Load-balancing algorithms in cloud computing: A survey

Abstract

Cloud computing is a modern paradigm to provide services through the Internet. Load balancing is a key aspect of cloud computing and avoids the situation in which some nodes become overloaded while the others are idle or have little work to do. Load balancing can improve the Quality of Service (QoS) metrics, including response time, cost, throughput, performance and resource utilization.

In this paper, we study the literature on the task scheduling and load-balancing algorithms and present a new classification of such algorithms, for example, Hadoop MapReduce load balancing category, Natural Phenomena-based load balancing category, Agent-based load balancing category, General load balancing category, application-oriented category, network-aware category, and workflow specific category. Furthermore, we provide a review in each of these seven categories. Also. We provide insights into the identification of open issues and guidelines for future research.

Introduction

Cloud computing is a modern technology in the computer field to provide services to clients at any time. In a cloud computing system, resources are distributed all around the world for faster servicing to clients (Apostu et al., 2013, Dasgupta et al., 2013). The clients can easily access information via various devices such as laptops, cell phones, PDAs, and tablets. Cloud computing has faced many challenges, including security, efficient load balancing, resource scheduling, scaling, QoS management, data center energy consumption, data lock-in and service availability, and performance monitoring (Kaur et al., 2014; Malladi et al., 2015). Load balancing is one of the main challenges and concerns in cloud environments;(Jadeja and Modi, 2012) it is the process of assigning and reassigning the load among available resources in order to maximize throughput, while minimizing the cost and response time, improving performance and resource utilization as well as energy saving (Singh et al., 2016; Goyal et al., 2016). Service Level Agreement (SLA) and user satisfaction could be provided by excellent load balancing techniques. Therefore, providing the efficient load-balancing algorithms and mechanisms is a key to the success of cloud computing environments. Several researches have been done in the field of load balancing and task scheduling in cloud environments. However, our studies showed that despite the key role of load-balancing algorithms in cloud computing, especially in the advent of big data, there are a few comprehensive reviews of these algorithms. First, we mention a few recent papers that have reviewed the load-balancing algorithms and mechanisms in cloud environments:

• Milani and Navimipour (2016) have presented a systematic review of the existing load balancing techniques. They classified the existing techniques based on different parameters. The authors compared some popular load-balancing algorithms and presented their main properties, including their advantages and disadvantages. They also addressed the challenges of these algorithms and mentioned the open issues. However, their work lacks a discussion regarding the load balancing and task scheduling techniques in Hadoop MapReduce that is an issue nowadays.

• Mesbahi and Rahmani (2016) have studied state of the art load balancing techniques and the necessary requirements and considerations for designing and implementing suitable load-balancing algorithms for cloud environments. They presented a new classification of load balancing techniques, evaluated them based on suitable metrics and discussed their pros and cons. They also found that the recent load balancing techniques are focusing on energy saving. However, their work suffers from the lack of simulating the load balancing techniques by simulator tools; in addition, a discussion of open issues and future topics that researchers should focus on is also missing.

• Kanakala and Reddy, 2015a, Kanakala and Reddy, 2015b have analyzed the performance of load balancing techniques in cloud computing environments. They studied several popular load-balancing algorithms and compared them based on metrics such as throughput, speed, complexity, etc. They concluded that none of the reviewed algorithms were able to perform well in all the required areas of load balancing. However, they did not mention the current trend, future works, and open issues in the field of load balancing in cloud environments.

• Ivanisenko and Radivilova (2015) have studied major load-balancing algorithms in distributed systems. They classified the most used load-balancing algorithms in distributed systems, including cloud technology, cluster systems, and grid systems. They also presented a comparative analysis of different load-balancing algorithms on various efficiency indicators such as throughput, migration time, response time, etc. In their work, a description of the main features of load-balancing algorithms, analysis of their advantages, and defaults of each type of algorithms is also presented. Nevertheless, a discussion of challenges, open issues, and future trends is similarly missing.

• Farrag and Mahmoud (2015) have reviewed intelligent cloud algorithms for load balancing problems, including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). They also proposed an implementation of Ant Lion Optimizer (ALO) based cloud computing environment as an efficient algorithm, which was expected to supply the outcomes in load balancing. The authors found that these algorithms showed a better performance than traditional ones in terms of QoS, response time, and makespan. However, they did not evaluate their proposed algorithm in different scales of cloud systems by comparing its results.

To help the future researchers in the field of load balancing in designing novel algorithms and mechanisms, we surveyed the literature and analyzed state of the art mechanisms. Therefore, the purpose of this paper is to survey the existing techniques, describe their properties, and clarify their pros and cons. The main goals of this paper are as follows:

• Studying the existing load balancing mechanisms

• Providing a new classification of load balancing mechanisms

• Clarifying the advantages and disadvantage of the load-balancing algorithms in each class

• Outlining the key areas where new researches could be done to improve the load-balancing algorithms

The rest of the paper is organized as follows. Section 2 provides a literature review for the model, metrics, policies, and taxonomy of load-balancing algorithms. Challenges in cloud-based load-balancing algorithms are explained in Section 3. Section 4 provides a relatively comprehensive review of literature on the existing load balancing techniques and presents a new classification. Section 5 provides a discussion of the mentioned techniques and some useful statistics. Open issues are outlined in Section 6. Finally, in Section 7, we conclude our survey and provide future topics.