internet of Things (IoT), cloud-centric application management raises questions related to quality of service for real-time applications. Fog and edge computing (FEC) provide a complement to the cloud by filling the gap between cloud and IoT. Resource management on multiple resources from distributed and administrative FEC nodes is a key challenge to ensure the quality of end-user’s experience. To improve resource utilisation and system performance, researchers have been proposed many fair allocation mechanisms for resource management. Dominant Resource Fairness (DRF), a resource allocation policy for multiple resource types, meets most of the required fair allocation characteristics. However, DRF is suitable for centralised resource allocation without considering the effects (or feedbacks) of large-scale distributed environments like multi-controller software defined networking (SDN). Nash bargaining from micro-economic theory or competitive equilibrium equal incomes (CEEI) are well suited to solving dynamic optimisation problems proposing to ‘proportionately’ share resources among distributed participants. Although CEEI’s decentralised policy guarantees load balancing for performance isolation, they are not faultproof for computation offloading. The thesis aims to propose a hybrid and fair allocation mechanism for rejuvenation of decentralised SDN controller deployment. We apply multi-agent reinforcement learning (MARL) with robustness against adversarial controllers to enable efficient priority scheduling for FEC. Motivated by software cybernetics and homeostasis, weighted DRF is generalised by applying the principles of feedback (positive or/and negative network effects) in reverse game theory (GT) to design hybrid scheduling schemes for joint multi-resource and multitask offloading/forwarding in FEC environments. In the first piece of study, monotonic scheduling for joint offloading at the federated edge is addressed by proposing truthful mechanism (algorithmic) to neutralise harmful negative and positive distributive bargain externalities respectively. The IP-DRF scheme is a MARL approach applying partition form game (PFG) to guarantee second-best Pareto optimality viii | P a g e (SBPO) in allocation of multi-resources from deterministic policy in both population and resource non-monotonicity settings. In the second study, we propose DFog-DRF scheme to address truthful fog scheduling with bottleneck fairness in fault-probable wireless hierarchical networks by applying constrained coalition formation (CCF) games to implement MARL. The multi-objective optimisation problem for fog throughput maximisation is solved via a constraint dimensionality reduction methodology using fairness constraints for efficient gateway and low-level controller’s placement. For evaluation, we develop an agent-based framework to implement fair allocation policies in distributed data centre environments. In empirical results, the deterministic policy of IP-DRF scheme provides SBPO and reduces the average execution and turnaround time by 19% and 11.52% as compared to the Nash bargaining or CEEI deterministic policy for 57,445 cloudlets in population non-monotonic settings. The processing cost of tasks shows significant improvement (6.89% and 9.03% for fixed and variable pricing) for the resource non-monotonic setting - using 38,000 cloudlets. The DFog-DRF scheme when benchmarked against asset fair (MIP) policy shows superior performance (less than 1% in time complexity) for up to 30 FEC nodes. Furthermore, empirical results using 210 mobiles and 420 applications prove the efficacy of our hybrid scheduling scheme for hierarchical clustering considering latency and network usage for throughput maximisation.Abubakar Tafawa Balewa University, Bauchi (Tetfund, Nigeria

DRIVE: A Distributed Economic Meta-Scheduler for the Federation of Grid and Cloud Systems

The computational landscape is littered with islands of disjoint resource providers including commercial Clouds, private Clouds, national Grids, institutional Grids, clusters, and data centers. These providers are independent and isolated due to a lack of communication and coordination, they are also often proprietary without standardised interfaces, protocols, or execution environments. The lack of standardisation and global transparency has the effect of binding consumers to individual providers. With the increasing ubiquity of computation providers there is an opportunity to create federated architectures that span both Grid and Cloud computing providers effectively creating a global computing infrastructure. In order to realise this vision, secure and scalable mechanisms to coordinate resource access are required. This thesis proposes a generic meta-scheduling architecture to facilitate federated resource allocation in which users can provision resources from a range of heterogeneous (service) providers. Efficient resource allocation is difficult in large scale distributed environments due to the inherent lack of centralised control. In a Grid model, local resource managers govern access to a pool of resources within a single administrative domain but have only a local view of the Grid and are unable to collaborate when allocating jobs. Meta-schedulers act at a higher level able to submit jobs to multiple resource managers, however they are most often deployed on a per-client basis and are therefore concerned with only their allocations, essentially competing against one another. In a federated environment the widespread adoption of utility computing models seen in commercial Cloud providers has re-motivated the need for economically aware meta-schedulers. Economies provide a way to represent the different goals and strategies that exist in a competitive distributed environment. The use of economic allocation principles effectively creates an open service market that provides efficient allocation and incentives for participation. The major contributions of this thesis are the architecture and prototype implementation of the DRIVE meta-scheduler. DRIVE is a Virtual Organisation (VO) based distributed economic metascheduler in which members of the VO collaboratively allocate services or resources. Providers joining the VO contribute obligation services to the VO. These contributed services are in effect membership “dues” and are used in the running of the VOs operations – for example allocation, advertising, and general management. DRIVE is independent from a particular class of provider (Service, Grid, or Cloud) or specific economic protocol. This independence enables allocation in federated environments composed of heterogeneous providers in vastly different scenarios. Protocol independence facilitates the use of arbitrary protocols based on specific requirements and infrastructural availability. For instance, within a single organisation where internal trust exists, users can achieve maximum allocation performance by choosing a simple economic protocol. In a global utility Grid no such trust exists. The same meta-scheduler architecture can be used with a secure protocol which ensures the allocation is carried out fairly in the absence of trust. DRIVE establishes contracts between participants as the result of allocation. A contract describes individual requirements and obligations of each party. A unique two stage contract negotiation protocol is used to minimise the effect of allocation latency. In addition due to the co-op nature of the architecture and the use of secure privacy preserving protocols, DRIVE can be deployed in a distributed environment without requiring large scale dedicated resources. This thesis presents several other contributions related to meta-scheduling and open service markets. To overcome the perceived performance limitations of economic systems four high utilisation strategies have been developed and evaluated. Each strategy is shown to improve occupancy, utilisation and profit using synthetic workloads based on a production Grid trace. The gRAVI service wrapping toolkit is presented to address the difficulty web enabling existing applications. The gRAVI toolkit has been extended for this thesis such that it creates economically aware (DRIVE-enabled) services that can be transparently traded in a DRIVE market without requiring developer input. The final contribution of this thesis is the definition and architecture of a Social Cloud – a dynamic Cloud computing infrastructure composed of virtualised resources contributed by members of a Social network. The Social Cloud prototype is based on DRIVE and highlights the ease in which dynamic DRIVE markets can be created and used in different domains