Top PDF Task Scheduling Balancing User Experience and Resource Utilization on Cloud

Task Scheduling Balancing User Experience and Resource Utilization on Cloud

Task Scheduling Balancing User Experience and Resource Utilization on Cloud

Since not all tasks will have the same calculated priority score, we implemented task aging into the proposed approach. The tasks that have lower than average score are at risk of ending up at the bottom of the queue. With incoming tasks having around the average score, or higher than average, tasks at the bottom of the queue will be overlooked because of their low priority and because incoming tasks will consistently have higher priority. To prevent tasks from being lost and ignored in the queue, we introduced the concept of task aging. When a task object is instantiated, the age for that task starts at 0. The scheduler will update the age of all tasks waiting to be scheduled at the beginning of each scheduling time interval. The age of a task is updated by adding a percentage of the overall task average to their calculated priority score. In practice, this percentage does have to be fixed and can be dynamically calculated, based on incoming task traffic, for instance. The more a task’s age is updated, the higher its priority will become, preventing any tasks from being overlooked by the scheduler regardless of their original priority. The calculation for the age of one task is illustrated in formula 3.1, where p represents the percentage chosen to increase the age for task t with n tasks.
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Scheduling Virtual Machines in Cloud Computing For Enhancing Income and Resource Utilization

Scheduling Virtual Machines in Cloud Computing For Enhancing Income and Resource Utilization

Abstract: With the advent of information technology (IT), doing computational tasks has become an indispensable aspect of modern life. People should be able to perform heavy computing tasks even if they do not possess costly hardware and software; indeed, they can do such tasks through service provides. Cloud computing is regarded as the latest development in IT which can accommodate these needs. The rationales for using cloud computing are maximum efficiency and minimum cost. Among cloud computing challenges, server cost in relation to virtual machine can be considered as a significant issue. In this paper, server cost has been examined as the research problem; more precisely, scheduling virtual machines in cloud computing has been investigated. The advantages of using common scheduling methods are that significant parameters such as energy consumption optimization, migration time minimization, response time reduction, resource utilization enhancement and system efficiency improvement have been addressed and enhanced. These parameters have been extensively studied and optimized in the literature related to cloud computing. However, it should be pointed out that host machine cost in relation to service providers is another important parameter which has been mainly ignored and under-researched. To address this under-researched issue in this paper, the researchers have proposed a novel scheduling algorithm for cloud computing setting which is based on server cost. The merits of scheduling algorithm proposed here are to enhance income and resource utilization. The results of implementation revealed that the proposed scheduling algorithm improved income and resource utilization for 8% and 3 %, respectively, when compared with the basic method. Nevertheless, it should be pointed out that minor reduction of resource utilization in comparison with the rotational shift method is the partial drawback of the proposed method which should be addressed in future studies.
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Resource Utilization of Workflow Scheduling Algorithms in Public Cloud Dr. T. Lucia Agnes Beena

Resource Utilization of Workflow Scheduling Algorithms in Public Cloud Dr. T. Lucia Agnes Beena

One of the most challenging problems in Clouds is workflow scheduling, i.e., the problem of satisfying the QoS of the user as well as minimizing the energy consumption of workflow execution. Workflow scheduling is the problem of mapping each task to a suitable resource and of ordering the tasks on each resource to satisfy some performance criterion. The traditional scheduling methods try to minimize the execution time (makespan) of the workflows. However, in Clouds, there are many other potential QoS attributes such as execution time, reliability, security and availability. Along with these parameters resource utilization is also to be concentrated in the Cloud workflow scheduling algorithms. Due to the complexities of the development of a general multi-objective scheduling algorithm, many researchers try to propose bi- criteria scheduling algorithms [12].
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Algorithms to Improve Resource Utilization and Request Acceptance Rate in IaaS Cloud Scheduling

Algorithms to Improve Resource Utilization and Request Acceptance Rate in IaaS Cloud Scheduling

To reduce request rejection rate between consumer and provider, and increase resource utilization on cloud provider side, Starvation-Removal and AR-to-BE Conversion algorithms are necessary. Proposed Starvation-Removal algorithm applies technique that provides a maximum limit a lease can be suspended considering constraints’ flexibilities to maximize the chances of their acceptance. Using proposed algorithms, consumers will get suitable lease and their allowable suspension according to their needs. AR-to-BE Conversion algorithm will reduce consumers’ efforts to wait for exact time of lease execution and checking weather lease is provisioned or rejected at all. These algorithms will not handle the situations when system has multiple requests of same type of lease for a single slot and therefore, it will just follow first in first out queue to handle them as proposed in Haizea.
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A Hybrid ACHBDF Load Balancing Method for Optimum Resource Utilization In Cloud Computing

A Hybrid ACHBDF Load Balancing Method for Optimum Resource Utilization In Cloud Computing

Cloud computing provides computing resources to the cloud on demand based and the concept is pay per use”. Cloud computing mainly focused on optimistic resource utilization in fewer cost efforts. Now, these days cloud computing technologies are utilized by most of the IT companies and business organizations. It increases number cloud users as well as computing resources which creates challenges for cloud service providers to maintain optimum utilization of computing resources. Task scheduling methods play an important role in cloud computing. A scheduling machine helps in allocation of the virtual machine to a user task and to maintain the balancing between machine capacity and total task load. Different task scheduling methods are suggested by cloud researchers. This work mainly overcomes the limitation of following methods-
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Load Balancing Using Resource Utilization for Cloud Computing

Load Balancing Using Resource Utilization for Cloud Computing

Load balancing is a technique that distributes the excess dynamic local workload evenly across all the nodes. It is used for achieving a better service provisioning and resource utilization ratio, hence improving the overall performance of the system Incoming tasks are coming from different location are received by the load balancer and then distributed to the data center ,for the proper load distribution. The aim of our project is as follows: To increase the availability of services, To increase the, user satisfaction, To maximize resource utilization. To reduce the execution time and waiting time of task coming from different location. To improve the performance, Maintain system stability, Build fault tolerance system, Accommodate future modification, Avoid overloading of virtual machine. With the demand in Cloud Computing industry, the cloud service providers attracts customers
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A Load Balancing Approach using VM Migration for Increasing the Resource Utilization in Cloud
                 

A Load Balancing Approach using VM Migration for Increasing the Resource Utilization in Cloud  

Abstract:- Cloud computing evolve as a new technology in the field of IT and growing so much faster due to attractive feature like easy to use, dynamic allocation and reallocation of the resources, less costly etc. It provides on demand resources to the client on the rent basis. Cloud support for the utility model, so user has to pay only for the use resources. Since it provide resource to the users and demand for the resources increasing very fast in the past few decades. So load balancing is the main requirement of the cloud system. But load balancing in cloud is more difficult as compare to other technology because it is so large and user requirement can be change dynamically. It helps in optimizing the resource utilization, hence enhancing the system performance. The prime goal of any load balancing approach is to maximize the resource utilization and reducing the number of active server which will further reduce energy consumption and carbon emission. During the past decades several load balancing approach have been proposed. Main objective of these approaches is to increase the system performance by reducing the number of migration. But these approaches are not focusing on the resource wastage. This paper proposed a load
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A DYNAMIC LOAD BALANCING SCHEME FOR ENERGY EFFICIENT RESOURCE UTILIZATION IN CLOUD COMPUTING

A DYNAMIC LOAD BALANCING SCHEME FOR ENERGY EFFICIENT RESOURCE UTILIZATION IN CLOUD COMPUTING

This project mainly used to balance the load to maximize the resource utilization, minimize the execution time, to increase the availability for the popular data acc[r]

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Enhanced Min-Min Task Scheduling Algorithm for Load Balancing in Cloud Data Center

Enhanced Min-Min Task Scheduling Algorithm for Load Balancing in Cloud Data Center

[1]. With the increase in demand for the computation there is a need for additional resources. Scalability improves the throughput when additional resources are added [13]. The scalability of an application is its ability to utilize the available resources without reducing the efficiency of the system. One of the ways to meet this is through load balancing. The method facilitates distribution of workload across resources and multiple computers over the network links. This helps to accomplish optimization in resource utilization, maximization of time and avoid overload of processors [8]. The services provided should be fault tolerant, highly scalable, available, flexible, less overhead, minimum cost [5]. Fundamental to this issue lays the implementation of an effective method for load balancing.
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An efficient Task Scheduling and Load Balancing in Cloud Computing using KD Tree Algorithm

An efficient Task Scheduling and Load Balancing in Cloud Computing using KD Tree Algorithm

There are two categories of LB algorithms presents in CC such as static and dynamic LB [10], where various meta-heuristic algorithms namely Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Genetic Algorithm (GA) [11-13] are developed to achieve better LB. Although, meta-heuristic algorithms attained LB effectively, it renders less useful to end users in realistic computational infrastructures due to high time complexity. The task's execution time is reduced and resource utilization are maximized by using various types of priority methods namely VM priority, task priority and sorting methods, that are involved in several heuristic algorithms [14,15]. In this research work, KD-Tree Algorithm is developed and implemented to balance the workloads among resources by partitioning the virtual environment. The LB is trigged when the server is overloaded and then the region limit is readjusted by changing the split coordinates, that are stored in the KD-Tree. In simulation testing, the prototypes are developed and used for testing their efficiency, where the performance of KD-Tree is compared to existing heuristic algorithms in terms of makespan, task migration and energy consumption. The load of a VM is identified by using the threshold value of KD-Tree algorithm. The task is removed in the particular VM which is having overloaded, and according to the deadline of task execution, a task is allocated to the identified VM.
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RESOURCE SCHEDULING IN CLOUD ENVIRONMET: A SURVEY

RESOURCE SCHEDULING IN CLOUD ENVIRONMET: A SURVEY

In [86], authors presented a Virtual Com- puting Laboratory framework model using the concept of private cloud by extending the open source IaaS solution Eucalyptus. A mapping al- gorithm for VMs based on rules and the princi- ples of set theoretic is also presented. The algo- rithmic design is projected towards being able to autonomic plotting between VMs and datacenter resources. A system based on virtualization for the allocation of data center resources dynami- cally on the basis of demands of the application is presented [87]. In parallel, the optimized number of servers henceforth supports the green comput- ing. The concept of “skewness” is put forward to determine the non-proportionality in the multi- dimensional resource utilization of a server. It is also shown that different types of workloads can be combined efficiently and overall utilization of server resources is improved upon minimiza- tion of skewness. A group of heuristics is also developed that is able to effectively save the en- ergy while avoiding the system overloading. Ef- ficiency of this algorithm was adjudged through the trace driven simulation and henceforth the results of experiments. In [88], a combination of ant colony optimization (ACO) and VM dynamic forecast scheduling (VM_DFS) to perform VM scheduling is presented. In this algorithm through analysis of historical memory consumption in each PM, future memory consumption forecast of VMs and their allocation on the cloud resources is performed. This methodology is experimented in MATLAB for both homogeneous and heteroge- neous mode and results indicate that the proposed algorithm produces lower resource wastage than other traditional approaches and better load bal- ancing among PMs.
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Task Scheduling in Public Cloud: A Review

Task Scheduling in Public Cloud: A Review

Priority based job scheduling algorithm using IBA and EASY algorithm has been purposed by Kalka Dubey et al. [12]. FCFS, SJF and LJF algorithm are used for task scheduling in cloud computing, but there is a problem of starvation. To overcome this problem and for best utilization of resources, an improved backfill algorithm using balanced spiral method was introduced. It gives same priority to all the tasks and these tasks are scheduled in FCFS order. But in some cases, there are number of tasks, which are more important than others and are scheduled according to their priority. In such cases, priority based improved backfill algorithm is used. It assigns different priority to different tasks and maximizes the resource utilization ratio according to user requirements. Final result is obtained by applying IBM and EASY Backfill algorithm on complete task sequence.
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Pso-Based Task Scheduling Algorithm Using Adaptive Load Balancing Approach For Cloud Computing Environment

Pso-Based Task Scheduling Algorithm Using Adaptive Load Balancing Approach For Cloud Computing Environment

acceptance ratio and throughput by submitting the heterogeneous tasks. The results of implement were conducted using the CloudSim tool and show that proposed PSO-BOOST algorithm outperforms compared with existing PSO, adaptive PSO, ABC and improved min-min load balancing algorithm. Fei Luo et al. [4] suggested an improved PSO strategy based on adaptive loads, in which adaptive loads use to make the load change with the increase of the number of iterations as well as also introduces random weights in the later stage. To avoid condition when the particle swarm technique could be stuck in the native optimal that originates to the advanced stage. The proposed strategy applied to task scheduling to achieve better scheduling plan in cloud computing. The results of experiments conducted using CloudSim simulator show that the overall performance of the proposed algorithm better than standard PSO. Thanaa S. Alnusairi et al. [15] have proposed a bio-inspired the scheduling algorithm for load balancing names as Bin-LB- PSOGSA. This algorithm enables scheduling method to optimize the stability load level on VMs. This algorithm mapped the task to VMs according to the length of submitted load and processing speed of VM. The implementation outcomes show that the Bin-LB-PSOGSA increase load over time and decrease processing speed of VM, which shows that proposed algorithm, performs more efficiently in keeping the load balanced over time. Fatemeh Ebadifard et al. [16] introduced a method based on the PSO where tasks are independent and non-preemptive. The proposed method enhanced the efficiency of the PSO standard method using the concept of load balancing technique. The conducted results indicate that the proposed algorithm increases the makespan by 33% and decrease the resource utilization by 22% compared PSO standard method. Gabi Hua et al. [17] have proposed adaptive multi-objectives task scheduling algorithm based on PSO techniques to optimize the processing time and the transmission time. The proposed PSO-based AMTS outperform quasi-optimal solutions with compared to the genetic algorithm. Rajni Aron et al. [18] have introduced hyper- heuristics resource scheduling strategy on the ground of PSO techniques to secure scheduling jobs on suitable resources without disturbing any of the security standards. The simulations were conducted using the GridSim Toolkit. The performance of proposed strategy outperforms than the existing algorithms in term of makespan as well as the cost of the user’s application. In this paper, we proposed PSO-based task scheduling algorithm using adaptive scheduling approach to solve the limitation of existing discussed algorithms.
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Power Aware Task Scheduling in Compute Cloud

Power Aware Task Scheduling in Compute Cloud

Cloud computing has become an attractive computing paradigm in recent years to offer on demand computing resources for users worldwide. Computing resources are delivered in the form of virtual machines. In such a scenario, task scheduling algorithms play an important role to schedule the tasks effectively to achieve reduction in power consumption and makespan with improvement in resource utilization. Many task scheduling algorithms are introduced to improve energy efficiency of data center. In our work, we have proposed and discussed a power aware dependent task scheduling (PADTS) algorithm and compare it with existing ones.
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Fast Handling of Cloud Data Based on user Level Virtualization and Resource Scheduling

Fast Handling of Cloud Data Based on user Level Virtualization and Resource Scheduling

Many believe the future of gaming lies in the cloud, namely Cloud Gaming, which renders an interactive gaming application in the cloud and streams the scenes as a video sequence to the player over Internet. This paper proposes GCloud, a GPU/CPU hybrid cluster for cloud gaming based on the user-level virtualization technology. Specially, we present a performance model to analyze the server-capacity and games’ resource-consumptions, which categorizes games into two types: CPU-critical and memory-io-critical. Consequently, several scheduling strategies have been proposed to improve the resource-utilization and compared with others. Simulation tests show that both of the First-Fit-like and the Best-Fit-like strategies outperform the other(s); especially they are near optimal in the batch processing mode. Other test results indicate that GCloud is efficient: An off-the-shelf PC can support five high-end video-games run at the same time. In addition, the average per-frame processing delay is 8_19 ms under different image-resolutions, which outperforms other similar solutions.
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Load Balancing Algorithm In Task Scheduling Process Using Cloud Computing

Load Balancing Algorithm In Task Scheduling Process Using Cloud Computing

virtualization, distributed computing, networking, software and web services. A cloud consists of several elements such as clients, datacenter and distributed servers. It includes fault tolerance, high availability, scalability, flexibility, reduced overhead for users, reduced cost of ownership, on demand services etc. Central to these issues lies the establishment of an effective load balancing algorithm. The load can be CPU load, memory capacity, delay or network load. Load balancing is the process of distributing the load among various nodes of a distributed system to improve both resource utilization and job response time while also avoiding a situation where some of the nodes are heavily loaded while other nodes are idle or doing very little work. Load balancing ensures that all the processor in the system or every node in the network does approximately the equal amount of work at any instant of time. This technique can be sender initiated, receiver initiated or symmetric type (combination of sender initiated and receiver initiated types).Our objective is to develop an effective load balancing algorithm using to maximize or minimize different performance parameters (throughput, latency for example) for the clouds of different sizes (virtual topology depending on the application requirement).
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Time and Resource Efficient Task Scheduling in Cloud Computing Environment

Time and Resource Efficient Task Scheduling in Cloud Computing Environment

Abstract - Cloud computing has become a new age technology that has got huge potentials in enterprises and markets. Cloud Computing has Large Scale Distributed Infrastructure which is accessible and scalable infrastructure. Cloud computing provides a pay as you go model in which the user has to pay for the services he uses. However one of the major challenges in cloud computing is related to optimizing the resources being allocated. Because of the uniqueness of the model, resource allocation should be performed with the objective of minimizing the costs associated with it. This optimized use of cloud can only be done by efficient and effective algorithm to select the best resources. In this paper, the Task Based allocation of resources is used to minimize the makespan of the cloud system and also to increase the resource utilization. The simulation is done using CloudSim and results show that TBA algorithm reduces the makespan, execution time and cost as compared to Random Algorithm and FCFS algorithm.
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Research on Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization

Research on Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization

Ant Colony Optimization (ACO) [5,6] is a classical algorithm for solving combinatorial optimization problems and is effective for solving cloud computing task scheduling problems. However, although the ant colony algorithm can shorten makespan, it does not consider the problem of system load balancing, and the algorithm may fall into a local optimal solution. The purpose of load balancing is to improve resource utilization while reducing makespan. Inappropriate task scheduling strategy may make the load between virtual machines unbalanced [7] .
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Resource Optimization Using Cloud Scheduling

Resource Optimization Using Cloud Scheduling

Pengze Guoet al Suggested that Cloud registering need developed Concerning illustration a capable stage for giving registering assets in the past decade. Creating workflow planning calculations might proficiently decrease the cosset about executing assignments in cloud frameworks. The features from claiming versatility What's more heterogeneity about cloud registering achieve tests to planning methodologies. To ongoing workflows, lessening execution the long haul Furthermore lessening execution expense are two clashing destinations. To deliver this issue, we recommend in this paper an enhanced ongoing workflow planning algorithm In light of particle swarm optimization (PSO). Unique in relation to customary planning heuristics which depend on the introductory asset pool, their algorithm could adaptively streamline the resource utilization. [7]. Wei-Neng Chen et al Suggested that Cloud computing has risen similarly as an capable registering standard that empowers clients should right registering administrations anyplace for interest. It gives a adaptable best approach should execute computation-intensive workflow requisitions around a pay-per-use foundation. Since clients would additional concerned on the fulfillment for personal satisfaction for administration (QoS) for cloud systems, those cloud workflow planning issue that addresses diverse QoS prerequisites from claiming clients need turned a paramount and testing issue to workflow administration clinched alongside cloud registering. In that paper, they tackle a cloud workflow planning issue which empowers clients with characterize Different QoS imperatives similar to those due date constraint, those plan constraint, and the dependability demand. It also empowers clients with point out particular case favored QoS parameter Similarly as those streamlining objective. A set-based PSO (S-PSO) approach is suggested for this planning issue. Similarly as those allotment about administration instances could make viewed, Previously, S-PSO will be characteristic to those acknowledged issue. Over addition, the S-PSO gives an compelling lifestyle to take advantage about issue built heuristics on further quicken hunt. They defined penalty based fitness function to address this problem imperatives also incorporated those S-PSO with seven heuristics. An discrete adaptation of the far reaching, Taking in PSO (CLPSO) calculation [8].
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Deadline Constrained Task Scheduling with Load Balancing in Cloud Computing Environment

Deadline Constrained Task Scheduling with Load Balancing in Cloud Computing Environment

Cloud based applications suffer from the latency problem existing in the network. The current technology is based on live migration of virtual machines for proper resource allocation to jobs. But, the migrations add overhead to the network. The common approach to handle this problem is to develop some possible schedules and choose the most suitable one which would minimize migration and maximize resource utilization. Yet another requirement is that jobs are to be completed within user specified deadlines. Thus, the present study focuses on developing task scheduling strategies that can ensure completion of tasks within deadlines and optimal resource utilization through load balancing in order to minimize migration of tasks.
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