A Comparative Study of Task Scheduling and Load Balancing Techniques with MCT using ETC on Computational Grids

ABSTRACTThe performance of 3GPP long term evolution (LTE) network can be significantly influenced by unbalanced user distribution. In heavily loaded cells, users cannot obtain satisfied services because of shortage of resources, whereas in lightly loaded cells, there are plenty of unoccupied resources. Load balancing (LB) is proposed to deal with this problem, through which the load distribution among nearby cells can be greatly balanced. However, because of the usage of full frequency reuse in LTE, the cell‐edge throughput is severely deteriorated by strong intercell interference. Meanwhile, LTE network usually serves users with heterogenous quality‐of‐service (QoS) requirements, which requires LB to be aware of it. Furthermore, call admission control (CAC) is indispensable in the process of LB, and it depends heavily on the long‐term average performance of the scheduling scheme. Thus, design of an efficient LB algorithm with the consideration of intercell interference, QoS, CAC and scheduling together becomes an important issue. To deal with the previous problem, a multi‐objective optimisation problem is formulated in this paper. Its complexity is analysed, and a practical algorithm, which includes hybrid scheduling, QoS‐guaranteed CAC, QoS‐aware intracell and intercell LB, is proposed. Extensive simulations are conducted. Results show that the proposed algorithm can approximate the optimal solution efficiently, thus leading to significantly better performance, that is, a lower new call blocking rate for users with QoS requirement, a larger total utility and a higher cell‐edge throughput for users without QoS requirement, although the cost is a bit degradation of the total throughput. Copyright © 2013 John Wiley & Sons, Ltd.

Design of energy efficient load balancing models in cloud environments requires in-depth analysis of the cloud architecture and nature of requests served by the cloud. Depending upon these parameters, machine learning models are designed which aim at assigning best possible resource combination to serve the given tasks. This assignment varies w.r.t. multiple task and cloud parameters; which include task time, virtual machine (VM) performance, task deadline, energy consumption, etc. In order to perform this task, a wide variety of algorithms are developed by researchers cloud designers. Each of these algorithms aim at optimizing certain load balancing related parameters; for instance, a Genetic Algorithm (GA) designed for optimization of VM utilization might not consider task deadline before task allocation to the VMs. While, algorithms aimed at performing deadline aware load balancing might not provide effective cloud-to-task-mapping before allocation of tasks. Thus, it becomes difficult for researchers to select the best possible algorithms for their cloud deployment. In order to reduce this ambiguity, the underlying text compares different energy efficient cloud load balancing algorithms; and evaluates their performance in terms of computational complexity, and relative energy efficiency. This performance evaluation is further extended via inter architecture comparison; in order to evaluate the most optimum load balancer implementation for a given energy efficient application. Thus, after referring this text, researchers and cloud system designers will be able to select optimum algorithmic implementations for their given deployment. This will assist in reducing cloud deployment delay, and improving application specific load balancer performance.

Dynamic load balancing techniques prevents computer nodes from overloading unevenly while leaving other idle. It is considered as one of the most challenging topics in parallel computing. Moreover, it is essential for increasing the efficiency of highly parallel systems especially in solving multitask problems with unpredictable load estimates. Particularly, over each processor in the parallel systems and interconnection networks. This paper focuses on developing an efficient algorithm for load balancing on Hyper Hexa Cell (HHC) interconnection network, namely, HHCLB algorithm. Basically, the Dimension Exchange Method (DEM) approach is used in this paper to construct a new load balancing approach on the network of HHC interconnections. Thus, an algorithm was introduced and simulated using java threads, where the performance of the algorithm is evaluated both analytically and experimentally. The evaluation was in terms of various performance metrics, including, execution time, load balancing accuracy, communication cost. By implementing the proposed load balancing algorithm to the HHC network, a high degree of accuracy and minimal execution time was achieved. It is important to highlight that the algorithm recorded small gap between the execution time for small number of processors and large number of processors. For instance, the algorithm achieved 0.14 seconds for balancing the load of 6 processors while 0.59 seconds for balancing the load of 3072 processors. This proves how effective the algorithm is in balancing the load for different network sizes from small to large number of processors, with a slight difference in execution time.

The tremendous advancement in distributed computing and Internet of Things (IoT) applications has resulted in the adoption of fog computing as today’s widely used framework complementing cloud computing. Thus, suitable and effective applications could be performed to satisfy the applications’ latency requirement. Resource allocation techniques are essential aspects of fog networks which prevent unbalanced load distribution. Effective resource management techniques can improve the quality of service metrics. Due to the limited and heterogeneous resources available within the fog infrastructure, the fog layer’s resources need to be optimised to efficiently manage and distribute them to different applications within the IoT network. There has been limited research on resource management strategies in fog networks in recent years, and a limited systematic review has been done to compile these studies. This article focuses on current developments in resource allocation strategies for fog-IoT networks. A systematic review of resource allocation techniques with the key objective of enhancing QoS is provided. Steps involved in conducting this systematic literature review include developing research goals, accessing studies, categorizing and critically analysing the studies. The resource management approaches engaged in this article are load balancing and task offloading techniques. For the load balancing approach, a brief survey of recent work done according to their sub-categories, including stochastic, probabilistic/statistic, graph theory and hybrid techniques is provided whereas for task offloading, the survey is performed according to the destination of task offloading. Efficient load balancing and task-offloading approaches contribute significantly to resource management, and tremendous effort has been put into this critical topic. Thus, this survey presents an overview of these extents and a comparative analysis. Finally, the study discusses ongoing research issues and potential future directions for developing effective management resource allocation techniques.

In recent years, cloud computing provides a spectacular platform for numerous users with persistent and alternative varying requirements. In the cloud environment, security and service availability are the two most significant factors during the data encryption process. For providing optimal service availability, it is necessary to establish a load balancing technique that is capable of balancing the request from diverse nodes present in the cloud. This paper aims in establishing a dynamic load balancing technique using the APMG approach. Here in this paper, we integrated adaptive neuro-fuzzy interference system-polynomial neural network as well as memory-based grey wolf optimization algorithm for optimal load balancing. The memory-based grey wolf optimization algorithm is employed to enhance the precision of ANFIS-PNN and to maximize the locations of the membership functions respectively. Also, two significant factors namely the turnaround time and CPU utilization involved in optimal load balancing scheme are evaluated. Finally, the performance evaluation of the proposed MG-ANFIS based dynamic load balancing approach is compared with various other load balancing approaches to determine the system performances.

PurposeIn general, cloud computing is a model of on-demand business computing that grants a convenient access to shared configurable resources on the internet. With the increment of workload and difficulty of tasks that are submitted by cloud consumers; “how to complete these tasks effectively and rapidly with limited cloud resources?” is becoming a challenging question. The major point of a task scheduling approach is to identify a trade-off among user needs and resource utilization. However, tasks that are submitted by varied users might have diverse needs of computing time, memory space, data traffic, response time, etc. This paper aims to proposes a new way of task scheduling.Design/methodology/approachTo make the workflow completion in an efficient way and to reduce the cost and flow time, this paper proposes a new way of task scheduling. Here, a self-adaptive fruit fly optimization algorithm (SA-FFOA) is used for scheduling the workflow. The proposed multiple workflow scheduling model compares its efficiency over conventional methods in terms of analysis such as performance analysis, convergence analysis and statistical analysis. From the outcome of the analysis, the betterment of the proposed approach is proven with effective workflow scheduling.FindingsThe proposed algorithm is more superior regarding flow time with the minimum value, and the proposed model is enhanced over FFOA by 0.23%, differential evolution by 2.48%, artificial bee colony (ABC) by 2.85%, particle swarm optimization (PSO) by 2.46%, genetic algorithm (GA) by 2.33% and expected time to compute (ETC) by 2.56%. While analyzing the make span case, the proposed algorithm is 0.28%, 0.15%, 0.38%, 0.20%, 0.21% and 0.29% better than the conventional methods such as FFOA, DE, ABC, PSO, GA and ETC, respectively. Moreover, the proposed model has attained less cost, which is 2.14% better than FFOA, 2.32% better than DE, 3.53% better than ABC, 2.43% better than PSO, 2.07% better than GA and 2.90% better than ETC, respectively.Originality/valueThis paper presents a new way of task scheduling for making the workflow completion in an efficient way and for reducing the cost and flow time. This is the first paper uses SA-FFOA for scheduling the workflow.

The exponentially extensive and emerging expansion of the cloud computing network (CCN) and its prolific application result in an ample number of users/clients. The frugal superintend of the client’s requests for resources is troublesome due to a precise number of resources and to actualize the engrossment of Quality of Service (QOS). In aisle to achieve QOS, it is mandatory to maximize the efficiency and reliability of CNN resources. The zero-ground affair is to find an efficient load balancing (LB) algorithm (LBA) that obliged to parcel out data traffic amidst multitudinous resources. Here, in this paper, a multitudinous proposed scheme for QOS has been analyzed.

Cloud computing is an innovative technology that mainly relies on sharing of computing resources, data as well as information over the internet. Cloud computing has been evolved from technological growth in Grid systems, Virtualization and Web2.0. Efficient Load Balancing (LB) and scheduling techniques are required for optimal utilization of computing resources and to reduce turnaround time. Load Balancing balances the load among nodes by shifting load from over-utilized node to under-utilized node. This paper presents an extensive review of different meta-heuristic techniques that are applicable in grid and cloud systems to generate optimal load balancing solution.

This paper provides a review of heuristics and metaheuristics methods, to solve the job scheduling problem in grid systems under the ETC (Expected Time to Compute) model. The problem is an important issue for efficient resource management in computational grids, which is performed by schedulers of these High Performance Computing systems. We present an overview of methods and a comparison of the results reported in the papers that use ETC model. The best methods are identified according to Braun et al. instances [8], which are ETC model instances most used in literature. This survey can help new researchers to lead them directly at the best scheduling algorithms already available to perform deep future works.

Efficient load balancing is vital in heterogeneous data center networks to ensure optimal resource use and service performance. Traditional methods struggle with dynamic VM workloads and diverse resource demands. This systematic review explores the integration of Software-Defined Networking (SDN) and Multi-Criteria Decision Making (MCDM) as a solution. SDN offers centralized, programmable control, while MCDM enables intelligent decision-making across multiple performance metrics. Reviewing recent literature, this study highlights key techniques, such as the Weighted Sum Model (WSM), and identifies gaps in real-time adaptability and metric integration. The review serves as a foundation for developing scalable, QoS-aware load balancing systems in cloud environments.

Load unbalancing problem is a multi-variant, multi-constraint problem that degrades performance and efficiency of computing resources. Load balancing techniques cater the solution for load unbalancing situation for two undesirable facets- overloading and under-loading. In contempt of the importance of load balancing techniques to the best of our knowledge, there is no comprehensive, extensive, systematic and hierarchical classification about the existing load balancing techniques. Further, the factors that cause load unbalancing problem are neither studied nor considered in the literature. This paper presents a detailed encyclopedic review about the load balancing techniques. The advantages and limitations of existing methods are highlighted with crucial challenges being addressed so as to develop efficient load balancing algorithms in future. The paper also suggests new insights towards load balancing in cloud computing.

An emerging method of digital computing known as "cloud computing" has recently gained immense popularity. While there are many benefits to using the cloud over the internet, there are also serious challenges that must be addressed in order to boost the efficiency of this method. Challenges to cloud computing via the internet include load balancing, work scheduling, fault tolerance, and several security concerns. The effectiveness of the cloud may be enhanced by fixing a number of problems, one of the most pressing being load balancing. To prevent any one node from being too overburdened or underused, a system employs a technique known as load balancing. In order to increase utilization and minimize overall task execution time, load balancing algorithms are developed to distribute work fairly across available resources. In this article, a review and comparison of different load-balancing algorithms is provided. This paper provides a basis of the application of different load-balancing techniques, which utilize the approach of soft computing within the cloud computing environment.

Objectives: To enlighten the issues and challenges of Load Balancing Algorithm (LBA) for cloud computing environment. In this study the authors proposed a LBA for cloud computing server. Methods/Statistical Analysis: The numerous LBAs are compared with objective, merit, demerit and challenges of the LBA in terms of parameters such as performance, response time, scalability, overhead and many more. This study facilitates identification of efficient LBA for optimizes resource use, minimum response time, maximum throughput and avoidance of overload. Findings: The load balancer is permitted in most instances to provide support continuity as well as dealing with extra traffic. Consequently the useful load balancing algorithms required to find inexpensive resource usage by provisioning of resources to cloud users. The use of proposed algorithm is to improve the overhead and load imbalance factor of the system. Application: The Load Balancing (LB) is a vital feature of cloud computing environment. The economical LB algorithm assures cost effective resource usage by provisioning of resources to cloud users on demand schedule in pay-as-you-say-manner. The LB may actually provide assistance prioritizing users by using suitable scheduling requirements and also it is crucial part in cloud computing for maintaining the work load across various systems over the network and gives minimum processing time, minimum response time and ignores the workload.

Cloud computing is an emerging computing paradigm in which shared resources are provided according to the customer request at specific time. Load balancing is the process of distributing workload among various nodes of the computing system. The load can be CPU load, memory capacity, or network load. An efficient load balancing avoids a situation where some of the nodes are heavily loaded while other nodes are idle or doing very little work. When Virtual Machine (VM) is overloaded with multiple tasks, these tasks are removed and migrated to the under loaded VMs of the same or different datacenter. This paper proposes a bee colony based algorithm for efficient load balancing, which is based on the foraging behavior of honey bees to balance load across VMs. In the proposed method, tasks removed from over loaded VMs are treated as honey bees and under loaded VMs are the food sources. The proposed method also considers the priorities of tasks in the waiting queues of VMs and tries to achieve minimum response time and reduced number of task migrations. The experimental result shows that there is significant improvement in the Quality of Service (QoS).

Throughput maximization in a network is one of the major key challenges in wireless LANs (WLANs) and it could be achieved when the load of access points (APs) is balanced. The AP load in WLANs is very often found to be uneven. To overcome such imbalance of load, a load balancing technique have been proposed. The load balancing techniques commonly require proprietary software or hardware at the user side for controlling the user-AP association. A new load balancing technique by controlling the size of WLAN cells (i.e., AP’s coverage range), which is conceptually similar to cell breathing in cellular networks. The proposed scheme does not require any modification from the user side nor may any changes be required to the IEEE 802.11 standard [1]. It only requires the ability of dynamically changing the transmission power of the AP beacon messages. We develop a newer load balancing algorithm that will find the optimal beacon power settings which will thereon help to minimize the load of the most congested AP. The load may become shared and this may be performed with minimal delay and also maximized throughput is to be achieved. Keywords: Network throughput, Access points, WLAN, Beacon Messages, Load balancing, Throughput, Wireless LANs.