**2.6 Interoperability issue**

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

taken for cleaning and removing errors from the system.

**2.5 Fault tolerance**

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.

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

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 sug-

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

gested fault tolerance mechanism and recovery solutions to resolve the issue.

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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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:



**Table 1.** *Classification of grid.* the group or departmental level. Another type is enterprise grid that provides to share resources within the enterprise.


Various authors analyzed the interoperability issues that are briefly presented with their respective solutions. Aazam et al. [58] focused on analyzed two complementary technologies: cloud computing and IoT. Various challenges and integration issues of CloudIoT framework are discussed. Data analysis, service provisioning, and storage are the future dimensions to improve the performance of CloudIoT model.

Botta et al. [59] also analyzed the integration issues of cloud and IoT. Both the technologies are analyzed separately based on applications, technology, issues, and challenges. The details of existing platforms and projects are presented that are currently implementing CloudIoT. Standardization, address resolution, multinetworking, and developments of APIs are some future directions to provide full potential to CloudIoT framework. Khodkari et al. [60] present the significance and requirement of CloudIoT paradigm. They presented complementary aspects of cloud computing and IoT and assure the QoS by evaluating the integrity requirement of both the technologies.

Bonomi et al. [61] analyzed characteristics, services, and applications of fog computing. They determined the importance of collaboration of fog and cloud and address that some applications need both cloud globalization and fog localization like big data and analytics.

The linked open data (LOD) provides a new dimension for various heterogeneous interoperability issues based on Web server architectures. These issues require attention to support heterogeneous description principles that is necessary to deal with different data from web resources. The LOD interoperability follows bottom up approach to establish the strong relationships among datasets. Various researchers addressed LOD interoperability issues and presents respective solutions to meet with the users demand [1, 2].

#### **2.7 QoS issues**

The user submits the tasks with various QoS constraints (cost execution time, energy consumption, delay, etc.) to improve the performance in distributed environment. Researchers addressed several QoS issues and provide the solutions for meeting the objective. Aron and Chana [62] observed various QoS issues and

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**Table 2.**

*Analysis of Effective Load Balancing Techniques in Distributed Environment*

identified four issues, i.e., cost, reliability, security, and time, for resource provisioning in grid environment. Service-level agreement (SLA) reduced the complexity of resource provisioning by maintaining up-to-date information of all the resources. The presented approach performs better in terms of resource utilization,

Popularity of cloud computing increased burden on distributed data centers. These data centers consumes excessive amount of energy to provide services and fulfill consumer satisfaction. Horri et al. [63] identified overloaded and underloaded servers and shift load from overloaded to under loaded resources. This makes a trade-off between energy consumption and SLA. Hoseiny Farahabady et al. [64] suggested an objective function to reduce cost and performance improvement for resource allocation mechanism. Two test cases are considered: tasks with known running time and tasks with unknown running time. They listed Monte Carlo method to determine the

**Author Technique Strength Limitations &** 

**Future Scope**

optimization

constraint

Cost Time constraint Grid

Execution time Fault tolerance Grid

Execution time Cost optimization Grid

metrics consideration

Task deadline consideration

Fault tolerance Grid

Fault tolerance Grid

Other optimization

Fault tolerance mechanism and associated overhead

Cost Performance

Cost Simulation on real cloud

Resource allocation Cost Budget and time

Makespan, finished rate, resubmitted time

cost

Queueing model Response time,

Execution time,

drop rate, server utilization

Response time, drop rate, server utilization

Execution time, response time, throughput, resubmitted time **Technology**

Grid

Grid

Grid

Grid

Grid

Grid

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

cost, and customer satisfaction.

task's unknown values (**Table 2**).

Shah et al. [22] Cost based resource

Chang et al. [16] Execution time

Hao et al. [15] Resource selection

Murugesan & Chellappan [21]

Singhal et al. [24]

Singh and Kumar [39]

Arabnejad and Barbosa [17]

Garg and Singh

[55]

Singh and Kumar [10]

Singh and Kumar [23] allocation

Parallel executionbased resource allocation

Cost based resource allocation

prediction

mechanism

Resource selection mechanism

Task scheduling and resource selection mechanism

Resource selection

and task scheduling

model

Patel et al. [48] Resource selection

*Existing load balancing techniques and future scope.*
