The data-center is considered the heart of cloud computing. Recently, the growing demand for cloud computing services has caused a growing load on data centers. In terms of system behavior and workload, patterns of cloud computing are very dynamic; and that might serve to imbalance the load among data center resources. Eventually, some data-center resources could come to be over-loaded/under-loaded, which leads to an increase in energy consumption in addition to decreased functioning and wastage of resources. Just considering energy-efficiency (that can be attained efficiently by consolidate the servers) may not be enough for real applications because it may cause problems such as unbalanced load for each Physical Machine (PM). Therefore, this paper surveys published load balancing algorithms that achieved by server consolidation via a meta-analysis. Load balancing with server consolidation enriches the exploitation of resource utilization and can enhance Quality of Service (QoS) metrics, since data-centers and their applications are increasing exponentially. This meta-study, reviews the literature on load balancing and server consolidation and presents a ready reference taxonomy on the most efficient algorithms that achieve load balancing and server consolidation. This work attempts to present a taxonomy with a new classification for load balancing and server consolidation, such as migration overhead, hardware threshold, network traffic, and reliability.

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Load Balancing and Server Consolidation in Cloud Computing Environments: A Meta-Study

The classification of the most used load balancing algorithms in distributed systems (including cloud technology, cluster systems, grid systems) is described. Comparative analysis of types of the load balancing algorithms is conducted in accordance with the classification, the advantages and drawbags of each type of the algorithms are shown. Performance indicators characterizing each algorithm are indicated.

Survey of Major Load Balancing Algorithms in Distributed System

The prominent technology that drives the industry now-a-days is cloud computing. The growth of cloud computing has resulted in the setup of large number of data centers around the world. The data centers consume more power making it source for the carbon dioxide emission. The cloud computing allows users to utilize and access resources from the data centers when they demand rather than owning it. Load balancing is challenging when the workload is dynamic. A two level scheduling is proposed for load balancing taking into consideration the energy efficiency, power consumption and effective resource utilization satisfying the Quality of Service (QoS) service for the consumers with reduced SLA violation. In the first level of scheduling main focus is on the Task scheduling to VMs in which the technique of genetic algorithm and particle swarm optimization (PSO) is combined, a hybrid approach. The second level includes the placement of Virtual Machines to suitable Physical Machines by using Power aware best-fit algorithm in which the number of active servers are reduced and energy consumption is reduced. The paper focuses on study of load balancing and VM placement techniques.

Energy efficient strategy for task allocation and VM placement in cloud environment

Nature inspired computing has been widely used to solve various research challenges of mobile network. Mobile network refers to mobile network, sensor network and ad hoc network. This chapter has focused on the application of nature inspired computing in mobile network. In this chapter, the bio-inspired techniques for wireless sensor network, mobile ad hoc network and mobile cloud computing are discussed. Ant colony optimization is used in sensor network and mobile cloud computing for efficient routing and scheduling respectively. Bee swarm intelligence is used to develop routing schemes for mobile ad hoc network. Bird flocking behavior is used for congestion control in wireless sensor network. The research challenges of bio-inspired mobile network are also illustrated.

Natural Computing in Mobile Network Optimization

In the contemporary realm of operation in the World Wide Web, the cloud applications have to meet the high availability, operations on dynamic loads and must function at a very high quality. Load balancing is a proper technique to achieve above goals to distribute the loads across the server or to the various resources. This distribution optimizes performance, scalability, reliability and resources to be used, hence making the network to be appealing to bigger organizations to adopt it. The advantages because of load balancing are reduced response time, increased throughput and fault tolerance because the network traffic load is spread evenly among different servers thereby avoiding the occurrence of a bottleneck server. Some of these ways include Round Robin, Least Connection, and Weighted Distribution, help in the process by making it possible for systems to horizontally scale up for more traffic. As cloud services becomes popular, it becomes very important to manage the load since some applications or services may be more resource intensive than others. It distributes the incoming requests following the current loads on various servers and consequently, no server is too busy or idle. Also, load balancers provide for fault tolerance in the sense that traffic will be handled by other healthy servers in the occurrence of failures hence the solutions are highly available and reliable. Load balancing algorithms such from the basic rule-dependent to complex reinforcement learning algorithms are very crucial with respect to allocation of resources, energy management, and stability of the system. These algorithms will be increasingly important as cloud environments develop further in the efficiency of delivering services and the managing of resources needed, benefiting both the economy and the environment. INDEX TERMS: Performance, Scalability, Response Times, Latency, Horizontal Scaling, Load Balancing 

Optimizing Cloud Application Performance: A Survey on Load Balancing Techniques

Cloud computing has proposed a new perspective for provisioning the large-scale computing resources by using virtualization technology and a pay-per-use cost model. Load balancing is taken into account as a vital part for parallel and distributed systems. It helps cloud computing systems by improving the general performance, better computing resources utilization, energy consumption management, enhancing the cloud services’ QoS, avoiding SLA violation and maintaining system stability through distribution, controlling and managing the system workloads. In this paper we study the necessary equirements and considerations for designing and implementing a suitable load balancer for cloud environments. In addition we represent a complete survey of current proposed cloud load balancing solutions which according to our classification, They can be classified into three categories: General Algorithm-based, Architectural-based and Artificial Intelligence-based load balancing mechanisms. Finally, we propose our evaluation of these solutions based on Suitable metrics and discuss their pros and cons. Index Terms—Cloud Computing, Load Balancing, Distributed Systems, Virtual Machine.

Load balancing in cloud computing

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Load balancing in cloud computing