Task partitioning, scheduling and load balancing strategy for mixed nature of tasks

Heterogeneous Distributed Computing (HDC) system consists of Workstations (WSs) and Personal Computers (PCs). In HDC system, each WS/PC may have different processor and performance. In order to take advantage of this diversity of processing power of a system, an effective task partitioning, scheduling, and load balancing are needed to get the optimum performance. This paper examines the effectiveness of task partitioning and scheduling strategies for image (raytracing) processing application on HDC system. The static and dynamic/Run-time Task Scheduling (RTS) strategies are shown inadequate for balancing the load of HDC system. Two adaptive tasks scheduling strategies are proposed for HDC image computing system. These adaptive strategies are: i) Master Initiate Sub-task size (MIS) strategy (based on centralized resources management approach), and ii) Worker Initiated Sub-task size (WIS) strategy (based on semi-decentralized resources management approach). The measured results show that the adaptive (MIS & WIS) strategies dramatically improve the performance of HDC raytracing system and remedy the defects of static and RTS strategies. Performances of the all investigated strategies are evaluated on manager/master and workers model of HDC system. In performance comparisons of MIS and WIS strategies, the WIS strategy shows a slightly better speedup and scalability over MIS strategy.

Cloud computing enables the internet based computing to everywhere. In cloud computing the task scheduling and load balancing is the important issue that must be considered in data centers. In cloud platform service providers allocate the resources based on the available resources this will increase the QoS. The process of distributing workloads is a load balancing Technique. In this paper focusing the load balancing between the virtual machine. Load balancing are different types that is network load, memory capacity, storage capacity and CPU load. Load balancing supports failover, enabling scalability and decreases costs and maximizes the resource availability. This paper presents the detailed review of load balancing with machine learning technique such as naïve bayes, random forest, Decision Tree and Support Vector Machine. By these machine learning model, SVM algorithm significantly to classify the incoming request to reduce the processing time (makespan). Also provide the moth flame optimization technique for dynamic task scheduling to make better scheduling under different load condition. We have experimented and evaluated this algorithm in a cloud simulation environment; we obtain better outcome than some other well-known algorithms. The Simulation results demonstrate that the proposed technique has a better development in terms of reducing the makespan time and load balancing.

Objectives: In this paper various task scheduling algorithm along with load distribution techniques investigated to ensure efficient mapping of tasks to resources and for coherent resource utilization in heterogeneous environment. Statistical Analysis: A detailed comparative approach is conferred for different job scheduling and load balancing algorithm available in the literature. Considered parameters for comparisons are scheduling approaches, techniques, findings, benefits, pros and cons. This study facilitates to identify the efficient scheduling and load balancing approach which optimizes resource use, minimizes completion time as well as task waiting time and balanced load among resources. Findings: Make span time and flow time are two important parameters to compute and minimize job execution time. This paper explores an intense survey of minimization of task execution time with minimum make span time and flow time. Apart from this a joint review on job scheduling, load balancing and minimizing job completion time using Expected Time to Compute (ETC) matrix is presented. At the end of this paper a research model is proposed which is a novel and hybrid approach with a combination of the efficiency of scheduling and load balancing. Application: The Load Balancing (LB) is an important feature of the grid environment. The efficient LB algorithm assures cost effective resource usage by the provisioning of resources to users on demand schedule in pay-as-you-say-manner.

In the proposed research work, novel heuristic energy efficiency architecture model is designed to minimize the response time and environmental impact, by considering task scheduling and load balancing operations. Scalable framework is used to enhance the QoS with auto scaling techniques. In this technique cloud resources are allocated and booted quickly to meet response time requirements, which depends upon incoming load. To improve the utilization of resources for load balancing, resource consolidation considers with two energy aware algorithm which includes VM placement and Live VM migration. Then modified Best Fit Decreasing (BFD) algorithm is used for VMs placement by considering CPU utilization and VMs resource consumption rate. Live migration is processed with significant moving constraints such as CPU state, memory content, storage content and network connections. The main aim of energy efficient model is to keep the CPU utilization under specified threshold limit for improving resource efficiency and ensure QoS.

Task scheduling and load balancing are challenging problems in cloud computing, as the demand of cloud computing resources is escalating everywhere anytime. The load on cloud nodes is very high with the increase in client request for cloud services. This condition needs load balancing techniques to uniformly distribute the client request among existing resources so that underutilization and overutilization of resources can be minimized. An impoverished scheduling strategy may over burdensome virtual machines while remaining virtual machines are idle. In this paper, load balance-aware task scheduling policy is proposed using genetic algorithm. The performance of the proposed technique is compared with existing schemes. The simulation output represents that the proposed algorithm outperforms than the current algorithms.KeywordsGenetic algorithmSchedulingLoad balancingCloud computing

Cloud computing is a distributed computing model in which access is based on demand. A cloud computing environment includes a wide variety of resource suppliers and consumers. Hence, efficient and effective methods for task scheduling and load balancing are required. This paper presents a new approach to task scheduling and load balancing in the cloud computing environment with an emphasis on the cost-efficiency of task execution through resources. The proposed algorithms are based on the fair distribution of jobs between machines, which will prevent the unconventional increase in the price of a machine and the unemployment of other machines. The two parameters Total Cost and Final Cost are designed to achieve the mentioned goal. Applying these two parameters will create a fair basis for job scheduling and load balancing. To implement the proposed approach, learning automata are used as an effective and efficient technique in reinforcement learning. Finally, to show the effectiveness of the proposed algorithms we conducted simulations using CloudSim toolkit and compared proposed algorithms with other existing algorithms like BCO, PES, CJS, PPO and MCT. The proposed algorithms can balance the Final Cost and Total Cost of machines. Also, the proposed algorithms outperform best existing algorithms in terms of efficiency and imbalance degree.

Hybrid grey wolf and improved particle swarm optimization with adaptive intertial weight-based multi-dimensional learning strategy for load balancing in cloud environments

Most of the existing solutions on task scheduling and resource management in grid computing are based on the traditional client/server model, enforcing a homogeneous policy on making decisions and limiting the flexibility, unpredictable reliability and scalability of the system. Thus, we need a well organised system architecture to provide high system availability with resource scheduling scheme for grid environment. In this paper, we integrate grid with P2P on to the hypercube topology with additional neighbourhood links for task scheduling and load balancing, which gives better makespan and balances the load. We propose the SPA-based task scheduling methodology in order to enhance system performance in hypercubic P2P grid (HPGRID). The simulation results show that the SPA-based min-mean scheduling minimises the makespan along with load balancing and guarantees the high system availability in system performance. At last, the min-mean algorithm is compared with min-min and max-min algorithm using SPA, by simulate experimentation, and it shows that the new algorithm has a better quality of system load balancing and the utilisation of system resources.

In Cloud Computing (CC) environment, load balancing refers to the process of optimizing resources of virtual machines. Load balancing in the CC environment is one of the analytical approaches utilized to ensure indistinguishable workload distribution and effective utilization of resources. This is because only by ensuring effective balance of dynamic workload results in higher user satisfaction and optimal allocation of resource, therefore improve cloud application performance. Moreover, a paramount objective of load balancing is task scheduling because surges in the number of clients utilizing cloud lead to inappropriate job scheduling. Hence, issues encircling task scheduling has to be addressed. In this work a method called, Ford Fulkerson and Newey West Regression-based Dynamic Load Balancing (FF-NWRDLB) in CC environment is proposed. The FF-NWRDLB method is split into two sections, namely, task scheduling and dynamic load balancing. First, Ford Fulkerson-based Task Scheduling is applied to the cloud user requested tasks obtained from Personal Cloud Dataset. Here, employing Ford Fulkerson function based on the flow of tasks, energy-efficient task scheduling is ensured. The execution of asymmetrical scientific applications can be smoothly influenced by an unbalanced workload distribution between computing resources. In this context load balancing signifies as one of the most significant solution to enhance utilization of resources. However, selecting the best accomplishing load balancing technique is not an insignificant piece of work. For example, selecting a load balancing model does not work in circumstances with dynamic behavior. In this context, a machine learning technique called, Newey West Regression-based dynamic load balancer is designed to balance the load in a dynamic manner at run time, therefore ensuring accurate data communication. The FF-NWRDLB method has been compared to recent algorithms that use the markov optimization and the prediction scheme to achieve load balancing. Our experimental results show that our proposed FF-NWRDLB method outperforms other state of the art schemes in terms of energy consumption, throughput, delay, bandwidth and task scheduling efficiency in CC environment.

Routing and wavelength assignment (RWA) is one of the key issues in the wavelength-routed optical networks. Although some RWA algorithms have been well performed to meet the need of certain networks requirement, they usually neglect the performance of the whole networks, especially the load balancing of the whole networks. This is quite likely to lead to some links bearing excessive ligthpaths and traffic load, while other links being at an idle state. In this paper, the load distribution vector ( LDV ) is introduced to describe the links load of the networks firstly. Then by means of minimizing the LDV of the networks, the load balancing of the whole networks is tried to improve. Based on this, a heuristic load balancing (HLB) strategy is presented. Moreover, a novel RWA algorithm adopting the heuristic load balancing strategy is developed, as well as two other RWA algorithms adopting other load balancing strategies. At last, the simulations of the three RWA algorithms with different load balancing strategies are done for comparison on the basis of both the regular topology and the irregular topology networks. The simulation results show that the key performance parameters such as the average variance of links, the maximum link load and the number of established lightpath are improved by means of our novel RWA algorithm with the heuristic load balancing strategy.

In recent decades, task scheduling and load balancing in the cloud is a growing research area, due to the vast amount of data stored in the server highly increases the load. In order to address this concern, Hybrid Max-Min Genetic Algorithm (HMMGA) is proposed for task scheduling and load balancing in the cloud environment. At first, the load is evaluated for every Virtual Machine (VM), if the load is high, then HMMGA is used for balancing the load. HMMGA selects the best VMs to assign the tasks and migrates the over-loaded VMs tasks to the under-loaded VMs. HMMGA significantly avoids the imbalanced workload performance in the cloud environment. In this research paper, the proposed HMMGA performance is compared to Max-Min algorithm, Low time complexity and low cost binary Particle Swarm Optimizer (IBPSO-LBS) and PSO with Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to examine the efficacy of HMMGA. From the experimental simulation, the result shows that HMMGA averagely delivers 1.63 and 3.88 seconds less make span compared to the Max-Min and TOPSIS-PSO algorithm for five VMs. In addition, HMMGA averagely enhances 10% to 40% of resource utilization than the MaxMin and TOPSIS-PSO algorithm. In another experiment, the HMMGA approximately showed 1.7 to 25.99 seconds less average waiting time compared to the Max-Min and IBPSO-LBS.

Cloud computing is a scalable computing paradigm that provides computing services to users over the Internet. To achieve high user satisfaction and improve the performance of the Cloud, a balanced distribution of users tasks plays a vital role. In the literature, a number of task scheduling and load-balancing schemes have been proposed. However, the majority of these scheduling heuristics focus on a single evaluation parameter (i.e., makespan or resource utilization, etc.) as a scheduling objective. Improving one parameter may not guarantee an increase in the overall performance of the Cloud. There is a need to have such algorithms that focus on improving the overall performance of the Cloud by taking into account multiple evaluation parameters. In this paper, an Overall Performance-based Resource-aware Dynamic Load-balancer (OG-RADL) for deadline constrained Cloud tasks is proposed. OG-RADL has the ability to distribute the workload of independent and compute-intensive tasks according to the resource computation capability at run time. Moreover, a novel normalization technique is proposed that overcome the limitations of existing normalization techniques. The OG-RADL enhance load balancing, support deadline constrained tasks, and improve the overall performance gain of the Cloud. The experimental result shows that the proposed approach OG-RADL outperforms as compared to existing task scheduling algorithms named DLBA, DC-DLBA, Dy-MaxMin, RALBA, PSSELB, and MODE in terms of the overall performance of the Cloud.

A novel four-tier architecture for delay aware scheduling and load balancing in fog environment

Most cluster schemes select cluster head (CH) randomly without considering load balancing and security in Wireless Sensor Networks (WSNs). Our research on relevant literature shows that the existing authentication schemes do not fully consider the load balancing of CHs, while the load balancing schemes ignore the security authentication of CHs. Therefore, this paper effectively combines load balancing and security verification, and proposes a secure load balancing and authentication scheme (SLEB) based on Clustered Wireless Sensor networks. SLEB not only effectively maintains the energy balance of the whole network, but also successfully improves packet forwarding rate and prolongs the network lifetime. The simulation shows that SLEB is energy-saving, safe and sustainable compared with other similar schemes.

High-speed routers and high-performance data centers share a common system-level architecture in which multiple processing nodes are connected by an interconnection network for high-speed communications. Load balancing is an important technique for maximizing throughput and minimizing delay of the interconnection network. In this thesis, efficient load balancing schemes are designed and analyzed for next-generation routers and data centers.
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\nIn high-speed router design, two preferred switch architectures are input-queued switch and load-balanced switch. In an input-queued switch, time-domain load balancing can be carried out by an iterative algorithm that schedules packets for sending in different time slots. The complexity of an iterative algorithm increases rapidly with the number of scheduling iterations. To address this problem, a single-iteration scheduling algorithm called D-LQF is designed, in which exhaustive service policy is adopted for reusing the matched input-output pairs in the previous time slots to grow the match size. 
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\nUnlike an input-queued switch, a load-balanced switch consists of two stages of crossbar switch fabrics, where load balancing is carried out in both time and space domains. Among various load-balanced switches, the feedback-based switch gives the best delay-throughput performance. In this thesis, the feedback-based switch is enhanced in three aspects. Firstly, we focus on reducing its switch fabric complexity. Instead of using crossbars, a dual-banyan network is proposed. The complexity of dual-banyan can be further reduced by merging the two banyans to form a Clos network, resulting in a Clos-banyan network. Secondly, we target at improving the delay performance of the feedback-based switch. A Clos-feedback switch architecture is devised where each switch module in the Clos network is a small feedback-based switch. With application-flow based load balancing, packet order is ensured and the average packet delay is reduced from O(N) to O(n), where N and n are the switch and switch module sizes, respectively. Thirdly, we extend the feedback-based switch to support multicast traffic. Based on the notion of pointer-based multicast VOQ, an efficient multicast scheduling algorithm with packet replication at the middle-stage ports only is proposed. In order to provide close-to-100% throughput for any admissible multicast traffic patterns, a three-stage implementation of feedback-based switch is also designed.
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\nIn designing load balancing schemes for data centers, we focus on the most popular fat-tree based data centers. Notably, packet-based load balancing is widely considered infeasible for data centers. This is because the associated packet out-of-order problem will cause unnecessary TCP fast retransmits, and as a result, severely undermine TCP performance. In this thesis, we show that if packet-based load balancing is performed properly, the packet out-of-order problem can be easily addressed by slightly increasing the number of duplicate ACKs required for triggering fast retransmit. Admittedly, in case of a real packet loss, the loss recovery time will be increased. But our simulation results show that such an increase is far less than the reduction in the network queueing delay (due to a better load-balanced network). As compared to a flow-based load balancing scheme, our packet-based scheme consistently provides significantly higher goodput and noticeably smaller delay.