Effective Load Balancing Research Articles

T-FSM: A Task-Based System for Massively Parallel Frequent Subgraph Pattern Mining from a Big Graph

Finding frequent subgraph patterns in a big graph is an important problem with many applications such as classifying chemical compounds and building indexes to speed up graph queries. Since this problem is NP-hard, some recent parallel systems have been developed to accelerate the mining. However, they often have a huge memory cost, very long running time, suboptimal load balancing, and possibly inaccurate results. In this paper, we propose an efficient system called T-FSM for parallel mining of frequent subgraph patterns in a big graph. T-FSM adopts a novel task-based execution engine design to ensure high concurrency, bounded memory consumption, and effective load balancing. It also supports a new anti-monotonic frequentness measure called Fraction-Score, which is more accurate than the widely used MNI measure. Our experiments show that T-FSM is orders of magnitude faster than SOTA systems for frequent subgraph pattern mining. Our system code has been released at https://github.com/lyuheng/T-FSM.

Clustering based EO with MRF technique for effective load balancing in cloud computing

PurposeCloud computing (CC) refers to the usage of virtualization technology to share computing resources through the internet. Task scheduling (TS) is used to assign computational resources to requests that have a high volume of pending processing. CC relies on load balancing to ensure that resources like servers and virtual machines (VMs) running on real servers share the same amount of load. VMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data.Design/methodology/approachVMs are an important part of virtualization, where physical servers are transformed into VM and act as physical servers during the process. It is possible that a user’s request or data transmission in a cloud data centre may be the reason for the VM to be under or overloaded with data. With a large number of VM or jobs, this method has a long makespan and is very difficult. A new idea to cloud loads without decreasing implementation time or resource consumption is therefore encouraged. Equilibrium optimization is used to cluster the VM into underloaded and overloaded VMs initially in this research. Underloading VMs is used to improve load balance and resource utilization in the second stage. The hybrid algorithm of BAT and the artificial bee colony (ABC) helps with TS using a multi-objective-based system. The VM manager performs VM migration decisions to provide load balance among physical machines (PMs). When a PM is overburdened and another PM is underburdened, the decision to migrate VMs is made based on the appropriate conditions. Balanced load and reduced energy usage in PMs are achieved in the former case. Manta ray foraging (MRF) is used to migrate VMs, and its decisions are based on a variety of factors.FindingsThe proposed approach provides the best possible scheduling for both VMs and PMs. To complete the task, improved whale optimization algorithm for Cloud TS has 42 s of completion time, enhanced multi-verse optimizer has 48 s, hybrid electro search with a genetic algorithm has 50 s, adaptive benefit factor-based symbiotic organisms search has 38 s and, finally, the proposed model has 30 s, which shows better performance of the proposed model.Originality/valueUser’s request or data transmission in a cloud data centre may cause the VMs to be under or overloaded with data. To identify the load on VM, initially EQ algorithm is used for clustering process. To figure out how well the proposed method works when the system is very busy by implementing hybrid algorithm called BAT–ABC. After the TS process, VM migration is occurred at the final stage, where optimal VM is identified by using MRF algorithm. The experimental analysis is carried out by using various metrics such as execution time, transmission time, makespan for various iterations, resource utilization and load fairness. With its system load, the metric gives load fairness. How load fairness is worked out depends on how long each task takes to do. It has been added that a cloud system may be able to achieve more load fairness if tasks take less time to finish.

ESCALB: An effective slave controller allocation-based load balancing scheme for multi-domain SDN-enabled-IoT networks

In software-defined networking (SDN), several controllers improve the reliability as well as the scalability of networks such as the Internet-of-Things (IoT), with the distributed control plan. To achieve optimal results in IoT networks, an SDN can be employed to reduce the complexity associated with IoT and provide an improved quality-of-service (QoS). With time, it is likely expected that the demand for IoT will rise, and a large number of sensors will be connected, which can generate huge network traffic. With these possibilities, the SDN controllers processing capacity will be surpassed by the traffic sent by the IoT sensors. To handle this kind of challenge, and achieve promising results, a dynamic slave controller allocation with a premeditated mechanism can play a pivotal role to accomplish the task management and migration plan. Following this, we proposed an efficient slave controller allocation-based load balancing approach for a multi-domain SDN-enabled IoT network, which aims to transfer switches to a controller with idle resources effectively. Among several slave controllers for selecting a target controller, a multi-criteria decision-making (MCDM) strategy, i.e., an analytical network process (ANP) has been used in our approach to enrich communication metrics and maintain high-standard QoS statistics. Moreover, switch migration is modeled with knapsack 0/1 problem to achieve maximum utilization of the slave controllers. Our proposed scheme enabled with a flexible decision-making process for selecting controllers with varying resources. The results demonstrated with emulation environment show the effectiveness of the ESCALB.

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

The popularization of Internet applications and rapid advent of information technologies have invited increased number of developers and companies to focus on the area of Cloud Computing (CC). The most significant issues and challenges in the domain of CC include Load Balancing (LB), scheduling of task executions and managing resource allocation. In particular, LB being the process of distributing computing resources and workloads has attracted maximum attention as it is the predominant issue in CC. In this paper, Hybrid Grey Wolf and Improved Particle Swarm Optimization Algorithm with Adaptive Intertial Weight-based multi-dimensional Learning Strategy (HGWIPSOA)-based LB scheme is proposed for enhancing precision and rapidness in task scheduling and assignment of resources to Virtual Machines (VMs) in cloud environments. In the proposed scheme, initially, Grey Wolf Optimization Algorithm (GWOA) is incorporated into Particle Swarm Optimisation (PSO) for considering the highest fitness particle as alpha wolf search agent, such that the objective of allocating tasks to VMs is attained effectively and efficiently. It then integrates chaos, Adaptive Inertial Weight (AIW) and Dimensional Learning (DL) into PSO specifically to prevent premature convergence and achieve better convergence speed and global search ability depending on the best experience determined by particles for effective LB. The simulation experiments of the proposed HGWIPSOA mechanism confirm better results by offering 21.32 % improved throughput, 19.84 % reduced makespan, 24.98 % minimized degree of imbalance, 18.74 % reduced latency and 27.92 % reduced execution time independent of the number of tasks in the cloud environment on par with benchmarked LB schemes.

Perspicacious Fuzzy Clustering Franchised by Grasshopper Optimization Dexterity for Robust Resource Scheduling with Effective Load Balancing in Cloud Precedent

Utilizing cloud resources become promising in encroachment of internet technology, countenancing everyone to use resources for little or no cost. It will be very important to have task scheduling for sharing resources in cloud environment. To maintain effective resource usage, cloud technology equally divides workload among shareable resources when it receives task requests. Machine learning and metaheuristic algorithms afford dynamic component for equitable task distribution in cloud paradigm. The current state-of-art unsupervised models-based load balancing arbitrarily selects centroid locations and struggles to achieve incorrect task requests. Using an optimization technique that takes inspiration from behavioral science, study aims to build well-balanced clustering model-based task scheduling system. In order to efficiently schedule tasks among virtual servers in cloud environment, this proposed work styles aids of perspicacious fuzzy and Grass Hopper algorithms. The results showed that PFC-GOD upsurges cloud resources usage while lowering make-span, execution time, and high balance load scheduling.

Joint load-balancing and power control strategy to maximize the data extraction rate of LoRaWAN networks

LPWAN enabled networks have a dizzying growth and continue to meet an essential need in the Internet of Things market due to their ability to provide low-cost wireless access to geographically spread-out devices. Consequently, an efficient allocation of wireless resources in order to support numerous devices becomes a major concern. In this paper, we propose an SF assignment approach in LoRa networks, paying attention on the traffic load both per Spreading Factor and over the channels. Indeed, our strategy consists in finding a better distribution of the end-devices on the SF by orchestrating an effective load balancing. Moreover, the performance of our solution is evaluated under diverse network configurations, taking into account the capture effect and the non-orthogonality of SFs. Furthermore, we extended the solution by a transmission power control strategy for overall system energy-efficiency. In addition, we validated some assumptions by full-scale experiments like for the 3GPP path loss model, which is used for the first time in LoRa simulations. Our results suggest that Load Shifting leads to better performance in terms of Date Extraction Rate (DER) while guaranteeing good scalability on the network size and density.

A novel approach for new architecture for green data centre

The massive energy usage of data centers may be traced in part to the growing number of data centers and workstations because of economies of scale for cloud computing. It does, though, indicate wasteful power usage. Consequently, researching ways to improve the energy efficiency of datacenter equipment is now a crucial aspect in reducing datacenter power consumption. In this study, we describe methodologies and algorithms for flexible, energy-efficient, and effective load balancing in data centers, resulting in lower energy consumption by systems. Because of difficulties related with energy consumption, such as capital expenditure, operational costs, and environmental effect, renewable energy is becoming an increasing major consideration in data centers. With the rise in environmental consequences around the world, data centers must consider energy efficiency as one of the most critical factors. Cloud computing techniques have made a huge impact all over the world. Green DC is a concept that has been tested. We suggested an algorithm in this paper based on certain current algorithm limitations that will help to reduce environmental effect. We investigated the efficiency of our program further and used a load balancing method to improve its performance.

Complex predictive analysis for health care: a comprehensive review

Healthcare organizations accept information technology in a management system. A huge volume of data is gathered by healthcare system. Analytics offers tools and approaches for mining information from this complicated and huge data. The extracted information is converted into data which assist decision-making in healthcare. The use of big data analytics helps achievement of improved service quality and reduces cost. Both data mining and big data analytics are applied to pharma co-vigilance and methodological perspectives. Using effective load balancing and as little resources as possible, obtained data is accessible to improve analysis. Data prediction analysis is performed throughout the patient data extraction procedure to achieve prospective outcomes. Data aggregation from huge datasets is used for patient information prediction. Most current studies attempt to improve the accuracy of patient risk prediction by using a commercial model facilitated by big data analytics. Privacy concerns, security risks, limited resources, and the difficulty of dealing with massive amounts of data have all slowed the adoption of big data analytics in the healthcare industry. This paper reviews the various effective predictive analytics methods for diverse diseases like heart disease, blood pressure, and diabetes.

Load Balancing With Deadline-Driven Parallel Data Transmission in Data Center Networks

With the explosive growth of the Internet of Things (IoT), an increasing amount of sensor data generated by soft real-time IoT applications has been moved to data centers for storage and data analysis. Large amounts of these data are required to be processed within a given deadline to ensure application performance. Therefore, meeting the transmission deadlines of data flows for soft real-time applications has always been crucial yet challenging to current data centers. Recent progress has demonstrated that adopting parallel data transmission over multipath data center network combining with effective load balancing can achieve a high bisection network bandwidth, thus speeding up the network transfer of data flows. Nevertheless, the deadline miss ratios (DMRs) of these flows are not lowered as expected since the existing load balancing schemes are naturally agnostic to the deadline requirement. They are either unable to reroute traffic flexibly or aimlessly reroute these deadline-restrained flows, regardless of their urgent levels and path conditions. To address these inefficiencies, we propose a deadline-aware load-balancing scheme, namely, DLB, which perceives the deadline requirements and helps the urgent flows to timely switch to those faster transmission paths to complete quickly. Specifically, DLB computes the urgent level for each flow in real time to judge if the switch needs to make proactive rerouting. When a flow is nonurgent, DLB does not proactively change its transmission path, leaving more available paths to those flows with higher urgent levels. When a flow becomes extremely urgent, it immediately switches to those light-loaded paths to finish its data transmission before its deadline as far as possible. Experimental results of NS2 simulations and real testbed implementations show that DLB reduces the DMRs by up to 50% compared to the state-of-the-art data center load-balancing schemes, while only induces trivial overhead during deployment.

Distance-Based Back-Pressure Routing for Load-Balancing LEO Satellite Networks

Featuring wide coverage and high data rate, LEO satellite networks will be an important supplement to the traditional terrestrial networks, enabling the space-air-ground integrated network service. However, effective load balancing routing strategies for LEO satellite networks need to be designed, due to the bursty characteristic of the network traffic and imbalanced regional communication load. To achieve that, we propose a Distance-based Back-Pressure Routing (DBPR) strategy for LEO satellite networks. DBPR calculates the link weights based on a novel distance-based metric, which can select uncongested short-distance paths to the destinations and distribute network traffic dynamically with low delay. To control the number of forwardings in the network, we restrict the transmission range to a rectangle region between each source-destination pair. We design DBPR in the distributed fashion without collecting the global network load information, which is suitable for LEO satellite networks with limited resources, long propagation delay, dynamic topology, etc. We analyze the network stability and prove the throughput optimality of DBPR. Simulation results demonstrate that DBPR can achieve higher throughput and lower delay, compared with the state-of-the-art strategies, especially in the environments with limited cache resource.

A new remote web-based MDSplus data visualization system for EAST

WebScope has been widely used in the Experimental Advanced Superconducting Tokamak (EAST) experiment for many years. The choice of the Java applet technology for its implementation has been successful, which allows for pulse data communication and instant feedback through various web browsers. Accessing MDSplus data with it however brings some inconvenience due to the majority of browsers have dropped support for Java plugin. Recently, a new web-based MDSplus data visualization system with the MVVM (Model–View–ViewModel) architecture has been developed. The flexibility brought by using JavaScript middle components in the architecture reduces the program coupling and improves the system availability. A further availability is proposed here, that is, using reverse proxy mechanism to publish the abstract interfaces provided by MDSplus separately from the visual resources, with the proxy to specify data request relationships. For incoming network traffic in the server pool, effective load balancing is ensured. The slice storage mechanism in MDSplus and thumbnail are now used for storing and reading long pulse data, and exporting data through proxy ports which ensures secure access to experimental data. Pulse access variables are also stored in cloud server for authorized users to share across devices. The system was successfully applied to EAST experiment and achieved good performance. The design details of the system are described in this paper.

Enhancing QoS with Resource Optimization Technique Based on Harmony Search in Cloud Environment

In cloud environment, enhancing QoS is a challenging task to solve the multi objective problems. Effective load balancing and scheduling is a solution to enhance QoS. Job scheduling is a NP complete problem and is difficult to get the best scheduling especially for large number of jobs to enhance QoS. Multiple activities may need to be scheduled efficiently on various resources (CPU, virtual machines) by reducing completion time and maximizing resource utilization. A meta-heuristic method has been proposed for optimizing multi-objective scheduling problems in cloud systems using harmony search algorithm. According to cloud user's expectations, throughput should be high such that most of the user tasks are completed before the deadline expires thereby resulting in a low task rejection rate. Major purpose of the HS-based, NEW HS scheduling algorithm is to arrange upcoming cloud applications at virtual machines in such a way that tasks are completed in least amount of time (execution and makespan time) with low execution cost and take deadline of cloud application as a QoS constraint.

CA‐MLBS: content‐aware machine learning based load balancing scheduler in the cloud environment

AbstractCloud computing is the on‐demand provision of computing resources over the Internet, such as cloud storage, computing power, network, and so on. Cloud computing has several advantages, including high speed, cost reduction, data security, and scalability. The main challenge in cloud environment is to balance the workloads and network traffic among the available resources to achieve maximum performance. Several methods have been proposed in the literature for effective load balancing, including heuristic, meta‐heuristic, and hybrid algorithms. The performance of these techniques has been improved by combining machine learning based Artificial Intelligence (AI) techniques and meta‐heuristic algorithms. Most of the existing load balancing techniques are not aware of the content type of user tasks. However, from the literature, the content type of the tasks can be very effective to design a balanced workload distribution system in the cloud. In this work, a novel AI‐assisted hybrid approach called Content‐aware Machine Learning based Load Balancing Scheduler (CA‐MLBS) is proposed. The scheduling system CA‐MLBS combines machine learning and meta‐heuristic algorithms to perform classification based on file type. To achieve this, a Support Vector Machine (SVM) based classifier is used to classify user tasks into different content types such as video, audio, image, and text. A metaheuristic algorithm based on Particle Swarm Optimization (PSO) is used to map users' tasks in the cloud. The proposed approach was implemented and evaluated using a renowned Cloudsim simulation kit and compared with Ant Colony Optimization File Type Format (ACOFTF) and Data Files Type Formatting (DFTF) heuristics. The results of the proposed study show that the proposed CA‐MLBS technique achieved improvements of up to 29%, 29%, and 44% in terms of makespan, response time, and throughput, respectively.

A Hybrid Supervised Learning Approach for Detection and Mitigation of Job Failure with Virtual Machines in Distributed Environments

Distributed data processing techniques are very popular nowadays due to high data generation from various resources. To increase work learning outcomes and to reduce consumption, modern massive computational systems divide jobs into several smaller tasks that perform in parallel. Nevertheless, responding with straggler processes, which are sluggish running processes that rise the total response time, is a typical performance issue in such platforms. In this paper, we proposed the detection of struggler nodes in a large distributed environment using a hybrid machine learning technique. Initially, the data has been collected from numerous virtual machine network logs. The entire data set has various fields such as Virtual Machine ID, CPU load, memory load, bandwidth utilization, etc. Memory utilization an input to the proposed system is collected from the garbage collection log files where the memory consumption on each VM and its timestamp is recorded. This is the most efficient way to get the memory consumption in web/desktop applications. Similarly, the CPU, I/O and bandwidth utilization is grabbed from the process monitoring functionality and SAR (System Activity Report) utility from the respective VM boxes. This data set is useful to identify weather-specific virtual machine is heated up or not. In this approach, we proposed three conventional machine learning algorithms and a hybrid machine learning algorithm for the identification of node status. Main purpose of the proposed system is to identify the slow performing node in an efficient way to prevent the other nodes from failures. This can provide effective load balancing and low response time for task execution from available VM’s in distributed cloud environments. To create its training program, several extractions of features approaches were used. TF-IDF, correlational co-occurrence, and density-based features have been mined from the whole data set. With extensive experimental analysis, we evaluate our system with our proposed classification algorithm. As a result, the system produces higher classification accuracy of 94.5% over the traditional machine learning classifiers. If the proposed system is tested against the data set fields, memory load and CPU load on the homogenous machine configurations, we see more efficiency while detecting the underperforming node than the heterogenous machine configurations.

Deadline-Aware Dynamic Task Scheduling in Edge–Cloud Collaborative Computing

In recent years, modern industry has been exploring the transition to cyber physical system (CPS)-based smart factories. As intelligent industrial detection and control technology grows in popularity, massive amounts of time-sensitive applications are generated. A cutting-edge computing paradigm called edge-cloud collaborative computing was developed to satisfy the need of time-sensitive tasks such as smart vehicles and automatic mechanical remote control, which require substantially low latency. In edge-cloud collaborative computing, it is extremely challenging to improve task scheduling while taking into account both the dynamic changes of user requirements and the limited available resources. The current task scheduling system applies a round-robin policy to cyclically select the next server from the list of available servers, but it may not choose the best-suited server for the task. To satisfy the real-time task flow of industrial production in terms of task scheduling based on deadline and time sensitivity, we propose a hierarchical architecture for edge-cloud collaborative environments in the Industrial Internet of Things (IoT) and then simplify and mathematically formulate the time consumption of edge-cloud collaborative computing to reduce latency. Based on the above hierarchical model, we present a dynamic time-sensitive scheduling algorithm (DSOTS). After the optimization of DSOTS, the dynamic time-sensitive scheduling algorithm with greedy strategy (TSGS) that ranks server capability and job size in a hybrid and hierarchical scenario is proposed. What cannot be ignored is that we propose to employ comprehensive execution capability (CEC) to measure the performance of a server for the first time and perform effective server load balancing while satisfying the user’s requirement for tasks. In this paper, we simulate an edge-cloud collaborative computing environment to evaluate the performance of our algorithm in terms of processing time, SLA violation rate, and cost by extending the CloudSimPlus toolkit, and the experimental results are very promising. Aiming to choose a more suitable server to handle dynamically incoming tasks, our algorithm decreases the average processing time and cost by 30% and 45%, respectively, as well as the average SLA violation by 25%, when compared to existing state-of-the-art solutions.

A Novel, Efficient, Green and Real-Time Load Balancing Algorithm for 5G Network Measurement Report Collecting Clusters

With the wide application of data mining and deep learning in mobile cellular network operation and maintenance, network measurement report (MR) plays an increasingly important role in artificial intelligence for IT operations (AIOps). For the integrity of MR reported by the operation and maintenance (OM) proxy of base station, existing collecting methods are typically based on static distributed clustering. Due to the lack of effective load balancing scheme, nevertheless, these methods typically result in some issues, e.g., low collecting efficiency, poor scalability, and excessive number of servers. Thus, in this work, leveraging the historical law of uploading MR for load forecasting, we propose the weighted least-connection load balancing algorithm (LPWLC) based on load forecasting. First, the historical law of reported MR is utilized to predict the load. Second, using the strategy of static binding and dynamic load adjustment, we bind OM with the assigned server in one cycle, calculate the server load in real-time, and evaluate the server weight by the load of each server. Finally, real-time load adjustment is carried out in line with the number of request connections and the weight of servers. Compared with the existing ones, the proposed algorithm could remove backup servers, thereby effectively reducing the cost and power consumption. Compared with the existing methods, this method has improved the load balancing degree by 28%, and reduced the energy consumption by 104[Formula: see text]W per hour.

An Integrated Framework on Autonomous-EV Charging and Autonomous Valet Parking (AVP) Management System

Autonomous vehicles (AVs) transform traditional commuting by decreasing congestion, improving road safety, and naturally integrate better with electric controls for flexible implementation of autonomous driving technologies. Indeed, electric-powered AVs or autonomous electric vehicles (AEVs) are benefiting each other in many aspects. While autonomy brings great efficiency in driving as well as battery use, electric vehicles (EVs) require less maintenance and drastically cut fuel costs. With AVs, a pivotal concern is within the realm of long-range autonomous valet parking (LAVP), such as diverse customer demands on parking (or drop-off/pick-up) for various journey planning. On the other hand, electric-powered AVs are typically with limited cruising range, and locating convenient charging services are also among the major impediments. As of yet, recent studies have started to investigate EV charging and LAVP in isolation as they rarely consider a joint optimization on user trip and energy refueling. Rather, we target in this work the integration of vehicle charging with autonomy in the sense of a systemic approach. Specifically, we propose an integrated AEV charging and LAVP management scheme, to resolve critical decision-making on convenient charging and parking management upon customer requirements during their journeys. The proposed scheme jointly considers charging reservations as well as parking duration at car parks (CPs), aiming to enable accurate predictions on future charging (and parking) states at CPs. Results show the advantage of our proposal over benchmarks, in terms of enhanced customer experiences in traveling period, as well as charging performances at both AEV and CP sides. Particularly, effective load balancing can be achieved across the network regarding the amount of charged as well as parked vehicles.

Effective load balancing strategy using fuzzy golden eagle optimization in fog computing environment

Fog computing (FC) is becoming popular for connecting real-time Internet of Things (IoT) applications such as driverless cars and health care. Although fog has low latency compared to the cloud, the fog becomes overloaded when serving a large number of IoT devices. FC struggles while distributing the load to the resources that may result in certain fog nodes being overloaded or underloaded. To overcome these issues, an effective load balancing algorithm named Fuzzy Golden Eagle Load Balancing (FGELB) is proposed. The proposed load balancing strategy consists of three stages, namely: assigning priority to the tasks, resource ranking and scheduling, and power management. Initially, the priority is assigned to the incoming task by considering the deadline time, task size, and predefined priority using a fuzzy algorithm. This prioritization of tasks helps to execute the important task without delay. Then the Golden Eagle Optimization algorithm (GEOA) is used to rank and schedule the resources. This ranking and scheduling help the tasks to be executed with suitable resources. Ranked resources execute the tasks in the resource engine, and the power engine manages the power by enabling/disabling the resources based on their necessity. The performance of the approach is tested and it is compared with the results of existing methods in terms of energy consumption, failure rate, computational cost, communication overhead, average turnaround time, and waiting time. The results show that the proposed FGELB approach achieves higher performance than the existing load balancing approaches.

Enhancing Response Time of Cloud Resources Through Energy Efficient Cloud Scheduling Algorithm

Cloud Computing is becoming a dominant trend in providing information technology (IT) services. The cloud comprises many hardware and software resources today, and more people are switching to such services. Users' requests for cloud resources must incur a minimum amount of load on the system while getting a rapid response. In the cloud today, there is too much computational power. Load balancing makes it possible for various components of the cloud computing environment to work efficiently. To balance client requests to available resources so that the system is not overloaded, and the requested resources are delivered as quickly as possible, an effective load balancing strategy is essential. In this research article, we have presented a critical analysis of various existing cloud load balancing and scheduling algorithms. Several task scheduling approaches have been proposed in the literature review, but there appears to be a lack of scheduling algorithms for real-time task works based on historical scheduling records (HSR). The proposed algorithm uses information available in HSR to efficiently distributes incoming user requests to available virtual machines. The proposed scheduling algorithm uses the scaleup and scale down resource algorithm which helps in achieving maximum resource utilization. The algorithm tries to balance the load on VMs by scaling up and down cloud resources. WorkflowSim is used to analyze the performance of the algorithm proposed. The simulation results are compared with the existing scheduling algorithm which shows the proposed algorithm outperforms existing scheduling algorithms in terms of makespan.

Effective Load Balancing and Security in Cloud using Modified Particle Swarm Optimization Technique and Enhanced Elliptic Curve Cryptography Algorithm

Effective Load Balancing and Security in Cloud using Modified Particle Swarm Optimization Technique and Enhanced Elliptic Curve Cryptography Algorithm