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

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

to share resources within the enterprise.

improve various QoS metrics.

handle applications [56, 57].

ment of both the technologies.

like big data and analytics.

to meet with the users demand [1, 2].

distributed but decentralized system.

the group or departmental level. Another type is enterprise grid that provides

• Cloud computing provides on demand computer resources such as storage or computational resources without direct involvement of users. It has effective data management and computing framework for executing task in parallel to

• Fog computing is the extension of cloud computing which consists of multiple fog nodes that are directly connected to the physical devices. The difference between both technologies is that cloud is a centralized system while fog is

• CloudIoT is an innovative trend which connects and manages millions of devices in very cost-effective manners that are dispersed globally. Cloud can profit by IoT to deal with real-world things by sharing the pool of highly computational resources rather than having local servers or personal devices to

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

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 require-

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

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

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

**72**

**2.7 QoS issues**

model.

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, cost, and customer satisfaction.

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


**Table 2.**

*Existing load balancing techniques and future scope.*
