Summary Each cloud service provider provides only an interface of its own cloud infrastructure for enabling clients to use its cloud resources. However, there is a number of difficulties for cloud providers to ensure proper functioning. One of the main problems of a cloud provider is the lack of resources to support a huge number of on‐demand resources provisioning. Thus, resources cannot be distributed among different cloud providers since the federation is not the basic operation of the cloud provider. The most efficient way to overcome this problem is to extend the interface's cloud provider with an automatic negotiation to dynamically form the best agreement between the different cloud providers based on the service level agreement. In this article, we propose an extension for the Open Cloud Computing Interface which is the standardized interface for the cloud computing to support the automatic negotiation between the different cloud providers. To prove the efficiency and the effectiveness of our approach, we implement a prototype to evaluate the key presented in this article.

ABSTRACT The Internet of Things (IoT) facilitates the connectivity of billions of physical devices for exchanging information and enabling a wide range of applications. These applications can be presented in the form of dependent tasks, as outlined in a workflow. These workflows face limitations due to constraints in IoT sensors. To address these limitations, cloud computing has emerged to offer a large capacity of computing and storing with a great capability to adjust resources according to the need. However, cloud computing might not adequately meet the low‐latency of IoT workflow requirements when scheduling a workflow composed of IoT tasks due to its centralized nature. Moreover, cloud computing is not ideal for delay‐sensitive workflows and may increase communication costs. In response to these challenges, the use of fog computing as an extension to cloud computing scheme is recommended. Fog computing aims to process workflow tasks close to IoT devices. While fog computing offers various advantages, integrating these systems into workflow scheduling remains one of the most formidable challenges in distributed environments. Indeed, significant issues arise due to the timely execution and the resource limitations. In this survey paper, we present a Systematic Literature Review (SLR) on the current state of the art in this domain. We propose a taxonomy to compare and evaluate the existing studies on workflow scheduling approaches in cloud–fog computing environments. This taxonomy encompasses various criteria, including scheduling techniques, performance metrics, workflow dependencies, scheduling policies, and evaluation tools. We highlight certain recommendations for open issues which require more investigations. Our aim is to provide valuable insights for researchers and developers interested in understanding the contributions and challenges of current workflow scheduling approaches in cloud–fog computing environments.

Negotiation is a decision-making process when the trading parties with conflicting aims pursue their own concerns. With the new advances of cloud technology compared with the traditional IT infrastructure, services such as discovery, elasticity and monitoring are automatically processed. In the same way, the negotiation service between clients and cloud providers can be difficult if it is performed manually. Clients need a service that allows them to negotiate automatically with the cloud providers on some parameters that are important for them. In this paper, we propose a generation process for ensuring the negotiation in the cloud environment and specifically for the infrastructure as a service (IaaS) category. Our contributions in this paper include: (1) Designing a new proposal generation process based on factors affecting the negotiation between parties in the cloud, (2) Performing simulation experiments to evaluate our strategy and compare it with the other ones.

In the world of cyber-physical systems, the emergence of Smart Living Spaces (SLS) trend gives people the opportunity to benefit from new ways of living. Such technological trend that involves several aspects of daily life, allows the residents of the space to better customize and control their environment. SLS emphasizes, mainly, energy conservation, convenience and comfort as well as health care concerns. They are gradually realized in practical applications e.g. household appliances and medical and care systems. The SLS related applications are intended to improve the quality of life for users by ensuring that their needs are met, thereby producing more sustainable use of energy in a given living space. Moreover, the development of SLS technology makes the devices around us “smart”. This has brought lot of challenges, one of which is the access control management. In a SLS, there has to be a robust access control mechanism in place in order to ensure an authorized access to devices. This paper focuses on the use of Role Based Access Control (RBAC) model as an efficient means to prevent unauthorized users from gaining access to devices in a Smart Building. Then, to guarantee that only the approved users can access the devices, a Smart Building Manager (SBM) is required to ask an evaluation engine to evaluate requests. We separate the evaluation as a third party in the smart building in which we propose RBAC with domains that stands our policy model. We implement the policy model for the aim to ensure a fine grained access control to a smart building application while evolving from a mono-user to a multi-user.

Nowadays, mobile devices (e.g., tablet and smartphone) have become an important part of human life as they provide effective communication tools not limited by place and time. Nevertheless, they are facing many problems in terms of resource storage capabilities and communications. So, Mobile Cloud Computing (MCC) has emerged as a new paradigm to overcome these limitations by integrating Cloud Computing into the mobile environment. Adopting this paradigm raises several challenges, particularly with regard to the performance and the correctness. On the one hand, attaining the optimal performance in MCC is not a trivial task due to the variety of often conflicting objectives. On the other hand, because of the complexity of the studied applications, their modeling is more prone to errors on the part of the designers. In this paper, we present a background on MCC including definition, advantages and applications. We also provide a survey of the proposed approaches in the literature focusing on the performance and the verification in MCC systems. Then, we show a comparative study of the existing solutions based on some criteria. Finally, we identify the main research issues and we suggest some directions for future research.

Time is one of the main issues when dealing with process management. In fact, proper time handling can reduce cost and thus maximize profit for organizations. Then, modeling and verifying temporal constraints in business processes have become an area of intensive research. As a particular issue, the execution of these processes needs to be flexible by applying dynamic change operations to satisfy the variable organization requirements and to cope with unforeseen situations. Thus, it is crucial to re-ensure the temporal consistency of the changed processes. To do so, we propose a formal model which guarantees the correctness of time-aware processes while considering change operations. We used the Event-B method which supports an incremental development based on the refinement technique.

Nowdays, Cloud computing has gained a particular attention to run dynamic workflow applications. Nevertheless, due to lack of formal description of the resource perspective in dynamic workflows, the behavior of Cloud resource allocation cannot be correctly managed. In this paper, we aim at formally verifying the correctness of dynamic workflow changes in a Cloud environment using the Event-B method. More precisely, we propose a formal model which aims to preserve the correctness of workflow properties at both design time and runtime. We have considered properties related to control flow and resource perspectives.

Today, cloud computing has become a promising paradigm that aims at delivering computing resources and services on demand. The adoption of these services has been rapidly increasing. One of the main issues in this context is how to evaluate the ability of cloud systems to provide the desired services while respecting the QoS constraints. Experimentation in a real environment is a hard problem. In fact, the financial cost and the time required are very high. Also, the experiments are not repeatable, because a number of variables that are not under control of the tester may affect experimental results. Therefore, using simulation frameworks to evaluate cloud applications is preferred. This paper presents a survey of the existing simulation tools in cloud computing. It provides also a critical and comparative analysis of the studied tools. Finally, it stands out a major challenge to be addressed for further research.

The resources provisioning for workflow applications has become one of the most difficult challenges in the cloud. It consists in taking an appropriate decision when mapping tasks to resources while meeting QoS requirements. The need to modify workflows while they are being executed has become a major requirement to deal with unusual situations and evolution. Therefore, it is necessary to implement a provisioning policy which takes into account dynamic changes of workflow, while satisfying some performance criteria defined by the user. Simulation tools are an efficient solution to evaluate the performance of cloud applications. They offer a free environment that can mimic the behavior of a real cloud environment. Nevertheless, existing simulators are based on static application models. In this paper, we introduce CloudSim4DWf, which extends the existing CloudSim simulator with a new resource provisioning policy for dynamic workflows. The different experiments that we present show the efficiency of our tool.

Workflow technologies have become an efficient mean for the development of different applications. One well known challenge for executing workflows on cloud computing is the resources provisioning. The latter consists in making an appropriate decision when mapping tasks to resources considering multiple objectives that are often contradictory. The problem of resources provisioning for workflow applications in the cloud has been widely studied in the literature. However, the existing works didn't consider the change in workflow instances at runtime. This functionality has become a major requirement to deal with unusual situations and evolutions. In this paper, we present a first step towards the resources provisioning for a dynamic workflow in the cloud. In fact, we propose a provisioning algorithm which takes into account some constraints. After that, we extend it in order to support the dynamic changes of workflow. Our algorithm is evaluated using CloudSim simulator. The different experiments that we present show the efficiency of our approach in terms of financial execution cost and overhead.

Workflow applications are an enabling technology for coordinating the activities of an enterprise. The resources provisioning for these applications is one of the most difficult challenges in the cloud. It consists in making an appropriate decision when mapping tasks to resources while satisfying QoS requirements. Current resources provisioning approaches in the cloud consider only static workflows and ignore the need to change workflow instances at runtime. This functionality is an essential requirement to deal with unusual situations and evolutions. In this paper, we propose a novel resources provisioning approach which considers the dynamic changes of workflows. The proposed approach introduces an algorithm which determines the assignment of tasks to the appropriate cloud resources. After that, we extend it in order to support the dynamic changes of workflows. Our algorithm is evaluated using CloudSim simulator. The experimental results which we present illustrate the efficiency of our approach in term of financial execution cost.