Ph.D Projects in Cloud Computing

Cloud computing comes into focus when there is a need for a way in IT to increase capacity or add capabilities on the fly without investing in new infrastructure, training new personnel, or licensing new software. Cloud computing encompasses any subscription-based or pay-per-use service that, in real time over the Internet, extends IT’s existing capabilities. Cloud computing is an expression used to describe a variety of computing concepts that involve a large number of computers connected through a real-time communication network such as the Internet.

Cloud computing offers numerous advantages both to end users and businesses of all sizes. Business are now able to focus on their core business by outsourcing all the hassle of IT infrastructure. Its user base grows constantly and more big players are attracted to it, offering better and more fine tuned services and solutions. Cloud computing remains strong and has great potential for the future.

In view of this great potential of cloud computing for the future S-LOGIX has included this domain in its project works.

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

Related Area

  • Data-Intensive Technologies for Cloud Computing
  • Resource Scheduling and Allocation in Cloud Computing
  • Task Scheduling and Load Balancing in Cloud Computing
  • Mobile Cloud Computing
  • Storage and Fault Tolerance Strategies Used in Cloud Computing
  • Adaptive and Data-Driven Workload Manager for Generic Clouds
  • Integration of High-Performance Computing into Cloud Computing Services
  • Peer-to-Peer Based Cloud Workflow System
  • Service Scalability Over the Cloud
  • Scientific Services on the Cloud
  • Scientific Data Management in the Cloud
  • Cloud Management Mechanisms
  • Cloud Delivery Model Considerations
  • Advanced Cloud Architectures
  • Cloud-Based High-Performance Computing Clusters
  • Cloud Computing Security
  • Privacy Preservation in Cloud Computing
  • Service Quality Metrics and SLAs