The Best UK Dissertation Computer Science Research Topics on load balancing, task and resource scheduling in a cloud environment

A critical review on load balancing, task and resource scheduling in a cloud environment: The Best UK Dissertation Research Topics from Existing Recent Research Gaps in Computer Science Engineering Domain

In brief:

• You will find the best dissertation research areas / topics for future researchers enrolled in Computer Science Engineering. These topics are researched in-depth at the University of Glasgow, UK, Sun Yat-sen University, University of St Andrews and many more.
• In order to identify the future research topics, we have reviewed the computer science literature (recent peer-reviewed studies) on load balancing, task and resource scheduling method for efficient cloud service provisioning.
• There is a possibility to minimize the total cost of computation by a new scheduling method on the basis of bio-inspired heuristics algorithms.
• Single objective- and multi-objective-based task scheduling method for making multidimensional task and resource scheduling model
• Tested the suggested system performance using CloudSim simulator toolkit. CloudSim Simulator has been widely recommended by the researchers, professors and universities from the Department of Computer Science such as the University of New South Wales, Sydney, Australia, The University of Melbourne, Australia.
• Download our CloudSim Simulator Reference book papers such as tutorials, proprietary materials, research projects and many more @ tutorsindia.com/academy/books

For more details of cloud computing refer: Disssertation Tips for Developing Secure Cloud Model

1. Resource scheduling algorithm with load balancing for cloud service provisioning 

Research by Priya et al. (2018) presented a load balancing (LB) and resource scheduling (RS) approach for efficient cloud service provisioning. Specifically, they have effectively scheduled the resources using Fuzzy-based Multidimensional Resource Scheduling model. Moreover, by utilizing a Multidimensional Queuing Load Optimization algorithm, they attained fair load balancing and increased the utilization of the virtual machine (VM) through selecting a request from a class in a dynamic manner. The suggested load balancing system is developed toward improving latency time for each class of request and avoiding overutilization and underutilization of cloud resources. The simulated results (tested using CloudSim simulator toolkit) shown that attained better performance with respect to high resource scheduling efficiency (7%), average success rate (9%) and reduced the response time (35.5 %). But the primary criteria called, scheduling of task not addressed. Also, need to consider LB and RS of intermediate data toward enhancing the system performance by considering privacy-preserving as a metric together with other metrics like average response time and success ration. Hire Tutors India experts to develop your algorithm and coding for your dissertation in Computer Science.

2. OPFT:A New Online Scheduling Approach for Enhancing QoS in Cloud

A research by Nasr et al. (2018) suggested an Online Potential Finish Time (OPFT) task scheduling method for enhancing the scheduling performance and resolve the QoS issues. They have allocated a group of a task through specific rules, whereas the allocated task to a specific VM, which is proportional to the power processing of this VM. The performance of OPFT method is assessed via CloudSim, and the simulated results indicate that this method better efficient when related with the Round-Robin, Minimum Completion Time, First Come First Serve and Min-Min with respect to schedule cost, response time, schedule length, balancing degree and resource utilization. However, the scheduling performance is poor; hence, there is a need to improving the scheduling efficiency, which gives low time complexity and low schedule length. Also, the integrity of the virtual machine and overhead, multidimensional resource scheduling was not addressed. Hire Tutors India experts to develop your algorithm and coding implementation on improving the scheduling efficiency for your Computer Science dissertation Services.

3. Meta heuristic-based task deployment mechanism for load balancing in IaaS cloud

A study by Adhikari et al. (2019) suggested a load balancing resource clustering method approach toward attaining optimal resource clustering and their cluster centers for faster convergence via meta-heuristic Bat-algorithm. Also, suggested dynamic task assignment policy to attain less execution cost, makespan within the given constraints. The suggested method has been evaluated (CloudSim simulator toolkit) by considering synthetic datasets, as a resulted shown superiority than the traditional approach. But, the integrity of VM and overhead remain unaddressed. Also, needs to validate its performance for the multidimensional resource scheduling process. Then, will scrutinize the trade-off amongst different nature-inspired algorithms with task deployment policies for various types of service level agreements (SLA) with their penalties and load balancing policies. Hire Tutors India Computer Science experts to develop your algorithm and coding implementation Services specifically on the integrity of VM and overhead related computer Science dissertation.

4. An efficient symbiotic organisms search algorithm with chaotic optimization strategy for multi-objective task scheduling problems in a cloud computing environment

A work by Adhikari et al. (2019) proposed a chaotic symbiotic organisms search (CMSOS) method for resolving multi-objective large scale task scheduling optimization issues on the cloud computing environment. They have generated the initial ecosystem population by employing chaotic optimization method. Then, confirmed the diversity among organisms for global convergence by random sequence-based components of SOS, which is replacing with a chaotic sequence. The suggested model performance has been evaluated on CloudSim simulator toolkit using synthesized workloads, and standard workload traces for huge instances, i.e. up to 5000. The simulated results of CMSOS shown better optimal trade-offs amongst financial cost (cost) and execution time (makespan) with no computational overhead. However, resource management, the integrity of VM remains unaddressed. Also, needs to handle QoS requirements like security and reliability for very large workload instances. Hire Tutors India Computer Science experts to develop your algorithm and coding specifically on resource management, the integrity of VM related computer Science dissertation.

5. Cost-optimized hybrid genetic-gravitational search algorithm for load scheduling in cloud computing

Research by Chaudhary and Kumar (2019) presented a Hybrid Genetic-Gravitational Search Algorithm (HG-GSA) based load scheduling technique for minimizing the computation cost. The computational cost measured by considering transfer and execution cost. Also, they calculated a force by the best position of particles and tested the performance using CloudSim simulator. The simulated results have shown that they minimized the computation cost, which significantly improved the system performance than the traditional method, namely CloudyGSA, particle swarm and Linear Improved Gravitational Search Algorithm in Cloud (LIGSA-C) method. But the suggested method is not suitable for multidimensional resource scheduling strategies. The task scheduling and resource management remain unaddressed. Also, there is a possibility to minimize the total cost of computation by a new scheduling method on the basis of bio-inspired heuristics. Hire Tutors India Computer Science researchers to develop your algorithm and coding specifically on resource management, the integrity of VM related computer Science dissertation.

6. Task scheduling techniques in cloud computing: A literature survey

A work by Arunarani et al. (2019) reviewed the available task scheduling methods and related metrics which is appropriate for a cloud platform. They have conducted a review on the basis of three perspectives such as parameter-based measures, methods and application were utilized. At the end of research, they have recommended enhancing the effectiveness of scheduling by combining virtual machine consolidation and task scheduling strategies. Also, will consider the different input parameters, namely deadlines, running costs. Then, there should be a need for a single objective- and multi-objective-based task scheduling for enhancing the system performance by a hybrid combination of earlier algorithms. But the major concern like the integrity of virtual machine and overhead, load balancing, resource management remains unaddressed. However, coming up with a unique topic for students pursuing their postgraduate at the UK universities would be challenging and cumbersome. Tutors India fills this gap where you can hire Tutors India Computer Science experts who have capability and expertise to develop a wide range of dissertation research topics and coding and algorithm development based on a hybrid combination of earlier algorithms.

Table 1: Summary of the Studies  (literature) for your dissertation research topic selection  

From these points of in-depth literature on Cloud Computing, I have concluded that some of the researchers consider the load balancing strategies and some of them considered the task scheduling process over cloud computing.

By considering a load balancing strategies, a work by Priya et al.(2018) suggested LB and RS approach for efficient cloud service provisioning. But the primary criteria called, scheduling of task not addressed.

• Similarly, a study by Adhikari et al. (2019) suggested a load balancing resource clustering method approach toward attaining optimal resource clustering and their cluster centres for faster convergence via meta-heuristic Bat-algorithm. But, the integrity of VM and overhead remain unaddressed.
• Research by Chaudhary and Kumar (2019) presented an HG-GSA based load scheduling technique for minimizing the computation cost. But the suggested method is not suitable for multidimensional resource scheduling strategies. Also, task scheduling and resource management remain unaddressed.
• For scheduling, the task of research by Nasr et al. (2018) suggested an OPFT task scheduling method for enhancing the scheduling performance and resolve the QoS issues. They have effectively scheduled the task, but there isa need for an effective way of minimizing the time complexity and schedule length.
• Similarly, research by Adhikari et al. (2019) proposed a CMSOS method for resolving multi-objective large scale task scheduling optimization issues on the cloud computing environment. However, resource management, the integrity of VM and overhead remains unaddressed. A work by Arunarani et al. (2019) reviewed the available task scheduling methods and related metrics which is appropriate for the cloud platform.
• At the end of the research, they have recommended enhancing the effectiveness of scheduling by combining virtual machine consolidation and task scheduling strategies. But the primary concern like the integrity of virtual machine and overhead, load balancing, resource management remains unaddressed.

Computer Science Engineering Dissertation Recommendations based on Critical Review of Literature.

Tutors India Computer Science Research department experts recommend dissertation topics based on the gaps obtained from a detailed critical review of the literature.

To summarize, there is a possibility to enhance the cloud system performance by considering the below criteria:

• To scrutinize the trade-off amongst different nature-inspired algorithms with task deployment policies for various types of service level agreements (SLA) with their penalties and load balancing policies.
• To minimize the total cost of computation by a new scheduling method on the basis of bio-inspired heuristics. Also will enhance the system performance by a hybrid combination of single objective- and multi-objective-based task scheduling method
• To enhancing the cloud system performance for the multidimensional dataset by considering integrity of virtual machine, overhead and multidimensional resource scheduling strategies
• To handle various QoS requirements like security and reliability for very large workload instances.

References

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