*2.1.1 Centralized queueing model for load balancing*

In this mechanism, homogeneous servers with finite buffer sizes are used as shown in **Figure 7**. The load balancer receives request from the user and redirects the request among servers using one of these routing policy:


Zhang and Fan [9] compared these policies in terms of rejection rate and system response time. They analyzed that these algorithms perform well when traffic is light. But when web traffic becomes high, shortest queue policy performs better than random and RR policy. The number of rejections in RR and random policy is increased as the traffic increases. Singh and Kumar [10] presented a queueing algorithm for measuring the overloading and serving capacity of server in distributed load balancing environment. The algorithm performs better in both homogeneous and heterogeneous environment than the remaining capacity (RC) and server content based queue (QSC) algorithms.

#### *2.1.2 Distributed queueing model for load balancing*

These mechanisms address the network latency issue also, which avoids network congestion. The queueing models follow certain arrival and distribution rules to distribute the requests. Zhang and Fan [9] suggested that distributed queueing models perform well in heavy traffic conditions. Routing decisions are taken on the basis of queue length differences of web servers. The collected information is used in traffic distribution for improving the performance of web servers. Singh and Kumar [11] suggested that task completion time directly affects the queue length of the web server. They presented a model based on the ratio factor of the task's average completion time. The model is compared with the model presented by Birdwell et al. [12], and it performs better for two performance metrics: average queue length and average waiting time of web servers.

Li et al. [13] analyzed network delay and presented a delay controlled load balancing approach for improving network performance. However, the approach has limited applicability and is suitable for stable path states.

Kamali et al. [14] used queueing theory to monitor the network traffic and its simulation is performed in a homogeneous as well as heterogeneous network environment. The traffic monitoring is required for the calculation of confidence and efficiency parameters from steady operations of the network. Based on queuing theory and little's law network congestion rate is balanced.

#### **2.2 Resource selection and task allocation**

Many researchers have addressed the problem of resource selection and task allocation for the fair perspective of load balancing. It is the responsibility of load balancer to map resource and task before actual execution as shown in **Figure 8**.

The resource management consists of two major functions: resource provisioning and resource scheduling. In resource provisioning, the user submits task to the broker with various predefined QoS constraints. The broker is responsible to find the suitable resource for task execution. The resource scheduling is all about mapping and execution of task on the appropriate resource . It comprises of three major functions: resource mapping, resource execution and resource monitoring as shown in **Figure 9**. Various types of resources that need to be managed are shown in **Figure 10**.

Hao et al. [15] categorized the resource in three categories—underloaded, normal loaded, and overloaded. The scheduler assigns the task to underloaded or normal-loaded resources only. Chang et al. [16] categorized the resources into L discrete levels for selecting the fittest resource for task execution. Arabnejad and Barbosa [17] presented a budget-based task scheduling and calculated the worthiness of all the resources for resource selection.

Naik et al. [18] presented a value function to select a resource for task execution. A value function is calculated using completion ratio and historic information of a resource. For minimizing the data transfer between the resources, Cheng et al. [19] used a hypergraph which identifies task and data dependency. Tasks that use similar data are assigned to the same resource to decrease the cost indirectly. AbdElrouf et al. [20] used a genetic algorithm for producing chromosomes. A fitness function

**Figure 8.** *Task and resource allocation model.*

**67**

scheduling algorithm.

**Figure 10.**

*Types of resources for Management.*

the algorithm efficiency and applicability.

selected by global dispatcher for scheduling IoT jobs.

*Analysis of Effective Load Balancing Techniques in Distributed Environment*

is used for generating chromosomes. Individuals who have higher fitness value will

selection method for divisible workloads. The method assigns the appropriate resource in terms of cost from the list of available resources. Shah et al. [22] also claimed a linear programming-based resource allocation method for divisible workloads. The job is categorized in appropriate sizes to allocate on available

Murugesan and Chellappan [21] suggested deadline and budget-based resource

resources. Singh and Kumar [23] improved the resource selection method presented by Singhal et al. [24] by determining the task workload and resource availability, respectively. Ang et al. [25] introduced a resource allocation mechanism by consid-

Various researchers suggested numerous techniques for heterogeneous task allocation. Raman et al. [26] provide improvements of traditional round robin (RR) task scheduling which performs well when all the resources have equal serving capacity. In heterogeneous environment, it does not provide prominent results. Therefore, a weight is assigned to each server which represents the priority of selecting a server. The algorithm performs well in distributing the load more efficiently then RR

Pham and Huh [27] analyzed the cloud-fog environment and presented a task scheduling algorithm. The suggestion behind the presented algorithm is the association between fog nodes and cloud nodes to decrease the makespan and price of cloud resources. If the computation is not feasible on fog node, then tasks are executed on cloud node. Several constraints like deadline and budget can enhance

Wu [28] presented a task scheduling for embedded systems to enhance the performance of real applications in CloudIoT paradigm. These approaches are used in real-time networks where time constraints are strictly followed. The algorithm increases the scheduling success rate of real-time task on heterogeneous web servers. Moschakis and Karatza [29] analyzed the workload generated by IoT devices and scheduled them on multi cloud-based system. The least loaded server is

only proceed for further chromosome reproduction.

ering the requirement of user as well as service provider.

*DOI: http://dx.doi.org/10.5772/intechopen.91460*

**Figure 9.**

*Resource management classifications.*

*Analysis of Effective Load Balancing Techniques in Distributed Environment DOI: http://dx.doi.org/10.5772/intechopen.91460*

#### **Figure 10.**

*Linked Open Data - Applications, Trends and Future Developments*

theory and little's law network congestion rate is balanced.

**2.2 Resource selection and task allocation**

ness of all the resources for resource selection.

Kamali et al. [14] used queueing theory to monitor the network traffic and its simulation is performed in a homogeneous as well as heterogeneous network environment. The traffic monitoring is required for the calculation of confidence and efficiency parameters from steady operations of the network. Based on queuing

Many researchers have addressed the problem of resource selection and task allocation for the fair perspective of load balancing. It is the responsibility of load balancer to map resource and task before actual execution as shown in **Figure 8**. The resource management consists of two major functions: resource provisioning and resource scheduling. In resource provisioning, the user submits task to the broker with various predefined QoS constraints. The broker is responsible to find the suitable resource for task execution. The resource scheduling is all about mapping and execution of task on the appropriate resource . It comprises of three major functions: resource mapping, resource execution and resource monitoring as shown in **Figure 9**. Various types of resources that need to be managed are shown in **Figure 10**. Hao et al. [15] categorized the resource in three categories—underloaded, normal loaded, and overloaded. The scheduler assigns the task to underloaded or normal-loaded resources only. Chang et al. [16] categorized the resources into L discrete levels for selecting the fittest resource for task execution. Arabnejad and Barbosa [17] presented a budget-based task scheduling and calculated the worthi-

Naik et al. [18] presented a value function to select a resource for task execution. A value function is calculated using completion ratio and historic information of a resource. For minimizing the data transfer between the resources, Cheng et al. [19] used a hypergraph which identifies task and data dependency. Tasks that use similar data are assigned to the same resource to decrease the cost indirectly. AbdElrouf et al. [20] used a genetic algorithm for producing chromosomes. A fitness function

**66**

**Figure 9.**

**Figure 8.**

*Task and resource allocation model.*

*Resource management classifications.*

*Types of resources for Management.*

is used for generating chromosomes. Individuals who have higher fitness value will only proceed for further chromosome reproduction.

Murugesan and Chellappan [21] suggested deadline and budget-based resource selection method for divisible workloads. The method assigns the appropriate resource in terms of cost from the list of available resources. Shah et al. [22] also claimed a linear programming-based resource allocation method for divisible workloads. The job is categorized in appropriate sizes to allocate on available resources. Singh and Kumar [23] improved the resource selection method presented by Singhal et al. [24] by determining the task workload and resource availability, respectively. Ang et al. [25] introduced a resource allocation mechanism by considering the requirement of user as well as service provider.

Various researchers suggested numerous techniques for heterogeneous task allocation. Raman et al. [26] provide improvements of traditional round robin (RR) task scheduling which performs well when all the resources have equal serving capacity. In heterogeneous environment, it does not provide prominent results. Therefore, a weight is assigned to each server which represents the priority of selecting a server. The algorithm performs well in distributing the load more efficiently then RR scheduling algorithm.

Pham and Huh [27] analyzed the cloud-fog environment and presented a task scheduling algorithm. The suggestion behind the presented algorithm is the association between fog nodes and cloud nodes to decrease the makespan and price of cloud resources. If the computation is not feasible on fog node, then tasks are executed on cloud node. Several constraints like deadline and budget can enhance the algorithm efficiency and applicability.

Wu [28] presented a task scheduling for embedded systems to enhance the performance of real applications in CloudIoT paradigm. These approaches are used in real-time networks where time constraints are strictly followed. The algorithm increases the scheduling success rate of real-time task on heterogeneous web servers. Moschakis and Karatza [29] analyzed the workload generated by IoT devices and scheduled them on multi cloud-based system. The least loaded server is selected by global dispatcher for scheduling IoT jobs.

Grandinetti et al. [30] presented an offline mathematical formula to improve task scheduling and average waiting time. Xu et al. [31] presented a task scheduling algorithm which determines crossover and mutation operations for mapping between tasks and resources. Kamalinia and Ghaffari [32] addressed the task scheduling as an NP complete problem. They also used a genetic algorithm to design task scheduling problem to improve makespan and resource efficiency. The presented scheduling algorithm reduces the communication cost among processors by using meta-heuristic methods.

### **2.3 Load measurement**

The load measurement is very important and crucial activity in distributed environment. Various load balancing algorithms determine resource load condition before real implementation of task. Various performance metrics like fault tolerance, waiting time, response time, etc. can effectively be optimized by measuring the current load of a resource. Many authors addressed this issue and presented various resource provisioning techniques for effective distribution of incoming load.

Patel and Tripathy [33] categorized the resources in three categories: underloaded, normal-loaded, and overloaded to manage the load of available resources. Before assigning a task to a resource, the scheduler checks the current load of each resource and selects the underloaded or normal-loaded resource for task execution. Task length, processing element capacity, and deadline constraints are the factors that are considered to determine the current load of each resource. If a resource becomes overloaded, the unfinished tasks are shifted to another suitable resource for completing their execution. Checkpoint mechanism is used to save and resume the task state which greatly decreases the average response time and task resubmission time and improves the system throughput.

Liu et al. [34] advised that resource provisioning techniques may balance the resource load effectively. They presented peer load balance provision compares the demand and resource capacity by considering requirement of both customer and service provider. The presented mechanism reduced the cost and average response time than other existing methods.

Rathore and Chana [35] determined a dynamic threshold value based on standard deviation for load balancing and job migration. For job migration, the resources are categorized and the average load of each cluster is compared with processing element's threshold value. For load balancing, tasks are selected randomly either from underloaded or overloaded resource collection.

Kaushik and Vidyarthi [36] consider various parameters for effective job scheduling and resource allocation. The presented model selects the best cluster in terms of increased system reliability and reduced energy consumption and balances the system load efficiently. The customer can prioritize their choices to select the suitable cluster for task execution. An effective approach for determining job migration overhead can increase the model adaptability in real scenarios.

#### **2.4 Cost optimization**

Load balancing algorithm maps task to various heterogeneous resources based on predefined objectives. The major objective of load balancer is to optimize task completion time, resource cost, and its utilization. Several authors addressed the cost issue and provide possible solutions for its optimization.

Garg and Singh [37] suggested an adaptive workflow scheduling (AWS) by considering resource cost and communication cost between task and resources. Due to heterogeneous nature of resources, final cost is calculated periodically. Arabnejad and Barbosa [17] presented a task scheduling algorithm which works in two

**69**

*Analysis of Effective Load Balancing Techniques in Distributed Environment*

phases - task selection phase and processor selection phase. For selecting the task, priority is assigned by computing the rank. For processor selection, worthiness of all processors is calculated and selects the processor with highest worthiness value. Chaisiri et al. [38] analyzed the resource provisioning phases and suggested that reservation method provides reduced cost than on demand methods. Broadly, there

In the first stage, the cloud broker arranged the resources in advance without experiencing the customer requirement. In the second phase, the customer requirement and resource cost are comprehended, and the resource overutilization or underutilization is identified. If customer requirement is greater than reserved resources, the broker could request for additional resources on pay-per-use basis. Here, the on demand phase started. In on demand phase, the customer must know the appropri-

Singh and Kumar [39] presented a cost optimization method based on process activity. Processing cost and waiting time are determined by using activity time, resource utilization, and variability factor to check the method efficiency. Bittencourt and Madeira [40] presented a cost optimization method for hybrid cloud. The clouds can be categorized in three categories based on resource availability: public cloud, private cloud, and hybrid cloud. A user can use the services of public cloud by using pay-per-use method. Private clouds belong to individuals and offer free variety of services. In hybrid cloud, resources from public cloud are aggregated as per requirement. Bittencourt and Madeira [40] identified the method for appropriate resource. Cao et al. [41] analyzed that each task is different from each other in cloud environment. They suggested an activity-based task scheduling approach for task reduction. The presented algorithm performs well than traditional task assignment

Efficient resource provisioning plays a vital role in reducing the cost of task execution. Suresh and Varatharajan [42] presented a particle swarm optimization (PSO)-based resource provisioning algorithm. PSO is adopted to select the appropriate resource for cost optimization. Three performance metrics task execution time, memory usage, and cost are evaluated and compared with other existing methods. The simulation result shows that the presented PSO-based algorithm provides minimum execution time and memory usage with least cost than other state-of-the-art methods. Salehan et al. [43] suggested auction-based resource allocation to meet the requirement of the customer and service provider. At the time of scheduling, resources are assigned to users that have highest bids. The algorithm provides highest profit and satisfies both the customers and service providers for multiple criteria than other existing methods. Nezarat and Dastghaibyfard [44] map the resource allocation mechanism to economic-based supply and demand problem which provides better functionality with 17% profit with other existing methods. Netjinda et al. [45] suggested a task scheduling for workflow applications. These workflow applications consist of dependent task with deadline constraints. The aim is to select the least cost cloud resource through PSO for workflow-based task execution. The effective task scheduling decreases the execution time which directly affects the final cost. By considering communication overhead, the model

effectiveness and applicability can be increased in real cloud environment.

ate future requirement which is difficult to estimate in cloud environment.

*DOI: http://dx.doi.org/10.5772/intechopen.91460*

are three stages in resource provisioning:

approaches in terms of cost reduction.

• Resource reservation

• Resource expanding

• Resource on demand

### *Analysis of Effective Load Balancing Techniques in Distributed Environment DOI: http://dx.doi.org/10.5772/intechopen.91460*

phases - task selection phase and processor selection phase. For selecting the task, priority is assigned by computing the rank. For processor selection, worthiness of all processors is calculated and selects the processor with highest worthiness value.

Chaisiri et al. [38] analyzed the resource provisioning phases and suggested that reservation method provides reduced cost than on demand methods. Broadly, there are three stages in resource provisioning:

• Resource reservation

*Linked Open Data - Applications, Trends and Future Developments*

sion time and improves the system throughput.

either from underloaded or overloaded resource collection.

overhead can increase the model adaptability in real scenarios.

cost issue and provide possible solutions for its optimization.

time than other existing methods.

**2.4 Cost optimization**

**2.3 Load measurement**

Grandinetti et al. [30] presented an offline mathematical formula to improve task scheduling and average waiting time. Xu et al. [31] presented a task scheduling algorithm which determines crossover and mutation operations for mapping between tasks and resources. Kamalinia and Ghaffari [32] addressed the task scheduling as an NP complete problem. They also used a genetic algorithm to design task scheduling problem to improve makespan and resource efficiency. The presented scheduling algorithm reduces the communication cost among processors by using meta-heuristic methods.

The load measurement is very important and crucial activity in distributed environment. Various load balancing algorithms determine resource load condition before real implementation of task. Various performance metrics like fault tolerance, waiting time, response time, etc. can effectively be optimized by measuring the current load of a resource. Many authors addressed this issue and presented various resource provisioning techniques for effective distribution of incoming load. Patel and Tripathy [33] categorized the resources in three categories: underloaded, normal-loaded, and overloaded to manage the load of available resources. Before assigning a task to a resource, the scheduler checks the current load of each resource and selects the underloaded or normal-loaded resource for task execution. Task length, processing element capacity, and deadline constraints are the factors that are considered to determine the current load of each resource. If a resource becomes overloaded, the unfinished tasks are shifted to another suitable resource for completing their execution. Checkpoint mechanism is used to save and resume the task state which greatly decreases the average response time and task resubmis-

Liu et al. [34] advised that resource provisioning techniques may balance the resource load effectively. They presented peer load balance provision compares the demand and resource capacity by considering requirement of both customer and service provider. The presented mechanism reduced the cost and average response

Rathore and Chana [35] determined a dynamic threshold value based on standard deviation for load balancing and job migration. For job migration, the resources are categorized and the average load of each cluster is compared with processing element's threshold value. For load balancing, tasks are selected randomly

Kaushik and Vidyarthi [36] consider various parameters for effective job scheduling and resource allocation. The presented model selects the best cluster in terms of increased system reliability and reduced energy consumption and balances the system load efficiently. The customer can prioritize their choices to select the suitable cluster for task execution. An effective approach for determining job migration

Load balancing algorithm maps task to various heterogeneous resources based on predefined objectives. The major objective of load balancer is to optimize task completion time, resource cost, and its utilization. Several authors addressed the

Garg and Singh [37] suggested an adaptive workflow scheduling (AWS) by considering resource cost and communication cost between task and resources. Due to heterogeneous nature of resources, final cost is calculated periodically. Arabnejad

and Barbosa [17] presented a task scheduling algorithm which works in two

**68**


In the first stage, the cloud broker arranged the resources in advance without experiencing the customer requirement. In the second phase, the customer requirement and resource cost are comprehended, and the resource overutilization or underutilization is identified. If customer requirement is greater than reserved resources, the broker could request for additional resources on pay-per-use basis. Here, the on demand phase started. In on demand phase, the customer must know the appropriate future requirement which is difficult to estimate in cloud environment.

Singh and Kumar [39] presented a cost optimization method based on process activity. Processing cost and waiting time are determined by using activity time, resource utilization, and variability factor to check the method efficiency. Bittencourt and Madeira [40] presented a cost optimization method for hybrid cloud. The clouds can be categorized in three categories based on resource availability: public cloud, private cloud, and hybrid cloud. A user can use the services of public cloud by using pay-per-use method. Private clouds belong to individuals and offer free variety of services. In hybrid cloud, resources from public cloud are aggregated as per requirement. Bittencourt and Madeira [40] identified the method for appropriate resource.

Cao et al. [41] analyzed that each task is different from each other in cloud environment. They suggested an activity-based task scheduling approach for task reduction. The presented algorithm performs well than traditional task assignment approaches in terms of cost reduction.

Efficient resource provisioning plays a vital role in reducing the cost of task execution. Suresh and Varatharajan [42] presented a particle swarm optimization (PSO)-based resource provisioning algorithm. PSO is adopted to select the appropriate resource for cost optimization. Three performance metrics task execution time, memory usage, and cost are evaluated and compared with other existing methods. The simulation result shows that the presented PSO-based algorithm provides minimum execution time and memory usage with least cost than other state-of-the-art methods.

Salehan et al. [43] suggested auction-based resource allocation to meet the requirement of the customer and service provider. At the time of scheduling, resources are assigned to users that have highest bids. The algorithm provides highest profit and satisfies both the customers and service providers for multiple criteria than other existing methods. Nezarat and Dastghaibyfard [44] map the resource allocation mechanism to economic-based supply and demand problem which provides better functionality with 17% profit with other existing methods.

Netjinda et al. [45] suggested a task scheduling for workflow applications. These workflow applications consist of dependent task with deadline constraints. The aim is to select the least cost cloud resource through PSO for workflow-based task execution. The effective task scheduling decreases the execution time which directly affects the final cost. By considering communication overhead, the model effectiveness and applicability can be increased in real cloud environment.

Chunlin and Layuan [46] presented a resource provisioning method for mobile clients. The mobile devices greatly depend on cloud resources for accessing data and performing operations. The aim is to select the optimal resource at least cost. The service provider executes the tasks on appropriate resources to get the maximum profit.

### **2.5 Fault tolerance**

Fault tolerance is a mechanism that provides the estimated quality results even in the presence of faults. A system with its components and services can consider reliable only if it has fault tolerance capability. Therefore, fault tolerance issue has got a noticeable attention by the research community over the last decades [47].

Fault tolerance techniques can be categorized into two: proactive and reactive. Proactive techniques are prevention techniques that determine the controlled state for fault tolerance before they occur. The systems are continuously monitored for fault estimation. Proactive fault tolerance can be implemented in three ways: self-healing, preemption migration, and system rejuvenation. In self-healing, fault recovery procedures are periodically applied for autonomous recovery. In preemptive migration, the tasks are shifted from fault probable resource to another resource. System rejuvenation is the mechanism in which periodic backups are taken for cleaning and removing errors from the system.

Another category is the reactive approaches that deal with faults after their occurrence. Reactive fault tolerance can also be implemented in three ways: job replication, job migration, and checkpoint. In job replication, several instances or copies of the same task make available on different resources. If one instance fails, task is executed on another instance. In job migration, tasks are migrated to another suitable resource for completing its execution. In checkpoint, task states are periodically saved and restarted from the last saved state instead of from the very beginning [47]. Several authors suggested fault tolerance mechanism and recovery solutions to resolve the issue.

Patel et al. [48] addressed resource failure issues and presented a checkpoint based recovery mechanism for task execution. If task does not complete its execution within deadline, then another suitable resource is selected for completing its execution. Before transferring it to another suitable resource, task state is saved and resumed for further execution through checkpoint. This results in reduced execution time, response time, and improved throughput than other existing methods.

Generally checkpoint increases the execution time that directly affects the execution cost. Egwutuoha et al. [49] use the process of redundant technique to reduce the task execution time. The presented technique is pretty good and reduces up to 40% checkpoint overhead. Choi et al. [50] identify the malicious users to provide fault tolerance scheduling in cloud environment. Any user which only use cloud services and reject other requests is treated as malicious user. The reputation is calculated to determine the malicious users. The work can be implemented to improve network reliability and task execution time in cloud paradigm.

Mei et al. [51] suggested that replication-based fault tolerance approaches waste lots of resources and also compromise with makespan. To resolve the issue, Mei et al. [51] presented fault tolerance scheduling mechanism that ensures successful completion of task execution. The limitation of replication is avoided by rescheduling the task for further execution. If scheduler identifies the failure, it reassigns task to another suitable resource and saves the wastage of resources. This mechanism reduces resource consumption and task execution time. However, costs are presumed for implementing scheduling, which limits the model applicability in real scenario.

Nazir et al. [52] use fault index for maintaining the history of resources. Fault index is determined based on successful and unsuccessful task completion on particular resource. Based on fault index value, grid broker replicates the task that

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**Table 1.**

*Classification of grid.*

*Analysis of Effective Load Balancing Techniques in Distributed Environment*

can be used when fault occurs. Budget and time constraints are also considered at the time of task scheduling. The presented mechanism satisfies various QoS requirement, increases the reliability, and performs consistent in the existence of fault also. Qureshi et al. [53] combined two fault tolerance techniques to inherit the favorable aspects. They perform hybridization of alternate task with retry and checkpoint mechanism and evaluate various performance metrics. The simulation result shows that alternate task with checkpoint mechanism performs better and improves

Cloud facilitates the storage and access heterogeneous data in a distributed remote network. Due to dynamicity, network congestion and system faults are key factors for fault occurrence. Preventing the network from congestion and selection of suitable servers can avoid the fault conditions. Tamilvizhi and Parvathavarthini [54] suggested the concept of square matrix multiplication to manage the network traffic and avoid network congestion. The resource monitor predicts the fault conditions and uses migration policies to avoid system failure. The presented fault

tolerance mechanism provides reduced cost with less energy consumption.

Garg and Singh [55] observed various fault conditions and suggested a fault tolerance-based task scheduling algorithm in grid environment. A genetic algorithm is used to determine the resource capacity for task scheduling. The presented approach increased system reliability and reduced task execution time in grid environment.

Interoperability refers efficient migration and integration of heterogeneous applications and data to get the seamless services across domains. Various distributed applications exist to provide millions of services that differ in the services they offered:

• Distributed computing is a collection of various heterogeneous components that are located at remote locations, which coordinate with each other by message passing. Each component or processor has its own memory. It is a kind of parallel computing in which a task is split into subtasks to run on multiple

• Grid computing is a network of computer resources that are connected to solve a complex problem. Each resource is loosely linked and runs independent task to achieve a common goal. Grid computing may be classified on the basis of scale and functionality. On the basis of scale, grid computing may be classified into two categories (**Table 1**), i.e., cluster grid and enterprise grid. Cluster means a group of similar kind of entities. So cluster grid provides services to

**Types of grid Characteristics**

1 Scale Cluster Computational services are limited to a group or a

2 Functionality Global Comprises of collection of cluster grid

department Enterprise Provides services within an enterprise

Computational Acts as an integrated processing resource Data Coordinate and manage database information

which is located at remote locations

*DOI: http://dx.doi.org/10.5772/intechopen.91460*

system throughput than other existing methods.

**2.6 Interoperability issue**

components simultaneously.

**S. no Classification criteria**
