Scheduling Algorithm For Load Balancing Research Articles

Phase space load balancing priority scheduling algorithm for cloud computing clusters

Due to the development of new technologies such as the Internet and cloud computing, high requirements have been placed on the storage and management of big data. At the same time, new applications in the cloud computing environment also pose new requirements for cloud storage systems, such as strong scalability and high concurrency. Currently, the existing nosql database system is based on cloud computing virtual resources, supporting dynamic addition and deletion of virtual nodes. Based on the study of phase space reconstruction, the necessity of considering traffic flow as a chaotic time series is analyzed. In addition, offline data migration methods based on load balancing are also studied. Firstly, a data migration model is proposed through analysis, and the factors that affect migration performance are analyzed. Based on this, optimization objectives for migration are proposed. Then, the system design of data migration is presented, and optimization research is conducted from two aspects around the migration optimization objectives: optimizing from the data source layer, and proposing the LBS method to convert data sources into distributed data sources, ensuring the balanced distribution of data and meeting the scalability requirements of the system. This paper applies cloud computing technology and phase space reconstruction to load balancing scheduling algorithms to promote their development.

A novel approach for Credit-Based Resource Aware Load Balancing algorithm (CB-RALB-SA) for scheduling jobs in cloud computing

In recent years, cloud computing has gained popularity, mainly because of its utility and relevance to current technological trends. It is an arrangement that is highly customizable and encapsulated for providing better computational services to its clients worldwide. In the cloud, computing scheduling plays a pivotal role in optimizing resources. A better scheduling algorithm should be efficient and impartial, reducing the makespan time with proper resource utilization. However, most scheduling algorithms customarily lead to less resource utilization, termed load imbalance. The analysis of the existing papers exhibits better Makespan time but cannot guarantee the load-balanced mapping of jobs with proper resource utilization. Therefore, to eliminate the shortcomings of the prevalent/existing algorithms and enhance the performance, CB-RALB-SA, Credit-based Resource Aware Load Balancing scheduling algorithm has been rendered. The proposed work ensures a balanced distribution of tasks based on the capabilities of the resources, which eventually proves sustainable improvement against the existing scheduling algorithms. Therefore, a novel Credit Based Resource Aware Load Balancing Scheduling algorithm (CB-RALB-SA) is proposed. The tasks weighted by the credit-based scheduling algorithm are then mapped to the resources considering each resource’s load and computing capability using FILL and SPILL functions of Resource Aware and Load using Honey bee optimization heuristic algorithm. With the experimental evaluations and results, it has been proved that the proposed approach provides 48.5% better in Processing Time and 16.90 % better results in makespan time than the Existing CBSA-LB algorithm. Thus, it improves the processor’s efficiency while uplifting the whole system’s performance and has saved memory allocated to tasks and RAM.

Effective scheduling algorithm for load balancing in fog environment using CNN and MPSO

Effective scheduling algorithm for load balancing in fog environment using CNN and MPSO

Ant Colony Optimization-based Light Weight Container (ACO-LWC) Algorithm for Efficient Load Balancing

Container technology is the latest lightweight virtualization technology which is an alternate solution for virtual machines. Docker is the most popular container technology for creating and managing Linux containers. Containers appear to be the most suitable medium for use in dynamic development, packaging, shipping and many other information technology environments. The portability of the software through the movement of containers is appreciated by businesses and IT professionals. In the docker container, one or more processes may run simultaneously. The main objective of this work is to propose a new algorithm called Ant Colony Optimization-based Light Weight Container (ACO-LWC) load balancing scheduling algorithm for scheduling various process requests. This algorithm is designed such that it shows best performance in terms of load balancing. The proposed algorithm is validated by comparison with two existing load balancing scheduling algorithms namely, least connection algorithm and round robin algorithm. The proposed algorithm is validated using metrics like response time (ms), mean square error (MSE), node load, largest Transactions Per Second (TPS) of cluster (fetches/sec), average response time for each request (ms) and run time (s). Quantitative analysis show that the proposed ACO-LWC scheme achieves best performance in terms of all the metrics compared to the existing algorithms. In particular, the response time for least connection, round robin and the proposed ACO-LWC algorithm are 58, 60 and 48 ms respectively when 95% requests are finished. Similarly, the error for scheduling 120 requests using least connection, round robin and the proposed ACO-LWC algorithm are 0.15, 0.11 and 0.06 respectively.

Load-balancing scheduling of simulation tasks based on a static-dynamic hybrid algorithm

ABSTRACT A scheduling algorithm is crucial for running a simulation model so that tasks can be performed efficiently.The traditionally used blade-based parallel engine system cannot be adapted to a new simulation model. This study proposed a combined dynamic priority and static method, that is, a hybrid load-balancing scheduling (HLB) algorithm. The algorithm is given priority according to the operating cycle of the model and system steps. The experimental results demonstrated that the algorithm outperformed the earliest deadline first and the time-stepped load-balancing scheduling algorithms. The results also demonstrated that the HLBhad a higher real-time operating efficiency than the other algorithms under a lower overhead guarantee. The HLB algorithm causedbetter performance while maintaining computation and communication efficiency. Simultaneously, the utilisation rate of the central processing unit was around 35%.The further study should be enhanced to generalise it so that it could be applied to incorporate load balancing.

A Novel Load Balancing Scheduling Algorithm for Wireless Sensor Networks

Clustering is a well-known approach to cope with large nodes density and efficiently conserving energy in wireless sensor networks (WSN). Load balancing is an effective approach for optimizing resources like channel bandwidth, the main objective of this paper is to combine these two valuable approaches in order to significantly improve the main WSN service such as information routing. So, our proposal is a routing protocol in which load traffic is shared among cluster members in order to reduce the dropping probability due to queue overflow at some nodes. To this end, a novel hierarchical approach, called Distributed Energy efficient Adaptive Clustering Protocol (DEACP) with data gathering and Load-balancing is proposed. The DEACP approach aims to fulfill the following purposes: decreasing the overall network energy consumption, balancing the energy dissipation among the sensor nodes and as direct consequence: extending the lifetime of the network. In fact, the cluster-heads are optimally determined and suitably distributed over the area of interest allowing the member nodes reaching them with adequate energy dissipation and appropriate load balancing utilization. In addition, nodes radio are turned off for fixed time duration according to sleeping control rules optimizing so their energy consumption. The performance evaluation of the proposed protocol is carried out through the well-known NS2 simulator and the exhibited results are convincing. Like this, the residual energy of sensor nodes was measured every 20 s throughout the duration of simulation, in order to calculate the total number of alive nodes. Based on the simulation results, we concluded that our proposed DEACP protocol increases the profit of energy, and prolongs the network lifetime duration from 32 to 40% compared to DEEAC reference protocol and from 25 to 28% compared to FEMCHRP protocol. The authors also note that the proposed protocol is 41.7% better than DEEAC with respect to fist node die, and 25.5% better than FEMCHRP with respect to last node die while maintaining the average data transmission delay. We found also that DEACP achieved 66.5% and 40.6% more rounds than DEEAC and FEMCHRP respectively.

Programming Algorithms of load balancing with HA-Proxy in HTTP services

The access to the public and private services through the web gains daily protagonism, and sometimes they must support amounts of requests that a team can not process, so there are solutions that use algorithms that allow to distribute the load of requests of a web application in several equipment; the objective of this work is to perform an analysis of load balancing scheduling algorithms through the HA-Proxy tool, and deliver an instrument that identifies the load distribution algorithm to be used and the technological infrastructure, to largely cover implementation. The information used for this work is based on a bibliographic analysis, eld study and implementation of the different load balancing algorithms in equipment, where the distribution and its performance will be analyzed. The incorporation of this technology to the management of services on the web, improves availability, helps business continuity and through the different forms of distribution of the requests of the algorithms that can be implemented in HA-Proxy to provide those responsible for information technology systems with a view of their advantages and disadvantages.

Strategy optimization of resource scheduling based on cluster rendering

Job scheduling strategy of rendering was studied according to the scheduling model of heterogeneous cluster rendering system. Then, the work proposed the job scheduling strategy for heterogeneous cluster rendering as follows: Definition of load balancing measurement, influencing factors of model, construction of job scheduling model based on cluster rendering, and dynamic algorithm design based on genetic algorithm. The heterogeneous cluster rendering system achieved the effective scheduling algorithm for load balancing. The simulation results showed the algorithm had good load balancing. The work proposes the theoretical basis and new idea for resource scheduling of cluster system and cloud platform as well as the support for the effective use of resources. It has great significance.

An Online Load Balancing Algorithm for Virtual Machine Allocation with Fixed Process Intervals

To improve the performance and reliability of resource in Cloud data centers, it is significant to apply load balancing strategy for resource scheduling. In a dynamic changing traffic environment, workload of a Cloud data center has real-time and fixed process time characteristic. One of the challenging scheduling problems in Cloud datacenters is to take the real-time allocation and migration of reconfigurable virtual machines as well as the integrated features of hosting physical machines into consideration. In general, load-balance scheduling is NP-hard problem as proved in many open literatures. We introduce an Online Load-balance Resource Scheduling Algorithm (OLRSA) for Cloud datacenters considering realtime and multi-dimensional resources. Unlike traditional load balance scheduling algorithms which often do not consider lifecycle and fixed interval constraints, OLRSA treats life cycles and fixed intervals for both physical machines and virtual machines. We develop and apply integrated measurement for each server and a Cloud datacenter. New metrics such as integrated imbalance level, capacity makespan, capacity skew are defined for Cloud data centers. Simulation results show that OLRSA has better performance than a few related load balancing algorithms with regard to total imbalance level, makespan, overall load efficiency as well as capacity makespan.

Adaptive load balancing model based on prediction mechanism

Workload characteristics have an important impact on the performance of load balancing scheduling algorithms in Web server cluster systems.According to the analysis and discussion on the role of load characteristics for scheduling algorithm,a prediction-based adaptive load balancing algorithm(RR_MMMCS-A-P) was proposed in this paper.RR_MMMCS-A-P can predict the arrival rate and the size of the follow-up request by monitoring the workload characteristics and rapid adjustment of the corresponding parameters in order to balance the load between servers.The experimental results show that compared with CPU-based and CPU-memory-based scheduling algorithm,RR_MMMCS-A-P has better performance in reducing average response time for both calculation-intensive and data-intensive jobs.

Research on scheduling algorithms in Web cluster servers

This paper analyzes quantitatively the impact of the load balance scheduling algorithms and the locality scheduling algorithms on the performance of Web cluster servers, and brings forward the Adaptive_LARD algorithm. Compared with the representative LARD algorithm, the advantages of the Adaptive_LARD are that: (1) it adjusts load distribution among the back-ends through the idea of load balancing to avoid learning steps in the LARD algorithm and reinforce its adaptability; (2) by distinguishing between TCP connections accessing disks and those accessing cache memory, it can estimate the impact of different connections on the back-ends' load more precisely. Performance evaluations suggest that the proposed method outperforms the LARD algorithm by up to 14.7%.