Cloud Computing and Distributed Systems for Big Data
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 15 April 2026 | Viewed by 18
Special Issue Editor
Interests: distributed systems; blockchain; web3; edge AI
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The last decade has witnessed an unprecedented explosion in data volume, velocity, and variety. From scientific instruments and IoT fleets to social media and enterprise transactions, global data creation is expected to surpass 180 ZB by 2025. Turning this deluge into actionable insight demands scalable, elastic, and cost-effective computing infrastructures. Cloud computing—with its virtually unlimited pools of compute, storage, and network resources—has become the de facto host for big-data analytics. Simultaneously, modern distributed systems (spanning geo-distributed data centers, edge–cloud continua, and serverless fabrics) are evolving to meet stringent requirements of low latency, high throughput, energy efficiency, privacy, and regulatory compliance.
This Special Issue invites original research and experience papers that advance the state of the art in converging cloud computing and distributed systems technologies to harness big data. We seek contributions that not only propose novel algorithms, architectures, and frameworks, but also provide reproducible artifacts, open data sets, and real-world deployments that validate their effectiveness at scale.
We welcome theoretical, experimental, and systems contributions that address, but are not limited to, the following challenges:
- Scalable storage and processing architectures for multi-modal big data (structured, semi-structured, unstructured);
- Resource management, scheduling, and elasticity in cloud–edge–IoT environments;
- Serverless and micro-services paradigms for data-intensive applications;
- Distributed machine-learning systems, including parameter servers, federated learning, and distributed deep learning;
- Data lakes, lakehouses, and federated query engines across heterogeneous clouds;
- Green and energy-efficient big-data processing (carbon-aware scheduling, renewable-powered DCs);
- Security, privacy, and trust (confidential computing, differential privacy, secure multi-party computation);
- Blockchain-empowered distributed ledgers for verifiable data sharing and auditing.
Dr. Shan Jiang
Guest Editor
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 submissions that pass pre-check are 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. Electronics 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 2400 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.
Keywords
- cloud computing
- distributed systems
- big data
- blockchain
- distributed machine learning
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