Special Issue "Machine Learning and Data Analytics for Edge Cloud Computing"
Deadline for manuscript submissions: 31 August 2021.
Interests: cybersecurity; computer networks; wireless networks; information-centric networking and software-defined networking; machine intelligence
Interests: machine learning; Ad-Hoc networks; cybersecurity; 5G technologies; blockchain
Interests: blockchain technology; cryptography; big data; data mining; social networks; security and privacy; anonymity and graphs
Interests: network traffic analysis; network fault detection; classification network fault and abnormality and Machine learning in the area of computer networking and network security
Cloud computing, as well as cloud-inspired business models, enables on-demand access to a shared pool of resources, namely computing, storage, networks, services, and applications. With the advent of cloud-based systems, cloud operators have been aiming at a reliable, secured, privacy-preserving, and cost-efficient cloud design and management. As the cloud infrastructure aims at offering various IT resources as services, requirements of cloud applications vary based on the resources which are requested as services. Thus, the resources may refer to heavy computation resources, massive storage resources, high-capacity network resources, and so on. The heterogeneity of cloud applications leads to the challenge of the holistic design of a robust cloud system that can oversee and handle the diverse needs of numerous types of applications. On the other hand, the new computation technologies, such as big data analytics, machine learning, and blockchain, have a great influence on the cloud and network.
There are many driving forces behind the rising adoption of edge computing that favor the preparation and examination of information at the edge. Presently, the business has consolidated around seven essential needs that edge processing can meet, namely the requirements for low/ultra-low dormancy, reduced expense of transmission capacity, improved network speed, security, data sovereignty, reliability, and interoperability with legacy frameworks. One of the difficulties in thinking about when, where, why, and how to process and examine information is that ventures and merchants have various meanings of 'the edge' contingent, where information preparation is done right now, and (for sellers specifically) the relative information handling capacities of their current items and administrations. For modern undertakings that have generally handled and broken down most of their information in incorporated datacenters (either on-premises or in the cloud), whatever is not a focal datacenter could honestly be considered 'the edge'. Rather like early stargazers who examined Saturn with moderately powerless telescopes and saw that it was encompassed by a ring, their viewpoint of an edge is characterized by their distance from it. Later, space experts with more impressive telescopes obtained a closer vantage point from which they could recognize that Saturn was, truth be told, surrounded by different rings. Likewise, those with a vantage point that is closer to the edge perceive that it cannot be viewed as a solitary, unmistakable element; instead, it is a range of various edge gadgets. Machine learning (ML) has recently shown good results in a variety of domains, especially when large data quantities are available. It has great potential in the cloud context because it can carry out representation learning by transforming data into hierarchical abstract representations that enable learning good features.
This Special Issue aims to bring together researchers from academia, industry, and government agencies to understand the innovative technologies such as big data analytics, machine learning, and blockchain in the edge cloud paradigm. Submitted papers are expected to employ state-of-the-art and novel approaches to cover solutions for the edge cloud related to cost-effectiveness, sustainability problems, and other challenges. Potential topics include but are not limited to the following:
- Cloud computing system and network design;
- Cloud network protocol design and management;
- Optimization for cloud computing, networking, and applications;
- Green cloud system design;
- Cloud storage design and networking;
- Cloud system and storage security;
- Cloud network virtualization techniques;
- Modeling for cloud system, network, and storage;
- Performance analysis for cloud system, network, and storage;
- Big data storage and networking in the clouds;
- Intra-cloud computing and networking;
- Mobile cloud system design;
- Cloud media and storage design;
- Real-time resource reporting and monitoring for cloud management;
- Cloud system interoperability;
- Cloud data center design;
- Utility computing solutions in cloud systems;
- Cloud forensics;
- Networking for cloud computing;
- Machine learning and data mining for cloud computing;
- Edge, fog, and mobile edge computing;
- Security, privacy, and trust for cloud computing;
- Machine learning for cloud resource management;
- Machine learning for traffic engineering and congestion control;
- Machine learning for network measurement;
- Data-driven methodology and architecture;
- Networking for machine learning systems;
- Resource management and device placement for machine learning systems;
- Measurement and diagnosis for machine learning systems;
- Blockchain with cloud computing.
Dr. Uttam Ghosh
Dr. Sahil Verma
Dr. Gautam Srivastava
Prof. Dr. Mouhammd Alkasassbeh
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.