A Self-Tuning procedure for resource management in InterCloud Computing

Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, College of Computer Science, North China University of Technology, Beijing, China The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.InterCloud Computing is a new cloud paradigm designed to guarantee service quality or performance and availability of on-demand resources. InterCloud enables cloud interoperability by promoting the interworking of cloud systems from different cloud providers using standard interfacing. Resource management in InterCloud, considered as an important functional requirement, has not attracted commensurate research attention. The focus of this paper is to propose a Software Cybernetic approach, in the form of an adaptive control framework, for efficient management of shared resources in peer-to-peer InterCloud computing. This research effort adopts cooperative game theory to model resource management in InterCloud. The space of cooperative arrangements (resource sharing) between the participant cloud systems is presented by using Integer Partitioning to characterise the worst case communication complexity in peer to peer InterCloud. Essentially, this paper presents an Integer partition based anytime algorithm as an optimal cost solution to the bi-objective optimisation problem in resource management, anchored principally on practical trade-off between the desired performance (quality of service) and communication complexity of collaborating resource clouds

FLA-SLA aware cloud collation formation using fuzzy preference relationship multi-decision approach for federated cloud

Cloud Computing provides a solution to enterprise applications in resolving their services at all level of Software, Platform, and Infrastructure. The current demand of resources for large enterprises and their specific requirement to solve critical issues of services to their clients like avoiding resources contention, vendor lock-in problems and achieving high QoS (Quality of Service) made them move towards the federated cloud. The reliability of the cloud has become a challenge for cloud providers to provide resources at an instance request satisfying all SLA (Service Level Agreement) requirements for different consumer applications. To have better collation among cloud providers, FLA (Federated Level Agreement) are given much importance to get consensus in terms of various KPI’s (Key Performance Indicator’s) of the individual cloud providers. This paper proposes an FLA-SLA Aware Cloud Collation Formation algorithm (FS-ACCF) considering both FLA and SLA as major features affecting the collation formation to satisfy consumer request instantly. In FS-ACCF algorithm, fuzzy preference relationship multi-decision approach was used to validate the preferences among cloud providers for forming collation and gaining maximum profit. Finally, the results of FS-ACCF were compared with S-ACCF (SLA Aware Collation Formation) algorithm for 6 to 10 consecutive requests of cloud consumers with varied VM configurations for different SLA parameters like response time, process time and availability

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

클라우드 서비스 연합 장려 모델

학위논문 (석사)-- 서울대학교 대학원 공과대학 협동과정 기술경영·경제·정책전공, 2017. 8. Jorn Altmann.In cloud computing, big service providers rule the market due to the economies of scale. A cloud federation presents a possible solution that allows small cloud providers to increase their competitiveness by making alliances with one another, thus forming a network with shared resources. Previous research suggests several different variables that may incentivize the participation of a selfish cloud provider, such as cost disparity, big competitors, and an efficient revenue sharing mechanism. It can be assumed that each individual cloud provider aims to maximize its profits and will choose to make alliances that provide it a constant benefit. For deciding on whether to federate or not, cloud providers take into consideration whether the federation-underlying revenue sharing will yield them an increase in profits. The proposed study models the interactions between selfish heterogeneous agents in a repeated game that aims to maximize individual profits. Each agent starts off as an individual and is allowed to change its strategies and federate with other providers in order to improve its own performance. By looking at the speed of collaboration and overall profit of individuals, we can determine which specific incentives encourage the creation of cloud federations.Chapter 1 Introduction 1 1.1 Cloud Computing 1 1.2 Problem Description 2 1.3 Research Objective 3 Chapter 2 Related Work.. 4 Chapter 3 Experiment Formulation 8 3.1 Model 8 3.2 Experiment Setup 10 3.2.1 Revenue Sharing. 11 3.2.2 Capacity Disparity 14 3.2.3 Cost Disparity. 15 3.2.4 Big Competitor. 16 3.2.5 Volatile Demand. 17 Chapter 4 Results 17 4.1 Revenue Sharing Scenario 18 4.2 Capacity Disparity Scenario 19 4.3 Cost Disparity Scenario. 21 4.4 Big Competitor Scenario 22 4.5 Federation Behavior in Demand Peaks. 23 Chapter 5 Conclusions.. 24 5.1 Summary. 24 5.2 Discussion and Implications. 25 5.3 Limitations and Future Work 26 Bibliography. 27 Abstract. 29Maste

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

A game-theoretic approach to coalition formation in green cloud federations

Federations among sets of Cloud Providers (CPs), whereby a set of CPs agree to mutually use their own resources to run the VMs of other CPs, are considered a promising solution to the problem of reducing the energy cost. In this paper, we address the problem of federation formation for a set of CPs, whose solution is necessary to exploit the potential of cloud federations for the reduction of the energy bill. We devise an algorithm, based on cooperative game theory, that can be readily implemented in a distributed fashion, and that allows a set of CPs to cooperatively set up their federations in such a way that their individual profit is increased with respect to the case in which they work in isolation. We show that, by using our algorithm and the proposed CPs' utility function, they are able to self-organize into Nash-stable federations and, by means of iterated executions, to adapt themselves to environmental changes. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm