All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey

With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.Comment: 48 pages, 7 tables, 11 figures, 450 references. The data (categories and features/objectives of the papers) of this survey are now available publicly. Accepted by Elsevier Journal of Systems Architectur

Performance-Aware Management of Cloud Resources: A Taxonomy and Future Directions

Dynamic nature of the cloud environment has made distributed resource management process a challenge for cloud service providers. The importance of maintaining the quality of service in accordance with customer expectations as well as the highly dynamic nature of cloud-hosted applications add new levels of complexity to the process. Advances to the big data learning approaches have shifted conventional static capacity planning solutions to complex performance-aware resource management methods. It is shown that the process of decision making for resource adjustment is closely related to the behaviour of the system including the utilization of resources and application components. Therefore, a continuous monitoring of system attributes and performance metrics provide the raw data for the analysis of problems affecting the performance of the application. Data analytic methods such as statistical and machine learning approaches offer the required concepts, models and tools to dig into the data, find general rules, patterns and characteristics that define the functionality of the system. Obtained knowledge form the data analysis process helps to find out about the changes in the workloads, faulty components or problems that can cause system performance to degrade. A timely reaction to performance degradations can avoid violations of the service level agreements by performing proper corrective actions including auto-scaling or other resource adjustment solutions. In this paper, we investigate the main requirements and limitations in cloud resource management including a study of the approaches in workload and anomaly analysis in the context of the performance management in the cloud. A taxonomy of the works on this problem is presented which identifies the main approaches in existing researches from data analysis side to resource adjustment techniques

Determinants of a Successful Migration to Cloud Computing in Iranian Telecommunication Industry

Although the research support for Cloud Computing (CC) is still developing, the concept of this paper has provided comprehensive frameworks for a successful migration to cloud computing (SMCC) in telecommunication industry in Iran. Using an academic orientation, the conceptual research is focusing on the determinants of a successful migration from legacy to cloud computing. The study attempts to reveal the constructive effects and deconstructive defects of migration to cloud computing with a close regard into prior literature and practical practices all around the world. Conceptual frameworks are deducted from the literature and Telco's revolutionary movements toward cloud computing. The confirmatory quantitative approach tries to verify or reject the validity of determinants of successful migration. The study reports that there are some success and failure factors which are influencing a successful migration of data centres and servers of Iranian Telecommunication to the cloud. Considering these approved factors before any migration decision would be valuable for engaged project members and finally for Telecommunication organization. This paper can be used as reliable model for any migration beforehand taking any action. Enforcing proper determinants in accordance with the prior success stories and academia viewpoints in Telecommunication industry in Iran as a first mover research in this field provides a precious insight for policy and decision makers to change their mindset and grant a proper space for cloud computing to grow in this industry due to its advantages. Obviously, like any other big Telco in the world, Iranian Telco might start cloud projects to sustain its presence in the global market.Comment: 7 page

Recent Developments in Cloud Based Systems: State of Art

Cloud computing is the new buzzword in the head of the techies round the clock these days. The importance and the different applications of cloud computing are overwhelming and thus, it is a topic of huge significance. It provides several astounding features like Multitenancy, on demand service, pay per use etc. This manuscript presents an exhaustive survey on cloud computing technology and potential research issues in cloud computing that needs to be addressed

Security and Privacy Aspects in MapReduce on Clouds: A Survey

MapReduce is a programming system for distributed processing large-scale data in an efficient and fault tolerant manner on a private, public, or hybrid cloud. MapReduce is extensively used daily around the world as an efficient distributed computation tool for a large class of problems, e.g., search, clustering, log analysis, different types of join operations, matrix multiplication, pattern matching, and analysis of social networks. Security and privacy of data and MapReduce computations are essential concerns when a MapReduce computation is executed in public or hybrid clouds. In order to execute a MapReduce job in public and hybrid clouds, authentication of mappers-reducers, confidentiality of data-computations, integrity of data-computations, and correctness-freshness of the outputs are required. Satisfying these requirements shield the operation from several types of attacks on data and MapReduce computations. In this paper, we investigate and discuss security and privacy challenges and requirements, considering a variety of adversarial capabilities, and characteristics in the scope of MapReduce. We also provide a review of existing security and privacy protocols for MapReduce and discuss their overhead issues.Comment: Accepted in Elsevier Computer Science Revie

Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges

For various reasons, the cloud computing paradigm is unable to meet certain requirements (e.g. low latency and jitter, context awareness, mobility support) that are crucial for several applications (e.g. vehicular networks, augmented reality). To fulfil these requirements, various paradigms, such as fog computing, mobile edge computing, and mobile cloud computing, have emerged in recent years. While these edge paradigms share several features, most of the existing research is compartmentalised; no synergies have been explored. This is especially true in the field of security, where most analyses focus only on one edge paradigm, while ignoring the others. The main goal of this study is to holistically analyse the security threats, challenges, and mechanisms inherent in all edge paradigms, while highlighting potential synergies and venues of collaboration. In our results, we will show that all edge paradigms should consider the advances in other paradigms.Comment: In press, accepted manuscript: Future Generation Computer System

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence

Along with the rapid developments in communication technologies and the surge in the use of mobile devices, a brand-new computation paradigm, Edge Computing, is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications are thriving with the breakthroughs in deep learning and the many improvements in hardware architectures. Billions of data bytes, generated at the network edge, put massive demands on data processing and structural optimization. Thus, there exists a strong demand to integrate Edge Computing and AI, which gives birth to Edge Intelligence. In this paper, we divide Edge Intelligence into AI for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial Intelligence on Edge). The former focuses on providing more optimal solutions to key problems in Edge Computing with the help of popular and effective AI technologies while the latter studies how to carry out the entire process of building AI models, i.e., model training and inference, on the edge. This paper provides insights into this new inter-disciplinary field from a broader perspective. It discusses the core concepts and the research road-map, which should provide the necessary background for potential future research initiatives in Edge Intelligence.Comment: 13 pages, 3 figure

Mobile Cloud Business Process Management System for the Internet of Things: A Survey

The Internet of Things (IoT) represents a comprehensive environment that consists of a large number of smart devices interconnecting heterogeneous physical objects to the Internet. Many domains such as logistics, manufacturing, agriculture, urban computing, home automation, ambient assisted living and various ubiquitous computing applications have utilised IoT technologies. Meanwhile, Business Process Management Systems (BPMS) have become a successful and efficient solution for coordinated management and optimised utilisation of resources/entities. However, past BPMS have not considered many issues they will face in managing large scale connected heterogeneous IoT entities. Without fully understanding the behaviour, capability and state of the IoT entities, the BPMS can fail to manage the IoT integrated information systems. In this paper, we analyse existing BPMS for IoT and identify the limitations and their drawbacks based on Mobile Cloud Computing perspective. Later, we discuss a number of open challenges in BPMS for IoT.Comment: 56 pages, 10 figures, 5 table

High Energy Physics Forum for Computational Excellence: Working Group Reports (I. Applications Software II. Software Libraries and Tools III. Systems)

Computing plays an essential role in all aspects of high energy physics. As computational technology evolves rapidly in new directions, and data throughput and volume continue to follow a steep trend-line, it is important for the HEP community to develop an effective response to a series of expected challenges. In order to help shape the desired response, the HEP Forum for Computational Excellence (HEP-FCE) initiated a roadmap planning activity with two key overlapping drivers -- 1) software effectiveness, and 2) infrastructure and expertise advancement. The HEP-FCE formed three working groups, 1) Applications Software, 2) Software Libraries and Tools, and 3) Systems (including systems software), to provide an overview of the current status of HEP computing and to present findings and opportunities for the desired HEP computational roadmap. The final versions of the reports are combined in this document, and are presented along with introductory material.Comment: 72 page

Programming Cloud Resource Orchestration Framework: Operations and Research Challenges

The emergence of cloud computing over the past five years is potentially one of the breakthrough advances in the history of computing. It delivers hardware and software resources as virtualization-enabled services and in which administrators are free from the burden of worrying about the low level implementation or system administration details. Although cloud computing offers considerable opportunities for the users (e.g. application developers, governments, new startups, administrators, consultants, scientists, business analyst, etc.) such as no up-front investment, lowering operating cost, and infinite scalability, it has many unique research challenges that need to be carefully addressed in the future. In this paper, we present a survey on key cloud computing concepts, resource abstractions, and programming operations for orchestrating resources and associated research challenges, wherever applicable.Comment: 19 page