Cloud computing with its three key facets (i.e., Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service) and its inherent advantages (e.g., elasticity and scalability) still faces several challenges. The distance between the cloud and the end devices might be an issue for latency-sensitive applications such as disaster management and content delivery applications. Service level agreements (SLAs) may also impose processing at locations where the cloud provider does not have data centers. Fog computing is a novel paradigm to address such issues. It enables provisioning resources and services outside the cloud, at the edge of the network, closer to end devices, or eventually, at locations stipulated by SLAs. Fog computing is not a substitute for cloud computing but a powerful complement. It enables processing at the edge while still offering the possibility to interact with the cloud. This paper presents a comprehensive survey on fog computing. It critically reviews the state of the art in the light of a concise set of evaluation criteria. We cover both the architectures and the algorithms that make fog systems. Challenges and research directions are also introduced. In addition, the lessons learned are reviewed and the prospects are discussed in terms of the key role fog is likely to play in emerging technologies such as tactile Internet.

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A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges

Cloud computing with its three key facets (i.e., IaaS, PaaS, and SaaS) and its inherent advantages (e.g., elasticity and scalability) still faces several challenges. The distance between the cloud and the end devices might be an issue for latency-sensitive applications such as disaster management and content delivery applications. Service Level Agreements (SLAs) may also impose processing at locations where the cloud provider does not have data centers. Fog computing is a novel paradigm to address such issues. It enables provisioning resources and services outside the cloud, at the edge of the network, closer to end devices or eventually, at locations stipulated by SLAs. Fog computing is not a substitute for cloud computing but a powerful complement. It enables processing at the edge while still offering the possibility to interact with the cloud. This article presents a comprehensive survey on fog computing. It critically reviews the state of the art in the light of a concise set of evaluation criteria. We cover both the architectures and the algorithms that make fog systems. Challenges and research directions are also introduced. In addition, the lessons learned are reviewed and the prospects are discussed in terms of the key role fog is likely to play in emerging technologies such as Tactile Internet.

A Comprehensive Survey on Fog Computing: State-of-the-art and Research Challenges

Big data refer to the massive amounts and varieties of information in the structured and unstructured form, generated by social networking sites, biomedical equipment, financial companies, internet...

Analytics, challenges and applications in big data environment: a survey

Cloud computing has recently emerged as a new computing paradigm enabling on-demand and scalable provision of resources, platforms and software as services. In order to satisfy different storage requirements, cloud applications usually need to access and interact with different relational and NoSQL data stores having heterogeneous APIs. This APIs heterogeneity induces two main problems. First it ties cloud applications to specific data stores hampering therefore their migration. Second, it requires developers to be familiar with different APIs. In this paper, we propose a generic resources model defining the different concept used in each type of data store. These resources are managed by ODBAPI a streamlined and a unified REST API enabling to execute CRUD operations on different NoSQL and relational databases. ODBAPI decouples cloud applications from data stores alleviating therefore their migration. Moreover it relieves developers task by removing the burden of managing different APIs.

ODBAPI: A Unified REST API for Relational and NoSQL Data Stores

Uniform data access platform for SQL and NoSQL database systems

The production of huge amount of data and the emergence of cloud computing have introduced new requirements for data management. Many applications need to interact with several heterogeneous data stores depending on the type of data they have to manage: traditional data types, documents, graph data from social networks, simple key-value data, etc. Interacting with heterogeneous data models via different APIs, and multiple data store applications imposes challenging tasks to their developers. Indeed, programmers have to be familiar with different APIs. In addition, the execution of complex queries over heterogeneous data models cannot, currently, be achieved in a declarative way as it is used to be with mono-data store application, and therefore requires extra implementation efforts. Moreover, developers need to master and deal with the complex processes of cloud discovery, and application deployment and execution. In this paper we propose an integrated set of models, algorithms and tools aiming at alleviating developers task for developing, deploying and migrating multiple data stores applications in cloud environments. Our approach focuses mainly on three points. First, we provide a unifying data model used by applications developers to interact with heterogeneous relational and NoSQL data stores. Based on that, they express queries using OPEN-PaaS-DataBase API (ODBAPI), a unique REST API allowing programmers to write their applications code independently of the target data stores. Second, we propose virtual data stores, which act as a mediator and interact with integrated data stores wrapped by ODBAPI. This run-time component supports the execution of single and complex queries over heterogeneous data stores. Finally, we present a declarative approach that enables to lighten the burden of the tedious and non-standard tasks of (1) discovering relevant cloud environment and (2) deploying applications on them while letting developers to simply focus on specifying their storage and computing requirements. A prototype of the proposed solution has been developed and is currently used to implement use cases from the OpenPaaS project.

Supporting Multi Data Stores Applications in Cloud Environments

The production of huge amount of data and the emergence of cloud computing have introduced new requirements for data management. Many applications need to interact with several heterogeneous data stores depending on the type of data they have to manage: relational and NoSQL (i.e., document, graph, key-value, and column) data stores. Interacting with heterogeneous data models via different APIs and query languages imposes challenging tasks to the developers of multiple data stores applications. Indeed, the execution of complex queries over heterogeneous data models cannot, currently, be achieved in a declarative way as it is used to be with single data store application, and therefore requires extra implementation efforts. In this paper we propose a mediation based component to optimize and execute complex queries over multiple data stores in Cloud environments. This component is referred to as virtual data store (VDS). The key ingredients of our solution are (1) a simple global schema describing the different data sources and their relationships, (2) a cost model to evaluate the cost of the operations, (3) an inter data stores parallelism execution model, and (4) a dynamic programming based approach to generate optimal execution plan. Quantitative and qualitative experiments are conducted to validate our approach.

Complex Queries Optimization and Evaluation over Relational and NoSQL Data Stores in Cloud Environments

Continuous and unforeseeable evolution of business rules, processing logics, and organizational structures within enterprises, requires from business process management systems to integrate continuous design Supporting business process rediscovery based on workflow logs analysis, workflow mining gathers retroactive (re)design techniques necessary to understand business process execution reality Most of the works in this area focus on the control flow perspective, while very few of them address the organizational aspect In addition, the used workflow mining techniques suffer from the lack of automation due to the purely syntactic logs In this paper, we propose an Organizational Ontology (OrO) specifying the organizational model This ontology is used to semantically annotate log files and establish an organizational knowledge base that will be used to discover relationships between performers in a workflow Our approach has been implemented within the ProM framework

An Ontology for Workflow Organizational Model Mining

Cloud computing has recently emerged as a new computing paradigm. This latter provides provisioning of dynamically scalable and often virtualized resources which are offered as services. In this context, there is a services pool that supports platforms and mechanisms in order to guarantee the applications development and execution. This set of services is called platform as a service (PaaS). One of the main goals of the PaaS is to support large data stores by ensuring elasticity, scalability and portability. Many applications have to manage different types of data that a single store can not efficiently support. Consequently, clouds need to deploy multiple data stores, allowing applications to choose those corresponding to their data requirements. In this paper, we present and discuss the requirements of such environments and analyze current state of the art.

Rami Sellami

Papers

ODBAPI: A Unified REST API for Relational and NoSQL Data Stores

Supporting Multi Data Stores Applications in Cloud Environments

Complex Queries Optimization and Evaluation over Relational and NoSQL Data Stores in Cloud Environments

An Ontology for Workflow Organizational Model Mining

Using Multiple Data Stores in the Cloud: Challenges and Solutions