Energy efficiency has a rising importance throughout society. With the growth of large data centers, the energy consumption becomes centralized and nowadays takes a significant amount of the overall electricity consumption of a country. Load balancing algorithms are able to make an existing infrastructure more efficient without major drawbacks. This structured literature research presents the state of the art technology regarding the load balancing approach to make data centers more en-ergy efficient. The state of the art approaches are reviewed for techniques, im-provements and consideration of performance effects.
Table of Contents
1 Introduction
2 Load Balancing in Cloud Environments
3 Search Strategy and Review Protocol
3.1 Research Goals
3.2 Search Term
3.3 Source Databases
3.4 Selection and Refinement Process
4 Results
4.1 First Refinement
4.2 Second Refinement
5 Discussion
5.1 RQ1
5.2 RQ2
5.3 RQ3
5.4 Current Status of the Research
5.5 Load Balancing Algorithm Architecture
6 Limitations
7 Conclusion and Further Work
Research Objectives and Topics
This paper aims to provide a structured literature review on state-of-the-art load balancing algorithms designed to improve energy efficiency in modern cloud data centers. The primary objective is to analyze existing techniques, their measurement criteria, and their impact on system performance and reliability.
- Energy efficiency in large-scale data centers
- Load balancing techniques in cloud environments
- Performance and service level agreement (SLA) trade-offs
- Methodologies for measuring energy savings
- Future trends in energy-aware resource management
Excerpt from the Book
2 Load Balancing in Cloud Environments
Current cloud environments work on virtualized machines (VMs). This requires each heavy-duty physical server to power multiple VMs at the same time. The VMs can have different configurations and service level agreements (SLAs) depending on the customer. Mainly, three abilities make it possible to save energy:
• overallocation,
• live migration,
• shutting down servers, depending on the overall data center load.
Overallocation is the transfer of the overbooking principle, commonly practiced in the hotel and airline industry, to the IT industry [3]. In the data center context it means that the placed VMs on the physical server have more resources reserved than the server actually has available. For example, the server has 24 GB of RAM and three VMs with 12 GB of RAM configuration are currently running on this machine. This is only possible as long as the VMs do not need their full RAM capacity, since the server only has 24 GB and not the required 36 GB. However, the load for one or multiple VMs could change at any time and increase the RAM demand. The data center owner wants to avoid a potentially costly SLA violation. Thus, one of the VMs should run on a different server.
Summary of Chapters
1 Introduction: Discusses the growing importance of energy efficiency in data centers due to increasing electricity demands and operational costs.
2 Load Balancing in Cloud Environments: Introduces core concepts like virtualization, overallocation, and live migration as methods to reduce energy consumption.
3 Search Strategy and Review Protocol: Outlines the systematic methodology used for the literature review, including research questions and selection criteria.
4 Results: Presents the categorized findings from the selected publications based on techniques and performance evaluation methods.
5 Discussion: Analyzes the research results, addressing the formulated research questions and proposing a general design architecture for load balancing algorithms.
6 Limitations: Acknowledges the constraints of the study, such as the limited number of source databases and the exclusion of non-scientific industry reports.
7 Conclusion and Further Work: Summarizes the state of research and suggests the development of a fully-functional prototype as the next step for the field.
Keywords
energy efficiency, load balancing, cloud computing, data center, virtualization, overallocation, live migration, resource management, SLA violation, bin-packing, energy consumption, green IT, performance evaluation, algorithm architecture, consolidation
Frequently Asked Questions
What is the core focus of this research paper?
This paper focuses on investigating how load balancing algorithms can be utilized to improve the energy efficiency of cloud data centers without compromising system performance.
What are the primary thematic areas explored?
The paper covers virtualization, energy reduction techniques, performance monitoring, service level agreements (SLAs), and the development of energy-aware algorithmic designs.
What is the main research question addressed?
The study seeks to identify what techniques are used to improve energy efficiency, how these improvements are measured, and whether they negatively affect overall system reliability or performance.
Which scientific methodology was applied?
The author conducted a structured literature review, applying specific inclusion and exclusion criteria to select 28 relevant academic articles from the ACM Digital Library and IEEE Xplore.
What does the main body of the work examine?
It examines existing load balancing strategies, analyzes how they are evaluated through simulations, and identifies current weaknesses, such as the lack of performance interference consideration.
Which keywords characterize this study?
Key terms include energy efficiency, load balancing, cloud computing, virtualization, live migration, and resource management.
How does overallocation impact data center energy use?
Overallocation allows for higher resource utilization by reserving more virtual resources than are physically available, thus reducing the number of active servers and energy consumption, provided VMs do not demand full capacity simultaneously.
Why do most researchers use simulations instead of real-world experiments?
Analytical proofs are often too complex, and experiments in real-world production data centers are considered too risky and disruptive to ongoing operations.
- Quote paper
- Norman Peitek (Author), 2013, Algorithms for Energy Efficient Load Balancing in Cloud Environments, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/286584
