Topics Related to Cloud Computing

Topics Related to Cloud Computing that are continuously emerging, which are more ideal for creating projects are listed in this page. We provide you professionals assistance in guiding throughout your research work. Relevant to cloud computing, we recommend some algorithm-based topics that emphasize their use in realistic applications and exploration:

1. Resource Allocation Algorithms

• Dynamic Resource Allocation: On the basis of actual-time requirements and workloads, allocate cloud resources in a dynamic manner by utilizing algorithms.
• Energy-Aware Resource Allocation: To reduce the usage of energy in addition to preserving functionality, consider suitable methods which allocate resources.
• Fair Resource Allocation: Among several applications or users, assure objective distribution of resources by exploring ideal algorithms.

2. Load Balancing Algorithms

• Round Robin Load Balancing: To distribute requests among accessible servers in a uniform manner, we plan to use this basic algorithm.
• Weighted Load Balancing: In terms of capabilities, this technique allocates weights to servers. In a proper way, it also balances the load.
• Dynamic Load Balancing: By altering the load distribution and reassigning resources in a dynamic manner, it adjusts to varying workloads.

3. Scheduling Algorithms

• First-Come, First-Served (FCFS): It is a basic scheduling algorithm which considers the arrival order of missions to process them.
• Priority Scheduling: In this algorithm, priorities are allocated to missions. Before the lower-priority missions, it schedules the higher-priority ones.
• Min-Min and Max-Min Scheduling: To enhance resource usage, the biggest missions are initially scheduled by Max-Min, whereas Min-Min chooses the compact missions initially.

4. Fault Tolerance Algorithms

• Checkpointing and Rollback: The condition of a process is stored by this algorithm in a periodic manner. At the time of failure, the process can be resumed from that phase.
• Replication and Redundancy: During failures, it assures availability by duplicating operations and data.
• Failure Detection and Recovery: To preserve system credibility, we aim to employ algorithms which begin recovery operations after identifying failures.

5. Security Algorithms

• Encryption Algorithms: For protecting data in both active and inactive states, focus on methods like ECC, RSA, and AES.
• Access Control Algorithms: Specifically for handling and implementing access strategies, consider techniques such as role-based access control (RBAC).
• Intrusion Detection Algorithms: In cloud platforms, identify intrusions and harmful actions by means of machine learning-related algorithms.

6. Optimization Algorithms

• Genetic Algorithms: For enhancing resource allocation, load balancing, and scheduling, these evolutionary algorithms are more suitable.
• Ant Colony Optimization: In cloud platforms, intricate optimization issues can be resolved by this algorithm, which is specifically influenced by the activity of ants.
• Simulated Annealing: It is more helpful in resource optimization. For approximating the global optimum of a specific function, this probabilistic method can be employed.

7. Machine Learning Algorithms for Cloud Management

• Predictive Analytics for Resource Demand: To enhance allocation and forecast upcoming resource requirements, we intend to utilize machine learning.
• Anomaly Detection: In order to improve security and functionality, identify abnormalities in cloud utilization patterns by employing machine learning algorithms.
• Auto-scaling Algorithms: Use algorithms which consider utilization patterns and predictions to scale cloud resources automatically with machine learning.

8. Data Management Algorithms

• Deduplication Algorithms: To preserve storage space, the repeating data’s duplicate copies have to be removed by utilizing these methods.
• Compression Algorithms: As a means to enhance transmission and storage effectiveness, minimize the size of data through these techniques.
• Data Partitioning Algorithms: For effective processing and storage, extensive datasets must be partitioned into compact, convenient parts using these methods.

9. Network Optimization Algorithms

• Software-Defined Networking (SDN) Algorithms: In cloud platforms, handle and improve network traffic flow by means of these algorithms.
• Traffic Engineering Algorithms: For enhancing the network traffic flows’ credibility and functionality, we focus on employing these methods.
• QoS Routing Algorithms: Consider algorithms which prioritize traffic and handle network resources to assure Quality of Service (QoS).

10. Service Level Agreement (SLA) Management Algorithms

• SLA Violation Detection: In cloud services, the SLA breaches can be tracked and identified by these algorithms.
• SLA-based Resource Provisioning: To assure compliance, use methods that consider SLA needs to provide resources.
• Penalty Minimization Algorithms: Penalties that are caused as a result of SLA breaches can be reduced by these policies.

11. Container Scheduling Algorithms

• Bin Packing Algorithms: To improve resource utilization, the containers can be effectively deployed on hosts by utilizing these algorithms.
• QoS-Aware Container Scheduling: Use algorithms which schedule containers by focusing on Quality of Service needs.
• Latency-Aware Scheduling: Through scheduling containers nearer to users or data sources, the latency can be reduced using these methods.

12. Migration Algorithms

• Live Migration Algorithms: With reduced downtime, the active VMs or containers can be transferred from one host to another host by means of these methods.
• Cost-Effective Migration: Consider transferring data or VMs among various cloud providers, and refine the cost related to this process through employing these algorithms.
• Latency-Sensitive Migration: In addition to preserving service quality and reducing latency, we aim to transfer resources with the aid of these techniques.

13. Blockchain in Cloud Computing

• Consensus Algorithms: For assuring consensus in blockchain-related cloud frameworks, focus on various algorithms such as Byzantine Fault Tolerance (BFT), Proof of Stake (PoS), and Proof of Work (PoW).
• Smart Contract Algorithms: On blockchain environments that are combined with cloud services, the smart contracts can be developed and implemented using these methods.
• Data Integrity Algorithms: To assure data morality and source, concentrate on these algorithms. In cloud storage, consider the utilization of blockchain mechanisms.

How to use CloudSim?

How to Use CloudSim for Cloud Computing Simulations?

In recent years, scholars and developers widely use CloudSim to simulate and examine cloud computing platforms. It is generally referred to as a Java-related framework. As a means to configure and utilize CloudSim, we offer a procedural instruction explicitly:

Step 1: Install Java Development Kit (JDK)

It is important to install the Java Development Kit (JDK), because CloudSim is specifically written in Java.

1. From Oracle JDK, we should download and install JDK. Alternatively, the OpenJDK can be utilized.
2. By executing the following code, check the installation:

java -version

javac -version

Step 2: Install an Integrated Development Environment (IDE)  

Any Java IDE can be utilized. Some of the highly general IDEs are:

• NetBeans
• IntelliJ IDEA
• Eclipse

Step 3: Download CloudSim

1. Acquire CloudSim from GitHub or Official Site

• From the official site or CloudSim GitHub repository, the current CloudSim version has to be downloaded.
• Then, the downloaded ZIP file must be extracted.

1. Import CloudSim into Our IDE

• IntelliJ IDEA or Eclipse should be opened.
• A novel Java Project has to be developed.
• Within the project’s lib folder, we have to copy and paste the CloudSim libraries (JAR files).
• To the classpath, the essential JARS have to be appended.

Step 4: Write the First CloudSim Program

In order to simulate a simple cloud data center, we provide a basic CloudSim instance.

Sample Code: CloudSim Simple Simulation

import org.cloudbus.cloudsim.*;

import org.cloudbus.cloudsim.core.CloudSim;

import java.util.*;

public class CloudSimExample {

public static void main(String[] args) {

try {

// Step 1: Initialize the CloudSim library

int numUsers = 1;

Calendar calendar = Calendar.getInstance();

boolean traceFlag = false;

CloudSim.init(numUsers, calendar, traceFlag);

// Step 2: Create a Datacenter

Datacenter datacenter = createDatacenter(“Datacenter_1”);

// Step 3: Create a Broker

DatacenterBroker broker = new DatacenterBroker(“Broker”);

// Step 4: Create a list of VMs

List<Vm> vmlist = new ArrayList<>();

int vmId = 0;

int mips = 1000;

int ram = 512;

long bw = 1000;

long size = 10000;

int pesNumber = 1;

String vmm = “Xen”;

Vm vm = new Vm(vmId, broker.getId(), mips, pesNumber, ram, bw, size, vmm, new CloudletSchedulerTimeShared());

vmlist.add(vm);

broker.submitVmList(vmlist);

// Step 5: Create a list of cloudlets (tasks)

List<Cloudlet> cloudletList = new ArrayList<>();

int cloudletId = 0;

long length = 40000;

long fileSize = 300;

long outputSize = 300;

int pesNumberCloudlet = 1;

Cloudlet cloudlet = new Cloudlet(cloudletId, length, pesNumberCloudlet, fileSize, outputSize,

new UtilizationModelFull(), new UtilizationModelFull(), new UtilizationModelFull());

cloudlet.setUserId(broker.getId());

cloudletList.add(cloudlet);

broker.submitCloudletList(cloudletList);

// Step 6: Start the CloudSim simulation

CloudSim.startSimulation();

// Step 7: Get results

List<Cloudlet> newList = broker.getCloudletReceivedList();

CloudSim.stopSimulation();

// Print results

printCloudletResults(newList);

} catch (Exception e) {

e.printStackTrace();

}

}

private static Datacenter createDatacenter(String name) {

List<Pe> peList = new ArrayList<>();

int mips = 1000;

peList.add(new Pe(0, new PeProvisionerSimple(mips)));

List<Host> hostList = new ArrayList<>();

int hostId = 0;

int ram = 2048;

long storage = 1000000;

int bw = 10000;

hostList.add(new Host(hostId, new RamProvisionerSimple(ram), new BwProvisionerSimple(bw), storage, peList,

new VmSchedulerTimeShared(peList)));

String arch = “x86”;

String os = “Linux”;

String vmm = “Xen”;

double timeZone = 10.0;

double costPerSec = 3.0;

LinkedList<Storage> storageList = new LinkedList<>();

return new Datacenter(name, new DatacenterCharacteristics(arch, os, vmm, hostList, timeZone, costPerSec,

0.05, 0.001, 0.0), new VmAllocationPolicySimple(hostList), storageList, 0);

}

private static void printCloudletResults(List<Cloudlet> list) {

System.out.println(“Cloudlet ID\tStatus\tData center ID\tVM ID\tTime\tStart Time\tFinish Time”);

for (Cloudlet cloudlet : list) {

System.out.print(cloudlet.getCloudletId() + “\t”);

System.out.print(cloudlet.getStatusString() + “\t”);

System.out.print(cloudlet.getResourceId() + “\t”);

System.out.print(cloudlet.getVmId() + “\t”);

System.out.print(cloudlet.getActualCPUTime() + “\t”);

System.out.print(cloudlet.getExecStartTime() + “\t”);

System.out.println(cloudlet.getFinishTime());

}

}

}

Step 5: Compile and Execute the Code

• As CloudSimExample.java, the file has to be saved.
• Then, the program must be compiled and executed.
• A simulation result table should be analyzed. It could encompass the aspects of cloudlet execution.

Step 6: Adapt the CloudSim Simulation

For the following requirements, the code can be altered:

• Datacenter resources have to be modified (storage, bandwidth, CPU, and RAM).
• Focus on adapting VM setups (provisioning, scheduling).
• Various workload models should be simulated (bursty, random).
• Diverse scheduling algorithms must be applied.

Different CloudSim Types

It is approachable to deal with the subsequent options, especially in the case of requiring particular simulations:

• iFogSim – It is more suitable for Edge Computing and Fog Computing simulations.
• EdgeCloudSim – This tool is highly useful for IoT applications and Edge Computing.
• CloudSim Plus – It offers enhanced functionality, which is considered as an advanced version of CloudSim.

On the basis of the cloud computing domain, we suggested numerous algorithm topics that are compelling as well as significant. For supporting you to configure and employ CloudSim, a detailed instruction is provided by us.

Dissertation Related to Cloud Computing

Dissertation Related to Cloud Computing where we provide comprehensive support and a range of innovative thesis ideas tailored for scholars are shared below. Let us streamline your research process and provide the assistance you need to succeed.

1. DeepDefend: A comprehensive framework for DDoS attack detection and prevention in cloud computing
2. A comprehensive review and conceptual framework for cloud computing adoption in bioinformatics
3. Advancements in intelligent cloud computing for power optimization and battery management in hybrid renewable energy systems: A comprehensive review
4. Integration of IoT and edge cloud computing for smart microgrid energy management in VANET using machine learning
5. Quantum walks-based classification model with resistance for cloud computing attacks
6. AI-HybridChain: Picturized Authentication and DRL based Access Control Method with Secure Two Fold Revocation for Ensuring Cloud Computing Security
7. QoS-SLA-aware adaptive genetic algorithm for multi-request offloading in integrated edge-cloud computing in Internet of vehicles
8. A chameleon and remora search optimization algorithm for handling task scheduling uncertainty problem in cloud computing
9. An examination of cloud computing adoption decisions: Rational choice or cognitive bias?
10. Auto-scaling techniques in container-based cloud and edge/fog computing: Taxonomy and survey
11. Application of building BIM technology based on sensor systems and 5G cloud computing
12. Net and configurational effects of determinants on cloud computing adoption by SMEs under cloud promotion policy using PLS-SEM and fsQCA
13. Determinants of cloud computing integration and its impact on sustainable performance in SMEs: An empirical investigation using the SEM-ANN approach
14. Fast multi-type resource allocation in local-edge-cloud computing for energy-efficient service provision
15. Security computing resource allocation based on deep reinforcement learning in serverless multi-cloud edge computing
16. Cloud Computing in Terms of Sustainable Development – Evaluation and Mutual Relations
17. Effective load balancing approach in cloud computing using Inspired Lion Optimization Algorithm
18. Cloud Brain-MRS: An intelligent cloud computing platform for in vivo magnetic resonance spectroscopy preprocessing, quantification, and analysis
19. Dynamic network-aware container allocation in Cloud/Fog computing with mobile nodes
20. Investigation on Innovative Models of Computer Laboratories Based on Cloud Computing