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Trending PhD Project Topics in Cloud Computing

In the Cloud environment, the dynamic task scheduling approach schedules the incoming tasks to the resources based on their current load to execute the task efficiently. An effective dynamic task scheduling approach aims to reduce the completion time, energy consumption and improves the utilization rate of the Cloud resources. In addition, it maintains the load in the Cloud resources effectually for minimizing the migration rate and performance degradation and improve the Quality of Service (QoS). Nowadays, the demand of the Cloud resources increases abruptly; however Cloud data center contains reliable resources, the Cloud providers struggle to manage the requirements of the users. To solve this constraint, the dynamic resource allocation technique is used in Cloud computing. It allocates the available resources to the users based on their requirements. An effective resource allocation minimizes resource wastage, cost, and SLA violations.

Implementation details of CloudSim

    CloudSim, the simulation framework can be extended to experiment the innovations in both system and behavior modeling of cloud system components and environment.

Configuration of Cloud environment using CloudSim

Basically large scale cloud environment can be modeled and simulated by configuring the following components with the parameters

  • DataCenter:DatacenterCharacteristics (arch, os, vmm, hostList, time_zone, cost, costPerMem, costPerStorage, costPerBw)
  • Host:RAM, bandwidth, storage, number of processor elements, MIPS, and VM Scheduler
  • VirtualMachine:RAM, bandwidth, storage, number of processor elements, MIPS, Virtual Machine Monitor, and Cloudlet Scheduler
  • Cloudlet:length, input file size of cloudlet, output file size of cloudlet, RAM, bandwidth and CPU
  • DatacenterBroker: VM creation, cloudlet submission to VM, and VM destruction

Policies for allocating VM to hosts using CloudSim

    VmScheduler such as VmSchedulerSpaceShared, and VmSchedulerTimeShared can be extended to simulate user defined policies for allocating VM to hosts.

Policies for allocating cloudlets to VM using CloudSim

    CloudletScheduler such as CloudletSchedulerSpaceShared, and CloudletSchedulerTimeShared can be extended to simulate user defined policies for allocating cloudlets to VM.

Network topology using CloudSim

    Solutions concerning with Network topology can be simulated using the class NetworkTopology and its methods such as buildNetworkTopology, mapNode.

Resource utilization based VM migration and Task migration in CloudSim

    Resource utilization is computed on the basis of CPU MIPS, storage and bandwidth using getAvailable methods. Based on the utilization, VM migration and task migration can be accomplished by extending the DataCenter class in which processCloudletSubmit() method is overridden with “VM_MIGRATE” tag and “CLOUDLET_MOVE” tag respectively.

Resource allocation techniques in CloudSim

    Resource allocation is carried out in number of ways such as auction, game theory. It can be carried out by creating Auctioneer class and extending DataCenterBroker and DataCenter class.

Attacks in cloud computing environment using CloudSim

    Attacks in cloud computing environment such as Denial Of Service (DoS) attack can be modeled in cloudsim by submitting large number of cloudlets by the malicious user.

Optimization algorithms for resource scheduling in CloudSim

    Optimization algorithms such as ACO algorithm, Genetic algorithm, and PSO algorithm can be applied for scheduling the task in VMs in large scale experiments.

Deduplication solutions using CloudSim

    Simulating storage system is required for deduplication techniques. HarddriveStorage class can be extended to simulate the solutions regarding Deduplication techniques.

Performance Evaluation of Cloud Computing environment using CloudSim

Impact can be observed for the various

  • File size
  • Number of files
  • Cloudlet length
  • Number of cloudlets
  • Number of VM migrations
  • Number of Task migrations
  • Resource characteristics
  • Number of requests
  • Number of VM instances
  • Number of rounds in auction

Performance Metrics

  • Execution time
  • Response time
  • Makespan
  • Network latency
  • Average CPU utilization
  • Average power consumption
  • Average energy consumption
  • Resource utilization
  • Load balancing factor

Economic Metrics

  • Profit percentage
  • Total Cost

Security Metrics

  • Data confidentiality (%)
  • Sensitivity
  • computation cost
  • Storage overhead

General Metrics

  • DeDuplication Ratio
  • Truthfulness in Auction