Cloud Federation Research Articles

Towards Security-based Formation of Cloud Federations: A Game Theoretical Approach

Cloud federations allow Cloud Service Providers (CSPs) to deliver more efficient service performance by interconnecting their Cloud environments and sharing their resources. However, the security of the federated service could be compromised if the resources are shared with relatively insecure CSPs, and violations of the Security Service Level Agreement (Security-SLA) might occur. In this paper, we propose a Cloud federation formation model that considers the security level of CSPs. We start by applying the Goal-Question-Metric (GQM) method to develop a set of parameters that quantitatively describes the Security-SLA in the Cloud, and use it to evaluate the security levels of the CSPs and formed federations with respect to a defined Security-SLA baseline, while taking into account CSPs’ customers’ security satisfaction. Then, we model the Cloud federation formation process as a hedonic coalitional game with a preference relation that is based on the security level and reputation of CSPs. We propose a federation formation algorithm that enables CSPs to join a federation while minimizing their loss in security, and refrain from forming relatively insecure federations. Experimental results show that our model helps maintaining higher levels of security in the formed federations and reducing the rate and severity of Security-SLA violations.

Monitoring and management of a cloud application within a federation of cloud providers

Cloud federation is an emerging computing model where multiple resources from independent cloud providers are leveraged to create large-scale distributed virtual computing clusters, operating as within a single cloud organisation. This concept of service aggregation is characterised by interoperability features, which can address different problems about inter-cloud collaboration. One of the main issues within a cloud federation is related to the monitoring of the application deployed on resources coming from different vendors belonging to the federation. We present an agent-based architecture that aims at monitoring the user's cloud environment provided by the federation: the elasticity of the proposed architecture allows the configuration and customisation of the monitoring infrastructure to adapt it to the specific cloud application. Agents will be strengthened by algorithms and rules used to monitor QoS parameters that are critical for the specific application. The prototypal implementation of the proposed framework will be applied on a testbed application to validate the monitoring architecture.

Towards Trustworthy Multi-Cloud Services Communities: A Trust-Based Hedonic Coalitional Game

The prominence of cloud computing led to unprecedented proliferation in the number of web services deployed in cloud data centers. In parallel, service communities have gained recently increasing interest due to their ability to facilitate discovery, composition, and resource scaling in large-scale services' markets. The problem is that traditional community formation models may work well when all services reside in a single cloud but cannot support a multi-cloud environment. Particularly, these models overlook having malicious services that misbehave to illegally maximize their benefits and that arises from grouping together services owned by different providers. Besides, they rely on a centralized architecture whereby a central entity regulates the community formation; which contradicts with the distributed nature of cloud-based services. In this paper, we propose a three-fold solution that includes: trust establishment framework that is resilient to collusion attacks that occur to mislead trust results; bootstrapping mechanism that capitalizes on the endorsement concept in online social networks to assign initial trust values; and trust-based hedonic coalitional game that enables services to distributively form trustworthy multi-cloud communities. Experiments conducted on a real-life dataset demonstrate that our model minimizes the number of malicious services compared to three state-of-the-art cloud federations and service communities models.

OCCI Extension for Autonomic Negotiation in the Cloud Federation

OCCI Extension for Autonomic Negotiation in the Cloud Federation

TrustyFeer: A Subjective Logic Trust Model for Smart City Peer‐to‐Peer Federated Clouds

Cloud computing plays a major role in smart cities development by facilitating the delivery of various services in an efficient and effective manner. In a Peer‐to‐Peer (P2P) federated clouds ecosystem, multiple Cloud Service Providers (CSPs) collaborate and share services among them when experiencing a shortage in certain resources. Hence, incoming service requests to this specific resource can be delegated to other members. Nevertheless, the lack of preexisting trust relationship among CSPs in this distributed environment can affect the quality of service (QoS). Therefore, a trust management system is required to assist trustworthy peers in seeking reliable communication partners. We address this challenge by proposing TrustyFeer, a trust management system that allows peers to evaluate the trustworthiness of other peers based on subjective logic opinions, formulated using peers’ reputations and Service Level Agreements (SLAs). To demonstrate the utility of TrustyFeer, we evaluate the performance of our method against two long‐standing trust management systems. The simulation results show that TrustyFeer is more robust in decreasing the percentage of services that do not conform to SLAs and increasing the success rate of exchanged services by good CSPs conforming to SLAs. This should provide a trustworthy federated clouds ecosystem for a better, more sustainable future.

Identity and access management as a service in e-healthcare cloud

Data in cloud are protected, sensitive, confidential and are only accessible to actual individuals like certain identity. The resources are accessible through modification of the access control policies based on the user request. During the implementation of this online, the challenges like providing security to the identity and getting access to the resources through the refactoring of access control policies are to be dealt with. Here we introduced a framework for getting over these challenges. Federated identity management along with single sign-on could be utilised for providing security of identity to the user. Therefore the goal is the enhancement of the security of identity along with the availability of resources in multitier cloud and to provide inter-link cloud federation. The workflow offers an insight concerned with the identity and access management in the form of a service to e-healthcare cloud and thereafter a case scenario is studied.

Caucus: an authentication protocol for cloud federation

Cloud federation provides computing services of internal and external cloud providers. These computing services (e.g., virtual resources or services etc.) are collectively owned, accessed and controlled by one or more federating participants like user, broker, cloud providers, service provider etc. Thus, subjects are subsets of federating participants for use, execution, deployment and management of computing services. Each such subject must be authenticated before authorisation of computing services. Identity management solutions cannot address authentication of subjects comprising of more than two federating participants. In this paper, we propose a protocol for authentication (called as caucus authentication protocol) of subjects as a subset of one or more federating participants using a variant of multi-party computation (MPC). Theoretical study attempts to prove liveness and safety properties of proposed caucus authentication protocol (CAP) for the validation of dead-lock-free communication. The simulation results show that the protocol provides authentication of subjects in acceptable response time.

Cost Effective, Reliable and Secure Workflow Deployment over Federated Clouds

The significant growth in cloud computing has led to increasing number of cloud providers, each offering their service under different conditions – one might be more secure whilst another might be less expensive or more reliable. At the same time user applications have become more and more complex. Often, they consist of a diverse collection of software components, and need to handle variable workloads, which poses different requirements on the infrastructure. Therefore, many organisations are considering using a combination of different clouds to satisfy these needs. It raises, however, a non-trivial issue of how to select the best combination of clouds to meet the application requirements. This paper presents a novel algorithm to deploy workflow applications on federated clouds. First, we introduce an entropy-based method to quantify the most reliable workflow deployments. Second, we apply an extension of the Bell-LaPadula Multi-Level security model to address application security requirements. Finally, we optimise deployment in terms of its entropy and also its monetary cost, taking into account the cost of computing power, data storage and inter-cloud communication. We implemented our new approach and compared it against two existing scheduling algorithms: Extended Dynamic Constraint Algorithm (EDCA) and Extended Biobjective dynamic level scheduling (EBDLS). We show that our algorithm can find deployments that are of equivalent reliability but are less expensive and meet security requirements. We have validated our solution through a set of realistic scientific workflows, using well-known cloud simulation tools (WorkflowSim and DynamicCloudSim) and a realistic cloud based data analysis system (e-Science Central).

Dynamic partner selection in Cloud Federation for ensuring the quality of service for cloud consumers

Cloud Computing has become the popular paradigm for accessing the various scalable and on-demand computing services over the internet. Nowadays, individual Cloud Service Providers (CSPs) offering specialized services to the customers collaborate to form the Cloud Federation, in order to reap the real benefits of Cloud Computing. By collaboration, the member CSPs of the federation achieve better resource utilization and Quality of Service (QoS), thereby increasing their business prospects. When a CSP runs out of resources in the Cloud Federation, in order to offload the customer requests for resources to other CSP(s), identifying a suitable partner is a challenging task due to the lack of global coordination among them. In this paper, we propose the design and implementation of an efficient partner selection mechanism in the Cloud Federation, using the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods, and also considering the trust values of various CSPs in the federation. The AHP method is used to calculate the weights of the QoS parameters used in the TOPSIS method which is used to rank the various CSPs in the Cloud Federation according to the user requirements. Simulation results show the effectiveness of this approach in order to efficiently select the trustworthy partners in large scale federations to ensure the required QoS to the cloud consumers.

Enhancing Provider’s Profit on Cloud Market Infrastructure

Cloud infrastructure and platform services of the cloud are progressively getting to be plainly well known everywhere throughout the world, however, resource scheduling and allocation of multiple virtual machines on clouds are still a difficult task to attain. Streamlining of these issues can be useful in enhancing the vitality savings and load adjusting in substantial datacenters. Since the request of assets from client fluctuates with time, therefore, the load on cloud generally remains low during normal hours and remains high during peak hours. A solitary cloud service provider might not have obliged assets to satisfy client's solicitations amid the pinnacle hour and on contrary, there may be some providers with under-utilized resources. These difficulties can be overcome by using cloud federation which allows outsourcing at peak time i.e., underutilized providers can rent their resources to different IaaS (Infrastructure as a service) providers. Resource allocation and scheduling also have an impact in federated clouds, assets can be purchased from different individuals of cloud federation. Existing practices cloud confederation and resource management are a bit intricate, which makes them less dynamic in nature and in some cases decreases the revenue and profit of cloud service provider. A prototype of new method for cloud confederation has expounded which focuses to enhance the gain for cloud service provider. Our parameters include free assets to be sold, a number of outsourced resources, the fare of maintaining servers, the fare of third party resources, and workload.

A novel coordinated resource provisioning approach for cooperative cloud market

Cloud computing has been the enabling technology for shifting mass scale computation and storage requirements from individually owned clients towards an on-demand and utility styled alternative that provides many services. However, cost of maintaining datacenters, keeping the environmental ramifications of data centers at check, providing affordable computation alternative to users still needs to be addressed in a wholesome manner. One of the most exciting and recent research areas in cloud computing has been cloud federations that can mitigate the aforesaid problems. The past decade has seen immense efforts towards interoperability of clouds leading to realistic cloud federations. Motivated by these advancements and equipped with available technologies, this paper presents a detailed account of a cooperative cloud market. It delineates trading mechanisms of such cloud markets, extent of coordination among market players with illustrative examples. It also presents a novel two-phase coordinated resource reservation and provisioning (CRRP) approach that allocates cloud resources to users to meet the goal of minimizing users’ cost. To that end, this paper proposes a novel Most Cost Effective Providers’ Resources First (MCEPRF) algorithm. The efficiency of the proposed algorithm has been tested using synthetic data and the simulation results presented herein demonstrate the superiority of the proposed approach over its non-coordinated counterparts.

Workload Factoring and Resource Sharing via Joint Vertical and Horizontal Cloud Federation Networks

In cloud computing, a private (secondary) cloud can: 1) outsource workload to public (primary) clouds via vertical federation or 2) share resources with other secondary clouds through horizontal federation to enhance its service quality. While there have been attempts to establish a joint vertical and horizontal cloud federation (VHCF), little is known regarding the economic aspects (e.g., what stable cooperation pattern will form, will it improve efficiency) of such a complex cloud network, where secondary clouds are self-interested. To fill the gap, we analyze the interrelated workload factoring and federation formation among secondary clouds, while providing scalable algorithms to assist them to optimally select partners and outsource workload. We use a game theoretic approach to model the federation formation of clouds as a coalition game with externalities. We adopt a pessimistic core to characterize the cooperation stability and formulate its computation as a bilevel optimization problem. The properties of the problem are explored and efficient algorithms are developed to solve it. Experimental results show that the two common practices (no-cooperation and all-in-one federation) are not always stable. The results also show that compared with the two common practices, secondary clouds can decrease service delay penalty by around 11% with the proposed VHCF network.

Set up your own bioinformatics server: Chipster in EGI Federated Cloud

Chipster is an easy to use data analysis platform for bioinformatics. It provides an uniform graphical interface for over 360 commonly used bioinformatics tools including several R/Bioconductor-based tools and standalone programs (i.e. BWA, TopHat). Chipster is based on a client-server system where the user runs locally a Chipster-client that submits analysis tasks to a Chipster server. Even though Chipster is an open source tool, there is no public Chipster server that would be open for everybody. Due to that, a researcher needs to have an access to some of the existing Chipster servers to be able to use this platform. Alternatively, a researcher can set up his own Chipster server. In this paper, we describe how a Chipster server can be launched EGI Federated Could environment, that provides resources for all European researchers. With the instructions provided here, any European researcher can launch and manage his own Chipster server, suited for needs of a small research group or a bioinformatics course. The setup described here is based on a collaboration of several European instances. Chipster is developed by CSC – IT Center for Science Ltd. in Finland. European Grid Infrastructure (EGI) has fitted Chipster to cloud environment and provides the cloud computing resources. Finally, Rutherford Appleton Laboratory hosts the CVMFS server that provides the scientific tools and data sets for the Chipster servers running in EGI federated cloud.

Cost minimization for deadline-constrained bag-of-tasks applications in federated hybrid clouds

A mathematical programming model is proposed for a resource allocation problem in federated clouds, where bag-of-tasks (BoT) applications are assigned to instance types with different costs and performance levels. The proposed model is a binary linear programming problem containing deadline and resource constraints in the cloud federations and by the objective of minimizing the total cost of applications. These constraints and objective are explicitly expressed using mathematical functions, and the model is solved with the CPLEX solver. This paper also discusses a post-optimality analysis that deals with stability in assignment problems. Numerical results show that the optimal cost and optimal solutions in the cloud federations are lower and more stable, respectively, than those presented by single-provider clouds. In contrast to optimality in single-provider clouds, that in the cloud federations is less sensitive to input data.

Mathematical Programming Approach for Revenue Maximization in Cloud Federations

This paper assesses the benefits of cloud federation for cloud providers. Outsourcing and insourcing are explored as means to maximize the revenues of the providers involved in the federation. An exact method using a linear integer program is proposed to optimize the partitioning of the incoming workload across the federation members. A pricing model is suggested to enable providers to set their offers dynamically and achieve highest revenues. The conditions leading to highest gains are identified and the benefits of cloud federation are quantified.

Issues on developing interoperable cloud applications: definitions, concepts, approaches, requirements, characteristics and evaluation models

Among research opportunities in software engineering for cloud computing model, interoperability stands out. We found that the dynamic nature of cloud technologies and the battle for market domination make cloud applications locked-id, i.e, proprietary, non-portable and non-interoperable. In general context of cloud computing, interoperability goes beyond communication between systems like in other fields, it goes in direction of more dynamic, heterogeneous, complex and composed applications that take advantage of best features from different providers and services simultaneously. Interoperability in cloud constitutes a great challenge that must be overcome for that, in the future, software be more dynamic and improved. Objective: This paper aims at identifying how interoperability in cloud computing has been addressed in the existing literature, offering an up-to-date view of concepts relate to how to develop interoperable software that takes advantage of different cloud models. Thus, providing a basis for further research in the field and consolidating e better exploring existing concepts. Method: To fulfill this objective, we surveyed literature. We defined six research questions and conducted the study according to a protocol that included planning, and execution. Results: A first result of the review is that there is no well established definition for cloud interoperability. This study also identified cloud interoperability concepts (e.g., cloud brokers, multi-cloud and cloud federation), requirements for interoperable applications and existing cloud interoperability solutions, showing that these are either too specific for particular situations. Finally, the survey found no evaluation models for cloud interoperability solutions. We also present a discussion on the findings of this study. Conclusion: Since the study observed that there are no well-established cloud interoperability solutions yet, we conclude that the issues raised by lack of interoperability persist. Selecting one interoperable solution or even a cloud standard can free the system from the underlying providers, but it would still be locked into the selected particular solution.

Distributed resource allocation in federated clouds

Cloud computing is an emerging technology which relies on virtualization techniques to achieve the elasticity of shared resources for providing on-demand services. When the service demand increases, more resources are required to satisfy the service demand. Single cloud generally cannot provide unlimited services with limited physical resources; therefore, the federation of multiple clouds may be one possible solution. In such environment, different cloud providers may own different pricing and resource allocating strategies. Thus, how to select the most appropriate provider to host applications becomes an important issue for clients. However, as the requests of accessing distributed resources increase, the occurrences of competing the same resource may also increase. In this study, a Distributed Resource Allocation (DRA) approach is proposed to solve resource competition in the federated cloud environment. Each job is supposed to consist of one or more tasks, and the communication behavior between tasks could be profiled. The proposed approach groups tasks according to communication behavior to minimize communication overhead, and tries to allocate grouped tasks to achieve equilibrium when resource competition occurs. Experimental results show that the cloud provider could obtain more profits by outsourcing resources in the federated cloud with enough resources.

Cloud Federation and the Evolution of Cloud Computing

To satisfy the demand for collective and collaborative cloud use, academia and industry want to interconnect heterogeneous clouds to form a federated system. This approach is promising but also faces significant challenges.

Cloud Customer's Historical Record Based Resource Pricing

Media content in its digital form has been rapidly scaling up, resulting in popularity gain of cloud computing. Cloud computing makes it easy to manage the vastly increasing digital content. Moreover, additional features like, omnipresent access, further service creation, discovery of services, and resource management also play an important role in this regard. The forthcoming era is interoperability of multiple clouds, known as cloud federation or inter-cloud computing. With cloud federation, services would be provided through two or more clouds. Once matured and standardized, inter-cloud computing is supposed to provide services which would be more scalable, better managed, and efficient. Such tasks are provided through a middleware entity called cloud broker. A broker is responsible for reserving resources, managing them, discovering services according to customer's demands, Service Level Agreement (SLA) negotiation, and match-making between the involved service provider and the customer. So far existing studies discuss brokerage in a narrow focused way. In the research outcome presented in this paper, we provide a holistic brokerage model to manage on-demand and advance service reservation, pricing, and reimbursement. A unique feature of this study is that we have considered dynamic management of customer's characteristics and historical record in evaluating the economics related factors. Additionally, a mechanism of incentive and penalties is provided, which helps in trust build-up for the customers and service providers, prevention of resource underutilization, and profit gain for the involved entities. For practical implications, the framework is modeled on Amazon Elastic Compute Cloud (EC2) On-Demand and Reserved Instances service pricing. For certain features required in the model, data was gathered from Google Cluster trace.

Power‐aware server consolidation for federated clouds

SummaryCloud computing has evolved to provide computing resources on‐demand through a virtualized infrastructure, letting applications, computing power, data storage, and network resources to be provisioned and managed over private networks or over the Internet. Cloud services normally run on large data centers and demand a huge amount of electricity. Consequently, the electricity cost represents one of the major concerns of data centers, because it is sometimes nonlinear with the capacity of the data centers, and it is also associated with a high amount of carbon emission (CO2). However, energy‐saving schemes that result in too much degradation of the system performance or in violations of service‐level agreement (SLA) parameters would eventually cause the users to move to another cloud provider. Thus, there is a need to reach a balance between energy savings and the costs incurred by these savings in the execution of the applications. Therefore, in this paper, we propose and evaluate a power and SLA‐aware application consolidation solution for cloud federations. It comprises a multi‐agent system for server consolidation, taking into account SLA, power consumption, and carbon footprint. Different for similar solutions available in the literature, in our solution, when a cloud is overloaded, its data center needs to negotiate with other data centers before migrating the workload to another cloud. Simulation results show that our approach can reduce up to 46% of the power consumption while trying to meet performance requirements. Furthermore, we show that federated clouds can provide an adequate solution to deal with power consumption in the clouds. Copyright © 2016 John Wiley & Sons, Ltd.