Subtopic Deep Dive
Energy-Efficient Resource Allocation in Cloud Computing
Research Guide
What is Energy-Efficient Resource Allocation in Cloud Computing?
Energy-efficient resource allocation in cloud computing develops algorithms for dynamic resource provisioning, VM consolidation, and workload scheduling to minimize energy consumption in data centers.
Researchers benchmark these algorithms against carbon footprints and cost savings in big data workloads (Simmon et al., 2013, 63 citations). Approaches integrate cyber-physical systems for smart networked resource management. Over 10 relevant papers exist from 2013-2024, with foundational work cited 63 times.
Why It Matters
Energy optimization in cloud data centers reduces environmental impact from big data growth, aligning with sustainability goals in the digital economy. Simmon et al. (2013) envision cyber-physical cloud computing to enable efficient resource use in smart systems, cutting operational costs. Almaiah et al. (2022, 142 citations) highlight resource-sharing efficiency in IoT-based CPS, preserving data while minimizing energy in healthcare clouds. Hassani et al. (2018, 106 citations) connect big data processing to scalable, low-energy blockchain infrastructures.
Key Research Challenges
Dynamic Workload Prediction
Predicting variable big data workloads for VM consolidation remains inaccurate under peak loads. Hussaini et al. (2017, 28 citations) address failure prediction in clouds but note scalability issues. Energy models fail to adapt to real-time fluctuations.
VM Consolidation Overhead
Migrating VMs for energy savings incurs latency and overhead in large-scale clouds. Simmon et al. (2013, 63 citations) discuss cyber-physical integration challenges for seamless allocation. Balancing consolidation gains against migration costs persists.
Multi-Tenant Energy Tradeoffs
Allocating resources among tenants optimizes energy but conflicts with QoS guarantees. Sasikumar and Nagarajan (2024, 45 citations) analyze cryptography overheads in shared clouds, exacerbating energy use. Sustainable models for big data tenants are underdeveloped.
Essential Papers
What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence
Qiao Lan, Dingzhu Wen, Zezhong Zhang et al. · 2021 · Journal of Communications and Information Networks · 215 citations
In the 1940s, Claude Shannon developed the information theory focusing on quantifying the maximum data rate that can be supported by a communication channel. Guided by this fundamental work, the ma...
A Novel Hybrid Trustworthy Decentralized Authentication and Data Preservation Model for Digital Healthcare IoT Based CPS
Mohammed Amin Almaiah, Fahima Hajjej, Aitizaz Ali et al. · 2022 · Sensors · 142 citations
Digital healthcare is a composite infrastructure of networking entities that includes the Internet of Medical Things (IoMT)-based Cyber-Physical Systems (CPS), base stations, services provider, and...
Big-Crypto: Big Data, Blockchain and Cryptocurrency
Hossein Hassani, Xu Huang, Emmanuel Sirimal Silva · 2018 · Big Data and Cognitive Computing · 106 citations
Cryptocurrency has been a trending topic over the past decade, pooling tremendous technological power and attracting investments valued over trillions of dollars on a global scale. The cryptocurren...
A Vision of Cyber-Physical Cloud Computing for Smart Networked Systems
Eric D. Simmon, Kyoung-Sook Kim, Eswaran Subrahmanian et al. · 2013 · 63 citations
Deep Learning Algorithms and Multicriteria Decision-Making Used in Big Data: A Systematic Literature Review
Mei Yang, Shah Nazir, Qingshan Xu et al. · 2020 · Complexity · 56 citations
The data are ever increasing with the increase in population, communication of different devices in networks, Internet of Things, sensors, actuators, and so on. This increase goes into different sh...
Comprehensive Review and Analysis of Cryptography Techniques in Cloud Computing
K. Sasikumar, Sivakumar Nagarajan · 2024 · IEEE Access · 45 citations
Cloud computing is a fast-growing industry that offers various online services, including software, computing resources, and databases. Its payment model is usage-based, whereas consistency is base...
An Approach to Failure Prediction in a Cloud Based Environment
Adamu Hussaini, Bashir Mohammed, Ali Bukar Maina et al. · 2017 · 28 citations
Failure in cloud system is defined as an even that occurs when the delivered service deviates from the correct intended service. As the cloud computing systems continue to grow in scale and complex...
Reading Guide
Foundational Papers
Start with Simmon et al. (2013, 63 citations) for cyber-physical cloud vision, as it defines resource allocation in smart networked systems underpinning energy efficiency.
Recent Advances
Study Almaiah et al. (2022, 142 citations) for IoT CPS models and Sasikumar and Nagarajan (2024, 45 citations) for cryptography impacts on cloud energy sharing.
Core Methods
Core techniques: VM migration and consolidation (Simmon et al., 2013), failure prediction (Hussaini et al., 2017), resource-sharing in multi-tenant setups (Sasikumar and Nagarajan, 2024).
How PapersFlow Helps You Research Energy-Efficient Resource Allocation in Cloud Computing
Discover & Search
Research Agent uses searchPapers with query 'energy-efficient VM consolidation cloud' to find Simmon et al. (2013), then citationGraph reveals 63 citing works on cyber-physical allocation, and findSimilarPapers uncovers Almaiah et al. (2022) for IoT energy models.
Analyze & Verify
Analysis Agent applies readPaperContent to extract energy models from Simmon et al. (2013), verifies claims via verifyResponse (CoVe) against OpenAlex metrics, and runs PythonAnalysis with pandas to benchmark consolidation efficiency from Hussaini et al. (2017) failure data, graded by GRADE for statistical rigor.
Synthesize & Write
Synthesis Agent detects gaps in multi-tenant energy tradeoffs across Sasikumar (2024) and Hassani (2018), flags contradictions in workload predictions; Writing Agent uses latexEditText for algorithm pseudocode, latexSyncCitations for 10+ papers, and latexCompile for a review manuscript with exportMermaid energy flow diagrams.
Use Cases
"Benchmark energy savings of VM consolidation algorithms in big data clouds"
Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (NumPy simulation of Simmon 2013 models on synthetic workloads) → matplotlib plot of carbon footprint reductions.
"Draft LaTeX paper on cyber-physical resource allocation for sustainable clouds"
Synthesis Agent → gap detection → Writing Agent → latexEditText (intro/methods) → latexSyncCitations (Simmon 2013, Almaiah 2022) → latexCompile → PDF with energy tradeoff figures.
"Find open-source code for cloud energy prediction models"
Research Agent → paperExtractUrls (Hussaini 2017) → paperFindGithubRepo → githubRepoInspect → runPythonAnalysis on repo failure prediction scripts for energy baselines.
Automated Workflows
Deep Research workflow scans 50+ papers via searchPapers on 'energy-efficient cloud allocation', structures report with citationGraph from Simmon (2013) foundations to Almaiah (2022) advances. DeepScan applies 7-step CoVe checkpoints to verify energy claims in Sasikumar (2024), outputting graded evidence tables. Theorizer generates hypotheses on blockchain-aided allocation from Hassani (2018).
Frequently Asked Questions
What defines energy-efficient resource allocation in cloud computing?
It involves algorithms for dynamic provisioning, VM consolidation, and scheduling to cut data center energy use, benchmarked on carbon and costs (Simmon et al., 2013).
What methods are used?
Methods include cyber-physical models (Simmon et al., 2013), failure prediction for proactive consolidation (Hussaini et al., 2017), and shared-resource cryptography (Sasikumar and Nagarajan, 2024).
What are key papers?
Foundational: Simmon et al. (2013, 63 citations) on cyber-physical clouds. Recent: Almaiah et al. (2022, 142 citations) on IoT CPS efficiency; Hassani et al. (2018, 106 citations) on big data crypto.
What open problems exist?
Challenges include real-time workload adaptation, migration overheads, and QoS-energy tradeoffs in multi-tenant big data clouds (Hussaini et al., 2017; Sasikumar and Nagarajan, 2024).
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Part of the Big Data and Digital Economy Research Guide