Collaborative Cloud Services Research Articles

A complex network-based approach for resilient and flexible design resource allocation in industry 5.0

The development of Industry 5.0 focuses on customization, personalization in production, and the innovative thinking of employees, elevating the value of human contribution. Design, being an innovation-driven domain, demands greater flexibility in resource allocation. Consequently, rapidly and effectively allocating cloud service resources for personalized design tasks becomes crucial. With the emergence of the Industrial Metaverse, which blurs the boundaries between real and virtual design and manufacturing, it is gaining increasing attention. To embrace the advent of Industry 5.0 and the Industrial Metaverse, swift collaborative cloud services for design and manufacturing resources are essential. In this context, this article introduces a novel approach combining complex networks with the Non-dominated Sorting Genetic Algorithm III (NSGA-III), aimed at rapidly optimizing the dynamic allocation of distributed design resources (DRs). Initially, a multipartite graph is created from raw data and mapped to multiple bipartite graphs to identify key nodes in the network through intersection. Subsequently, these key nodes are used as reference points in the NSGA-III algorithm to achieve high-quality cloud service combinations, meeting the needs of design tasks with multiple subtasks, and related multi-objective optimization, including time, cost, reliability, maintainability, and reputation associated with the design. Finally, the Pareto service combinations obtained are used to construct a new complex network and employ the Girvan-Newman algorithm based on edge betweenness to identify community structures. In case of anomalies in the best service combination, alternative options can be swiftly searched from the identified communities, thereby enhancing the resilience of the cloud service process. Experimental results demonstrate the method's advantages in recovery and robustness, contributing significantly to the optimization of rapid cloud service allocation for DRs in the context of Industry 5.0.

A Cross-Tenant RBAC Model for Collaborative Cloud Services

A Cross-Tenant RBAC Model for Collaborative Cloud Services

Fast and Parallel Trust Computing Scheme Based on Big Data Analysis for Collaboration Cloud Service

Providing high trustworthy service is the most fundamental task for any cloud computing platform. Users are willing to deliver their computing tasks and the most sensitive data to cloud data centers, which is based on the trust relationship established between users and cloud service providers. However, with the development of collaboration cloud computing, how to provider fast response for a large number of users' service requests becomes a challenging problem. In order to quickly provide highly trustworthy services, the service platform must efficiently and quickly reply tens of millions of service requests, and automatically match-make tens of thousands of service resources. In this context, lightweight and fast (high-speed, low-overhead) trust computing schemes become the fundamental demand for implementing a trustworthy and collaborative cloud service. In this paper, we propose an innovative and parallel trust computing scheme based on big data analysis for the trustworthy cloud service environment. First, a distributed and modular perceiving architecture for large-scale virtual machines' service behavior is proposed relying on distributed monitoring agents. Then, an adaptive, lightweight, and parallel trust computing scheme is proposed for big monitored data. To the best of our knowledge, this paper is the first to use a blocked and parallel computing mechanism, the speed of trust calculation is greatly accelerated, which makes this trust computing scheme very suitable for a large-scale cloud computing environment. Performance analysis and experimental results verify feasibility and effectiveness of the proposed scheme.

Hypernetwork-based manufacturing service scheduling for distributed and collaborative manufacturing operations towards smart manufacturing

In the future smart manufacturing, both of sensor-based environment in shop floors and cloud-based environment among more and more enterprises are deployed gradually. Various distributed and separated manufacturing facilities are as collaborative cloud services, integrated and aggregated with their real-time information. It provides opportunities for the distributed and collaborative manufacturing operations across lots of distributed but networked enterprises on demand with enough flexibility. To this end, the scheduling problem and its result of those collaborative services for distributed manufacturing operations play an important role in improving manufacturing utilization and efficiency. In this paper, we put forward the hypernetwork-based models introducing the thought of graph coloring and an artificial bee colony algorithm based method for this scheduling problem. Three groups of experiments are carried out respectively to discuss therein different situations of distributed and collaborative manufacturing operations, i.e., in a private cloud, in a public cloud, and in a hybrid cloud. Some future studies with further consideration of collaboration equilibrium, dynamic control and data-based intelligence, are finally pointed out in the conclusion.

FRABAC: A new Hybrid Access Control Model for the Heterogeneous Multi-domain Systems

In this paper, a new hybrid, flexible, scalable and fine-grained model, fine-grained role-attribute access control (FRABAC) is proposed. FRABAC combines the benefits of the two most popular basic models: role-based access control (RBAC) and attribute-based access control (ABAC). The new proposed model overcomes the shortcomings of both models RBAC and ABAC known as combinatorial explosion in rules and roles when the security policy becomes complicated. Besides avoiding the combinatorial explosion, the new proposed model provides a role permission agreement (APA) to handle inter organisational access decision in collaborative cloud services cases. The proposed model is applied to define the security policies in realistic case studies and results are compared to previous existing models. On the other hand, the consistency and the correctness of the built policies are analysed through a formal modelling/analysis approach. This formal approach uses hierarchical coloured Petri nets (HCPNs) to model the policy and the CPN-tools to analyse the generated models.

A Collaborative Cloud Service Platform for Realizing Sustainable Make-To-Order Apparel Supply Chain

As fashion mass customization has been developing rapidly in the recent decade, supply chain in apparel industry was required to be more flexible for meeting the need of quick response toward the dynamic changes in the fashion market. Forecast-based make-to-stock production strategy was not suitable anymore. Make-to-order strategy was employed by more and more apparel companies. However, there were still many defects in traditional make-to-order apparel supply chain, e.g., sustainability is still at a low level. In this study, we proposed a collaborative cloud service platform to develop a novel model of sustainable make-to-order apparel supply chain. A service provider selection heuristic was designed for this platform to optimally select suppliers corresponding to each received demand. Multi-agent-based simulation technology was utilized to build the proposed platform and to evaluate the new sustainable supply chain model. Experiment was conducted in the simulation to compare our new model to traditional apparel make-to-order model and model with outsourcing mechanism. Based on simulation results, the remarkable improvement in terms of sustainability of the proposed platform and corresponding supply chain model was demonstrated.

An agility-oriented and fuzziness-embedded semantic model for collaborative cloud service search, retrieval and recommendation

Cloud computing enables a revolutionary paradigm of consuming ICT services. However, due to the inadequately described service information, users often feel confused while trying to find the optimal services. Although some approaches are proposed to deal with cloud service retrieval and recommendation issues, they would only work for certain restricted scenarios in dealing with basic service specifications. Indeed, the missing extent is that most of the cloud services are “agile” whilst there are many vague service terms and descriptions. This paper proposes an agility-oriented and fuzziness-embedded cloud service ontology model, which adopts agility-centric design along with OWL2 (Web Ontology Language) fuzzy extensions. The captured cloud service specifications are maintained in an open and collaborative manner, as the fuzziness in the model accepts rating updates from users on the fly. The model enables comprehensive service specification by capturing cloud concept details and their interactions, even across multiple service categories and abstraction levels. Utilizing the model as a knowledge base, a service recommendation system prototype is developed. Case studies demonstrate that the approach can outperform existing practices by achieving effective service search, retrieval and recommendation outcomes.

G-Route: an energy-aware service routing protocol for green cloud computing

In this paper, we present the design and implementation of Green Route (G-Route), an autonomic service routing protocol for constructing energy-efficient provider paths in collaborative cloud architectures. The chief contribution of this work resides in autonomously selecting the optimal set of composite service components sustaining the most efficient energy consumption characteristics among a set of providers for executing a particular service request. For ensuring the accountability of the system, the routing decision engine is designed to operate by processing accountable energy measurements extracted securely from within the cloud data centers using trusted computing technologies and cryptographic mechanisms. By pushing green computing constraints into the service routing decision engine, we can leverage the collaborative cloud computing model to maximize the energy savings achieved. This is realized by focusing on a path of providers that execute the service requests instead of directing the green computing efforts towards a single provider site. To the best of our knowledge, G-Route is the first service routing protocol that utilizes the collaborative properties among cloud providers to select "green" service routes and thus, to enhance the energy savings in the overall cloud computing infrastructure. The devised G-Route design is developed and deployed in a real cloud computing environment using the Amazon EC2 cloud platform. The experimental results obtained analyze the protocol convergence characteristics, traffic overhead, and resilience under anomalous service configurations and conditions and demonstrate the capability of the proposed system to significantly reduce the overall energy requirements of collaborative cloud services.

A Combinatorial Auction-based collaborative cloud services platform

In this paper, we introduce a combinatorial auction-based market model to enable Cloud Service Providers (CSPs) to satisfy complex user requirements collaboratively, where the CSPs are connected in a social network and communication costs among them cannot be ignored. However, in many situations CSPs may lie about their private information in order to maximize their earnings. Therefore, we combine the combinatorial auction with the VCG-auction mechanism to ensure that CSPs do not lie in auction. Based on above market model, we construct a collaborative cloud platform, which is divided into three layers: The user-layer receives requests from end-users, the auction-layer matches the requests with the cloud services provided by the Cloud Service Provider (CSP), and the CSP-layer forms a coalition to improve serving ability to satisfy complex requirements of users. In fact, the aim of the coalition formation is to find suitable partners for a particular CSP, and, we propose two heuristic algorithms for the coalition formation. The Breadth Traversal Algorithm (BTA) and Revised Ant Colony Algorithm (RACA) are proposed to form a coalition when bidding for a single cloud service in the auction. The experimental results show that RACA outperforms the BTA in bid price and our methods work well compared to the existing auction-based method in terms of economic efficiency. Other experiments were conducted to evaluate the impact of the communication cost on coalition formation and to assess the impact of iteration times for the optimal bidding price.

Incentive-based resource assignment and regulation for collaborative cloud services in community networks

Community networks are a successful example of a collective where communities operate ICT infrastructure based on the principle of reciprocal sharing of network bandwidth. Cloud computing, common in today's Internet, has however not materialised within community networks. We analyse in this paper socio-technical characteristics of community networks in order to derive scenarios for community clouds. Based on an architecture for such a community cloud, we implement a prototype for the incentive-driven resource assignment component and evaluate its behaviour experimentally. In simulations of large-scale community cloud scenarios we study the behaviour of the incentive mechanism in different configurations. Our evaluation gives insight into how the developed mechanisms regulate the consumption of cloud resources. Our results suggest a further integration of this regulation component into current cloud management platforms in order to open them up for the operation of an ecosystem of collaborative cloud services in community networks.

Multi‐tenancy authorization models for collaborative cloud services

SummaryThe cloud service model intrinsically caters to multiple tenants, most obviously not only in public clouds but also in private clouds for large organizations. Currently, most cloud service providers isolate user activities and data within a single tenant boundary with no or minimum cross‐tenant interaction. It is anticipated that this situation will evolve soon to foster cross‐tenant collaboration supported by Authorization as a Service. At present, there is no widely accepted model for cross‐tenant authorization. Recently, Calero et al. informally presented a multi‐tenancy authorization system (MTAS), which extends the well‐known role‐based access control model by building trust relations among collaborating tenants. In this paper, we formalize this MTAS model and propose extensions for finer‐grained cross‐tenant trust. We also develop an administration model for MTAS. We demonstrate the utility and practical feasibility of MTAS by means of an example policy specification in extensible access control markup language. To further test the metrics of the model, we develop a prototype system and conduct experiments on it. The result shows that the prototype has 12‐ms policy decision overhead on average and is scalable. We anticipate that researchers will develop additional multi‐tenant authorization models before eventual consolidation and convergence to standard industry practice. Copyright © 2014 John Wiley & Sons, Ltd.

Dynamic Collaborative Cloud Service Platform: Opportunities and Challenges

This letter presents a model for a dynamic collaboration (DC) platform among cloud providers (CPs) that prevents adverse business impacts, cloud vendor lock-in and violation of service level agreements with consumers, and also offers collaborative cloud services to consumers. We consider two major challenges. The first challenge is to find an appropriate market model in order to enable the DC platform. The second is to select suitable collaborative partners to provide services. We propose a novel combinatorial auction-based cloud market model that enables a DC platform among CPs. We also propose a new promising multi-objective optimization model to quantitatively evaluate the partners. Simulation experiments were conducted to verify both of the proposed models.

A market-oriented dynamic collaborative cloud services platform

Currently, interoperability and scalability are two major challenging issues for cloud computing. Forming a dynamic collaboration (DC) platform among cloud providers (CPs) can help to better address these issues. A DC platform can facilitate expense reduction, avoiding adverse business impacts and offering collaborative or portable cloud services to consumers. However, there are two major challenges involved in this undertaking; one is to find an appropriate market model to enable a DC platform, and the other one is to minimize conflicts among CPs that may occur in a market-oriented DC platform. In this paper, we present a novel combinatorial auction (CA)-based cloud market (CACM) model that enables a DC platform in CPs. To minimize conflicts among CPs, a new auction policy is proposed that allows a CP to dynamically collaborate with suitable partner CPs to form groups and publishes their group bids as a single bid to compete in the auction. However, identifying a suitable combination of CP partners to form the group and reduce conflicts is a NP-hard problem. Hence, we propose a promising multi-objective (MO) optimization model for partner selection using individual information and past collaborative relationship information, which is seldom considered. A multi-objective genetic algorithm (MOGA) called MOGA-IC is proposed to solve the MO optimization problem. This algorithm is developed using two popular MOGAs, the non-dominated sorting genetic algorithm (NSGA-II) and the strength pareto evolutionary genetic algorithm (SPEA2). The experimental results show that MOGA-IC with NSGA-II outperformed the MOGA-IC with SPEA2 in identifying useful pareto-optimal solution sets. Other simulation experiments were conducted to verify the effectiveness of the MOGA-IC in terms of satisfactory partner selection and conflict minimization in the CACM model. In addition, the performance of the CACM model was compared to the existing CA model in terms of economic efficiency.