stochastic models of load balancing and scheduling in cloud computing cluster pdf

Stochastic Models Of Load Balancing And Scheduling In Cloud Computing Cluster Pdf

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Metrics details. With the rapid increase of user access, load balancing in cloud data center has become an important factor affecting cluster stability. From the point of view of green scheduling, this paper proposed a virtual machine intelligent scheduling strategy based on machine learning algorithm to achieve load balancing of cloud data center.

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Heavy traffic optimal resource allocation algorithms for cloud computing clusters

To browse Academia. Skip to main content. By using our site, you agree to our collection of information through the use of cookies. To learn more, view our Privacy Policy. Log In Sign Up. Download Free PDF. Download PDF. A short summary of this paper. The latest vision of large distributed computing is "Cloud".

The term "cloud" originates from the world of telecommunications when providers began using virtual private network VPN services for data communications. Figure 1 Cloud computing [4]Cloud computing is an on demand service in which shared resources, information, software and other devices are provided according to the client's requirement at specific time.

Cloud computing is internet based computing, whereby shared resources, software and information are provided to computers and other devices on-demand, like a public utility. The process in which the load is divided among several nodes of distributed system is called load balancing in cloud computing.

Load balancing assists the cloud computing through algorithms. Lots of work has been done to balance the load in order to improve performance and avoid over utilization of resources. Geethu Gopinath and Shriram K Vasudevan [2] worked on "process of load balancing in cloud computing using genetic algorithm". In this paper, author focused on various load balancing algorithms, the topic of load balancing in Cloud Computing are researched and compared to provide a gist of the latest way in this research area.

By using Genetic Algorithm the balance is most flexible which is represented here. Zhang et al. The characteristics of this fast adaptive balancing method are to be adjusted the workload between the processors from local areas to global areas. According to the difference of workload, the arrangements of the cells are obtained.

But the main workload concentrates on certain cells so that the procedure of adjusting the vertices of the grid can be very long because of the local workload can be considered. This problem can be avoided by the fast load balancing adaptive method. Dhinesh et al. Here in this session well load balance across the virtual machines for maximizing the throughput. The load balancing cloud computing can be achieved by modeling the foraging behavior of honey bees.

This algorithm is derived from the behavior of honey bees that uses the method to find and reap food. In bee hives, there is a class of bees called the scout bees and the another type was forager bees. Figure 2 Challenge issues in cloud [15]Suriya Begum et. The system jointly addresses the routing as well as task scheduling and also focuses on the issues pertaining to resource allocation.

A novel mathematical model considering stochastic model for load balancing and scheduling in cloud computing clusters has been developed. A cloud system consists of a number of networked servers. Each of the servers may host multiple Virtual Machines. Bhaskar [10] has developed a novel Round Robin Algorithm for load balancing in cloud computing. Here, skewness measurement technique introduced along with load balancing using Round Robin Algorithm, in order to provide enhanced performance which results in Green Computing.

Skewness concept is used to measure the utilization rate of a node. Cloud partitioning is the process of dividing a huge public cloud into sub partitions. Each cloud partition contains some number of nodes; one node might be working for a long time while other nodes are sitting idle.

Despite a node being utilized for a long time the cloud partition status will be showing normal. The load can be a memory, CPU capacity, network or delay load. It is always required to share work load among the various nodes of the distributed system to improve the resource utilization and for better performance of the system.

This can help to avoid the situation where nodes are either heavily loaded or under loaded in the network. Load balancing is the process of ensuring the evenly distribution of work load on the pool of system node or processor so that without disturbing, the running task is completed.

There are mainly two types of load balancing algorithms- Static Algorithm -In static algorithm the traffic is divided evenly among the servers.

This algorithm requires a prior knowledge of system resources, so that the decision of shifting of the load does not depend on the current state of system. Static algorithm is proper in the system which has low variation in load. Dynamic Algorithm -In dynamic algorithm the lightest server in the whole network or system is searched and preferred for balancing a load.

For this real time communication with network is needed which can increase the traffic in the system. Here current state of the system is used to make decisions to manage the load [5]. Hence the node that makes the provisioning decision also governs the category of algorithm to be used. There can be three types of algorithms that specify which node is responsible for balancing of load in cloud computing environment [16,17].

Centralized Load Balancing-In centralized load balancing technique all the allocation and scheduling decision are made by a single node. This node is responsible for storing knowledge base of entire cloud network and can apply static or dynamic approach for load balancing [9].

This technique reduces the time required to analyze different cloud resources but creates a great overhead on the centralized node. Also the network is no longer fault tolerant in this scenario as failure intensity of the overloaded centralized node is high and recovery might not be easy in case of node failure [6,7].

Distributed Load Balancing-In distributed load balancing technique, no single node is responsible for making resource provisioning or task scheduling decision [11]. There is no single domain responsible for monitoring the cloud network instead multiple domains monitor the network to make accurate load balancing decision. Every node in the network maintains local knowledge base to ensure efficient distribution of tasks in static environment and redistribution in dynamic environment.

Hierarchical Load Balancing-Hierarchical load balancing involves different levels of the cloud in load balancing decision. Such load balancing techniques mostly operate in master slave mode [13]. These can be modeled using tree data structure wherein every node in the tree is balanced under the supervision of its parent node.

Master or manager can use light weight agent process to get statistics of slave nodes or child nodes. Based upon the information gathered by the parent node provisioning or scheduling decision is made. Figure 4 shows comparisons of various cloud load balancing methods based on various parameters such as static environment, dynamic environment and based on spatial distribution of loads such as centralized load balancing, distributed load balancing and hierarchal load balancing.

With proper load balancing waiting time can be kept to a minimum which will further maximize the response time. In this research paper, comparison of different load balancing algorithms is carried out on the basis of certain parameters. The above comparison shows that static load balancing algorithms are more stable than dynamic algorithms but due to capability of performing accurate in distributed systems, dynamic load balancing is chosen over static load balancing algorithms.

In future this analysis further can also help in designing new load balancing algorithms. In future wore we will developed in efficient load balancing method for cloud computing, which helps to increase the cloud performance. To check the validation of proposed method we will compare existing methods such as round robin, honey beee, ant colony, PSO, ESWLC with proposed method, based on various parameters such as waiting time, turnaround time, load balancing time.

Related Papers. By sheida dayani. By Dr. Download pdf. Remember me on this computer. Enter the email address you signed up with and we'll email you a reset link. Need an account? Click here to sign up.

An Empirical Survey on Load Balancing: A Nature-Inspired Approach

Load balancing algorithms and job allocations are main research problems in areas of resource management of future internet. In this paper, we introduce a load balancing model for future internet. We formulate the static load balancing problem in the model proposed above as noncooperative game among users and cooperative game among processors. Based on this model, we derive a load balancing algorithm for computing center. Finally, we execute the algorithm presented in this paper with another three algorithms for comparison purpose. The advantages of our algorithm are better scalability to the model, improving system performance, and low cost on maintaining system information.

We consider a stochastic model of a cloud computing cluster, where jobs arrive according to a stochastic process and request virtual machines (VMs), which are​.

Virtual machine scheduling strategy based on machine learning algorithms for load balancing

Aghajani, P. Robert, and W. Sun , A large scale analysis of unreliable stochastic networks , The Annals of Applied Probability , vol. DOI : Alanyali and M.

Task scheduling and energy efficiency seem to be the necessary design requirements for current computing systems in recent years. It extends from single servers to data centers and clouds, as they consume large amounts of electrical power. For this reason, an effective energy management for cloud data centers is essential.

We consder a stochastc odel of a cloud coputng cluster, where jobs arrve accordng to a stochastc process and request vrtual achnes VMs, whch are specfed n ters of resources such as CPU, eory and storage space. Whle there are any desgn ssues assocated wth such systes, here we focus only on resource allocaton probles, such as the desgn of algorths for load balancng aong servers, and algorths for schedulng VM confguratons. Gven our odel of a cloud, we frst defne ts capacty,. We then study the delay perforance of these alternatve algorths through sulatons. A cloud coputng platfor can provde a varety of resources, ncludng nfrastructure, software, and servces, to users n an on-deand fashon.

We consder a stochastc odel of a cloud coputng cluster, where jobs arrve accordng to a stochastc process and request vrtual achnes VMs , whch are specfed n ters of resources such as CPU, eory and storage space. Whle there are any desgn ssues assocated wth such systes, here we focus only on resource allocaton probles, such as the desgn of algorths for load balancng aong servers, and algorths for schedulng VM confguratons. Gven our odel of a cloud, we frst defne ts capacty,.

Stochastic Models of Load Balancing and Scheduling in Cloud Computing Clusters

Since the dawn of humanity man tried to mimic several animals and their behavior be it in the age of hunting of while designing the aero plane. Human brain holds a significant amount of power in observing the species around him and trying to incorporate their behavior in several walks of life. This mimicking has helped human to evolve into beings which we are now. Some typical examples include navigation systems, designing several gadgets like aero planes, boats, etc. These days these inspirations are several, and their inspiration is being utilized in several fields like operations, supply-chain management, machine learning and several other fields. The similar kind of approach has been discussed in this paper where we tried to analyze different phenomenon in nature and how different algorithms were designed from these and how these can ultimately be used to solve different issues in cloud balancing.

 Стресс - это убийца, Сью. Что тебя тревожит. Сьюзан заставила себя сесть. Она полагала, что Стратмор уже закончил телефонный разговор и сейчас придет и выслушает ее, но он все не появлялся. Пытаясь успокоиться, она посмотрела на экран своего компьютера.

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Journal of Applied Mathematics

Бедлам. Так он и. Очередь из десяти человек, толкотня и крик. Испания не славится эффективностью бюрократического аппарата, и Беккер понял, что ему придется простоять здесь всю ночь, чтобы получить информацию о канадце. За конторкой сидела только одна секретарша, норовившая избавиться от назойливых пациентов. Беккер застыл в дверях, не зная, как поступить.

Он повернулся: из полуоткрытой двери в кабинку торчала сумка Меган. - Меган? - позвал. Ответа не последовало.  - Меган. Беккер подошел и громко постучал в дверцу. Тишина.

Зеленоватое, оно было похоже на призрак. Это было лицо демона, черты которого деформировали черные тени. Сьюзан отпрянула и попыталась бежать, но призрак схватил ее за руку. - Не двигайся! - приказал. На мгновение ей показалось, что на нее были устремлены горящие глаза Хейла, но прикосновение руки оказалось на удивление мягким. Это был Стратмор.

A New Method of Scheduling Tasks in Cloud Computing

 Плевал я на Стратмора! - закричал Чатрукьян, и его слова громким эхом разнеслись по шифровалке.

Наклонные стены помещения, образуя вверху широкую арку, на уровне глаз были практически вертикальными. Затем они приобретали как бы полупрозрачность, завершаясь у пола непроницаемой чернотой - посверкивающей черной глазурью кафеля, отливавшей жутковатым сиянием, создававшим какое-то тревожное ощущение прозрачности пола. Черный лед.

В другой стороне комнаты зазвонил телефон. Это был Джабба. ГЛАВА 107 Сьюзан понятия не имела, сколько прошло времени.


Gaspar Q.

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