A Big Data-Enabled Consolidated Framework for Energy Efficient Software Defined Data Centers in IoT Setups
Bibliographic record
Abstract
The rapidly evolving industry standards and transformative advances in the field of Internet of Things are expected to create a tsunami of Big Data shortly. This, in turn, will demand real-time data analysis and processing from cloud computing platforms. A substantial part of the computing infrastructure is supported by large-scale and geographically distributed data centers (DCs). Nevertheless, these DCs impose a substantial cost in terms of rapidly growing energy consumption, which in turn adversely affects the environment. In this context, efficient resource utilization is seen as a potential candidate to enhance energy efficiency and minimize the load on the power sector. Nevertheless, in the majority of the public clouds, the resources are idle most of the time (i.e., under-utilized) as the load of the servers is unpredictable; thereby leading to a lofty increase in the energy utilization index and wastage of resources. Thus, it is highly essential to devise a precise and efficient resource management technique. Therefore, in this article, we leverage the advantages of software defined data centers (SDDCs) to minimize energy utilization levels. Precisely, SDDC refers to the process of programmatically abstracting the logical computing, network, and storage resources; and configuring them in real-time based on workload demands. In detail, we demonstrate the possibility of 1) designing a consolidated SDDC-based model to jointly optimize the process of virtual machine (VM) deployment and network bandwidth allocation for reduced energy consumption and guaranteed quality of service (QoS), particularly for heterogeneous computing infrastructures; 2) formulating a multiobjective optimization problem to deduce the optimal allocation of resources for both critical and noncritical applications; and 3) designing an efficient scheme based on heuristics to provide suboptimal results for the formulated multiobjective optimization problem. The proposed article presents a suboptimal approach based on first fit decreasing algorithm. Further, our empirical evaluations suggest that the proposed framework leads to almost 27.9% savings in terms of energy consumptions against the existing schemes with negligible QoS violations (approximately 0.33).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".