A Game-Theoretic Approach to Coalition Formation in Fog Provider Federations

In this paper we deal with the problem of making a set of Fog Infrastructure Providers (FIPs) increase their profits when allocating their resources to process the data generated by IoT applications that need to meet specific QoS targets in face of time-varying workloads. We show that if FIPs cooperate among them, by mutually sharing their workloads and resources, then each one of them can improve its net profit. By using a game-theoretic framework, we study the problem of forming stable coalitions among FIPs. Furthermore, we propose a mathematical optimization model for profit maximization to allocate IoT applications to a set of FIPs, in order to reduce costs and, at the same time, to meet the corresponding QoS targets. Based on this, we propose an algorithm, based on cooperative game theory, that enables each FIP to decide with whom to cooperate in order to increase its profits. The effectiveness of the proposed algorithm is demonstrated through an experimental evaluation considering various workload intensities. The results we obtain from these experiments show the ability of our algorithm to form coalitions of FIPs that are stable and profitable in all the scenarios we consider

MIFaaS: A Mobile-IoT-Federation-as-a-Service Model for dynamic cooperation of IoT Cloud Providers

In the Internet of Things (IoT) arena, a constant evolution is observed towards the deployment of integrated environments, wherein heterogeneous devices pool their capacities to match wide-ranging user requirements. Solutions for efficient and synergistic cooperation among objects are, therefore, required. This paper suggests a novel paradigm to support dynamic cooperation among private/public local clouds of IoT devices. Differently from . device-oriented approaches typical of Mobile Cloud Computing, the proposed paradigm envisages an . IoT Cloud Provider (ICP)-oriented cooperation, which allows all devices belonging to the same private/public owner to participate in the federation process. Expected result from dynamic federations among ICPs is a remarkable increase in the amount of service requests being satisfied. Different from the Fog Computing vision, the network edge provides only management support and supervision to the proposed Mobile-IoT-Federation-as-a-Service (MIFaaS), thus reducing the deployment cost of peripheral micro data centers. The paper proposes a coalition formation game to account for the interest of rational cooperative ICPs in their own payoff. A proof-of-concept performance evaluation confirms that obtained coalition structures not only guarantee the satisfaction of the players' requirements according to their utility function, but also these introduce significant benefits for the cooperating ICPs in terms of number of tasks being successfully assigned

Profit Maximization by Forming Federations of Geo-Distributed MEC Platforms

This paper has been presented at: Seventh International Workshop on Cloud Technologies and Energy Efficiency in Mobile Communication Networks (CLEEN 2019). How cloudy and green will mobile network and services be? 15 April 2019 - Marrakech, MoroccoIn press / En prensaMulti-access edge computing (MEC) as an emerging technology which provides cloud service in the edge of multi-radio access networks aims to reduce the service latency experienced by end devices. When individual MEC systems do not have adequate resource capacity to fulfill service requests, forming MEC federations for resource sharing could provide economic incentive to MEC operators. To this end, we need to maximize social welfare in each federation, which involves efficient federation structure generations, federation profit maximization by resource provisioning configuration, and fair profit distribution among participants. We model the problem as a coalition game with difference from prior work in the assumption of latency and locality constraints and also in the consideration of various service policies/demand preferences. Simulation results show that the proposed approach always increases profits. If local requests are served with local resource with priority, federation improves profits without sacrificing request acceptance rates.This work was partially supported by the Ministry of Science and Technology, Taiwan, under grant numbers 106-2221-E-009-004 and by the H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant number 761586)

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

A Game-Theoretic Based QoS-Aware Capacity Management for Real-Time EdgeIoT Applications

More and more real-time IoT applications such as smart cities or autonomous vehicles require big data analytics with reduced latencies. However, data streams produced from distributed sensing devices may not suffice to be processed traditionally in the remote cloud due to: (i) longer Wide Area Network (WAN) latencies and (ii) limited resources held by a single Cloud. To solve this problem, a novel Software-Defined Network (SDN) based InterCloud architecture is presented for mobile edge computing environments, known as EdgeIoT. An adaptive resource capacity management approach is proposed to employ a policy-based QoS control framework using principles in coalition games with externalities. To optimise resource capacity policy, the proposed QoS management technique solves, adaptively, a lexicographic ordering bi-criteria Coalition Structure Generation (CSG) problem. It is an onerous task to guarantee in a deterministic way that a real-time EdgeIoT application satisfies low latency requirement specified in Service Level Agreements (SLA). CloudSim 4.0 toolkit is used to simulate an SDN-based InterCloud scenario, and the empirical results suggest that the proposed approach can adapt, from an operational perspective, to ensure low latency QoS for real-time EdgeIoT application instances

Resource management in the cloud: An end-to-end Approach

Philosophiae Doctor - PhDCloud Computing enables users achieve ubiquitous on-demand , and convenient access to a variety of shared computing resources, such as serves network, storage ,applications and more. As a business model, Cloud Computing has been openly welcomed by users and has become one of the research hotspots in the field of information and communication technology. This is because it provides users with on-demand customization and pay-per-use resource acquisition methods

Strategic and Blockchain-based Market Decisions for Cloud Computing

The cloud computing market has been in the center of attention for years where cloud providers strive to survive by either competition or cooperation. Some cloud providers choose to compete in the market that is dominated by few large providers and try to maximize their profit without sacrificing the service quality which leads to higher user ratings. Many research proposals tried to contribute to the cloud market competition. However, the majority of these proposals focus only on pricing mechanisms, neglecting thus the cloud service quality and users satisfaction. Meanwhile, cloud providers intend to form cloud federations to enhance their services quality and revenues. Nevertheless, traditional centralized cloud federations have strict challenges that might hinder the members' motivation to participate in, such as formation of stable coalitions with long-term commitments, participants' trustworthiness, shared revenue, and security of the managed data and services. For a stable and trustworthy federation, it is vital to avoid blind-trust on the claimed SLA guarantees from the members and monitor the quality of service considering the various characteristics of cloud services. This thesis aims to tackle the issues of cloud computing market from the two perspectives of competition and cooperation by: 1) modeling and solving the conflicting situation of revenue, user ratings and service quality, to improve the providers position in the market and increase the future users' demand; 2) proposing a user-centric game theoretical framework to allow the new and smaller cloud providers to have a share in the market and increase users satisfaction through providing high quality and added-value services; 3) motivating the cloud providers to adopt a coopetition behavior through a novel, fully distributed blockchain-based federation's structure that enables them to trade their computing resources through smart contracts; 4) introducing a new role of oracle as a verifier agent to monitor the quality of service and report to the smart contract agents deployed on the blockchain while optimizing the cost of using oracles; and 5) developing a Bayesian bandit learning oracles reliability mechanism to select the oracles smartly and optimize the cost and reliability of utilized oracles. All of the contributions are validated by simulations and implementations using real-world data

UML class diagrams supporting formalism definition in the Draw-Net Modeling System

The Draw-Net Modeling System (DMS) is a customizable framework supporting the design and the solution of models expressed in any graph-based formalism, thanks to an open architecture. During the years, many formalisms (Petri Nets, Bayesian Networks, Fault Trees, etc.) have been included in DMS. A formalism defines all the primitives that can be used in a model (nodes, arcs, properties, etc.) and is stored into XML files. The paper describes a new way to manage formalisms: the user can create a new formalism by drawing a UML Class Diagrams (CD); then the corresponding XML files are automatically generated. If instead the user intends to edit an existing formalism, a "reverse engineering" function generates the CD from the XML files. The CD can be handled inside DMS, and acts an intuitive and graphical "meta-model" to represent the formalism. An application example is presented