PhD Guidance in Distributed Computing

PhD Guidance in Distributed Computing

    PhD Guidance in Distributed Computing offers you most essential and sophisticated information about distributed computing. .  Distributed computing is also a research field to study distributed systems. A network system’s software components can communicate and share among messages to improve communication performance and efficiency.

The major challenge while communicating large scale systems with thousands of nodes is “network performance.” To solve this, we also support various effective distribution systems such as Amazon Web services (EC2 and S3), TeraGrid, top computers (Sun constellation, IBM BlueGne/P, and also Cray XT5), and software programming platforms (MapReduce, Hadoop, Dryad, etc.).

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    Our research teams in the distributed computing field help to define what will be the future trends of research. Here we also listed a few for your reference,

Advanced Technologies in Distributed Computing

  • Micro services
  • Artificial Intelligence system
  • Heterogeneous clouds also for cyber infrastructures
  • BlockChain-as-a-Service (BaaS)
  • Dynamic Orchestration
  • Unikernels
  • Small Satellite Technologies
  • Many core architecture
  • Machine learning powered monitoring services
  • Containerization
  • Large scale distributed systems
  • FPGAs also with reconfigurable computing
  • Energy efficient computing

Guidance in Distributed Computing

     PhD Guidance in Distributed Computing service medium to guaranteed your success. We also have world-class pioneering researchers who have extraordinary knowledge in all key research areas. Distributed computing is also an emerging field, and also the subfields of distributed computing are trust management, development of cryptographic tools, security issues, probability theory, also complex distributed systems, etc. Our guidance in distributed computing is also a piece of your success and works towards patience. We also offer both research guidance (Proposal, paper, and thesis) and project guidance (Programming, software, tools, and coding implementation) for our PhD scholars.

 Let’s see our major research areas in distributed computing,

Major Research Areas in Computing

  • Big data
  • Cloud computing
  • Apache Spark
  • Apache Hadoop
  • Grid computing
  • Super computing
  • Many core computing
  • Storage systems
  • Data intensive computing
  • Data Intensive computing also with GPUs
  • Distributed and Parallel File Systems
  • Data Intensive computing also with Databases
  • Local/global resource management
  • Parallel I/O files

    Here the sub-areas of big data, apache spark and also hadoop are demonstrated for our young scholars and budding students.

Distributed-Computing in big data

  • Privacy and security also in social big data
  • Blockchain technology
  • Cryptocurrency
  • Advanced persistent threat
  • Spatiotemporal big data challenges
  • Bio-inspired algorithms also for complex ephemeral environments

Distributed Computing in Apache Spark

  • Fault-tolerant model also based stream processing on large clusters
  • Lightning-Fast Computing Clusters
  • Relational data processing in spark
  • Unifying data parallel and also parallel graph analytics-GraphX
  • Machine learning in Spark

Distributed-Computing in Hadoop:

  • Machine learning automation
  • Data Security and also Governance
  • Data Fabrics Spreading
  • Big data strategy beyond Hadoop
  • Cloud Gravity Grows
  • Data virtualization with business intelligence

Development Tools and Software’s

  • Q2ADPZ
  • Alchemi
  • Apache Spark
  • ECMNet
  • Apache Hadoop
  • JPPF
  • GridSim
  • SimGrid
  • And also Nimrod

Purpose of Tools and Software’s

  • BOINC: Free and Open Source Software to provide set of tools also for creating and managing distributed computing
  • DOGMA: Research basis tool written java that provides global web based user interfaces also for distributed computing based applications
  • Q2ADPZ: Open source Quite Advanced Distributed Parallel system also for distributed computing.
  • Alchemi: Grid computing framework (.Net) that also easy to use in grid environment
  • Apache Spark: Database runs on Hadoop and also it is a general and fast engine to process large scale data in the cloud.
  • ECMNet: Client or Server software also to designed ECMNet projects e.g. Minimal equal sums of like powers
  • Apache Hadoop: Open source software that also more reliable and scale for distributed computing
  • JPPF: Software that also easy to parallelize computationally intensive tasks and execute on grid.
  • GridSim: Simulator that functioning also for resource modeling and application scheduling for both distributed and parallel computing.
  • SimGrid: Scientific instrument also used to study about the large scale distributed systems
  • Nimrod: Tool to create and also execute computationally parallel programs over grid.

Major Research Topics in Distributed-Computing

  • Online entertainment
  • Electronic Commerce
  • Load balancing also for parallel computations
  • Collaborative research
  • Fault free network emulation
  • Communication networks
  • Parallel architectures also using network emulations
  • Distance learning
  • Distributed Super Computing
  • Next generation Grid computers also in clouds
  • Large scale self-organized distributed systems
  • Concurrency control
  • Fault tolerance
  • Algorithmic efficiency
  • Incentive scheme also for cooperative computing
  • Fault tolerant geo-distributed data centers
  • Priority structure also with distributed independent automata
  • Intrusion detection system
  • Distributed caching algorithms
  • Distributed interference also for D2D communication