Parallel and Distributed Systems in Cloud Computing

The practice of managing distributed large data computation and storage based on pay-as-you-go and parallel service is said to be as parallel and distributed systems in cloud computing. 

It is a wise-spread platform to give more innovative ideas to handle computing resources, applications, and services. In the beginning, the first computer faced more challenges in treating massive data computation and resource allocation. To overcome these issues, parallel and distributed systems are introduced. These systems make the computing process as easy as possible in a cloud environment. 

This page talks about the new developments in parallel and distributed systems in cloud computing with significant research ideas, directions, and technologies!!!

Due to the vast number of benefits, parallel and distributed systems are growing fast in the cloud computing field. Since these concepts are keen to understand the technical issues and functional requirements in the design of the best cloud systems

Our resource teams have so many years of experience in handling parallel and distributed computing models. So, we are always passionate to create continuous achievements in the field of cloud computing. This makes our handhold research scholars and final year students select us all the time for their dream projects. Here, we have given you some main benefits of parallel and distributed systems in cloud computing. 

Top 6 Emerging Technologies for parallel and distributed systems in cloud computing

Benefits of Parallel and Distributed Computing 

  • Robustness – When any datacentre/server in a cloud-enabled network may undergo shutdown, it will not affect the services of remaining systems 
  • Boundless Horizontal Scaling – When more systems are added to cloud-enabled networks, the network is capable to accept those systems without affecting other regular services
  • Low Delay – When the machines/systems are located nearer to users, service request and delivery is performed in less time

Before getting into the research information, first make yourself clear in line with the difference between cloud computing, parallel computing, and distributed computing. Although all these technologies may give similar look, there is some high dissimilarity among them. For your information, here we have revealed the key differences between these technologies. Let’s have a quick look over the below points.

Cloud Computing vs. Parallel Computing vs. Distributed Computing 

  • Cloud computing – Aimed to provide services to users based on their demands through the user model.
  • Distributed computing – Aimed to split one task into multiple sub-tasks and distribute them to multiple systems for accessibility through perfect coordination  
  • Parallel computing – Aimed to concurrently execute multiple tasks through multiple processors for fast completion

What is parallel and distributed computing in cloud computing?

Now, we can the role of parallel and distributed computing in the field of cloud computing. In recent days, these two technologies are gaining more attention among cloud researchers. Since, both the technologies are great in creating positive contributions over cloud research platforms. In specific, parallel systems comprises multiple processors to process the tasks simultaneously in shared memory, and distributed system comprises multiple processors to distribute the same task to multiple sub-tasks. Here, we have given you the layers of a computing system.

Layers of Parallel and Distributed Cloud Computing

  • Cloud Infrastructure Layer
    • Virtual Switch
    • Virtual Router
    • Cloud Server
  • Distributed Computing Layer
    • Distributed Hadoop Architecture
      • Mapreduce Model
      • Storage Model
    • Spark Stream and Batch Computing
    • Hive, Spark basis, and Hbase
  • Application / Data Access Layer
    • The sub-system of AIS collection
    • The sub-system of the User interface

As mentioned earlier, nowadays cloud computing holds hands tightly with parallel and distributed computing. Since cloud computing services and resources are largely employed by both individual and big-scale industries/organizations. Majorly, cloud systems function based on the client-server model through thin client/software programs on user machines. Moreover, it performs all the required computations of particular task(s) in the cloud platform. Further, many of the cloud applications are recognized as data-intensive which utilizes a greater number of instances at the same time. 

Parallel and Distributed Cloud Computing Applications

  • Computing Environ – Cloud, Clusters, Grids, etc.
  • Data – Mobile data, Big data, Data transmission, Data preservation, etc.
  • Distributed Modes – Internet, Internet of things, etc.
  • Software-defined resources – NFV, SDN, SD-datacenter, SD-storage, etc.

Parallel computers are categorized based on the hardware supportive level for parallelism. In the cloud environment, distributed computing concept become a more important class. On the one hand, the distributed system utilizes parallel computing which is loosely coupled. Here, many computers process their computation in parallel form. Also, the computers from the distributed location are connected to form the whole network. Further, this distributed system use “dictionary memory”. 

On the other hand, the message passing technique shares information among processors and uses its own memory of each. For your reference, here we have given top-3 real-world applications of parallel and distributed systems as examples.

What are some examples of distributed systems used today?

  • Real-world Process Control – Aircraft Controller
  • Telecommunication – Cellular Communication and Telephone Networks
  • Networking Applications – Peer-to-Peer Services and World Wide Web

For add-on advantage, we have also given other Latest Parallel and Distributed Computing Applications  

  • Parallel Neural Networks
  • Distributed Computing Impact on Banking Service
  • Comparison of Current and Future IT infrastructures
    • For instance: SDS, NFV, Clouds, Clusters, SDN, Grids, etc.
  • Growth of Internet Applications from Social/political Perception
  • Parallel and Distributed Techniques for Real-time Systems
  • Role of Upcoming Internet Services in Industrial Internet of Things 
  • Improvisation of Trust, Privacy, Integrity and Security Aspects
  • Distributed Services Impact on Sustainable Development Solutions

In the conventional methods, list of distributed computing majorly focuses only on code portability, outcome accuracy, resource accessibility, and transparency. Presently, it has a wide perception in the aspects of security, speed, scalability, efficiency, etc. All these are central to parallel systems. So, the current researchers are focusing their study on the following special characteristics. Since these are issues that are currently attempted by everybody to achieve the best outcome of parallel and distributed models. Let’s see the main issues of parallel and distributed computing systems.

What are the issues in parallel and distributed Systems in cloud computing?
  • Security
  • Energy Efficiency
  • Scalability
  • Optimization
  • Robustness
  • Parallel performance
  • Dependability
  • Execution control
  • Scheduling 
  • Offloading

Next, we can see the important research trends of parallel and distributed computing systems. We have a link with experts in parts of the world. Also, some of them are a member of reputed research journals like IEEE, Springer, etc. So, we always update our latest research areas and ideas based on the evolving research trends. We guarantee you that all our research updates are very truthful. Here, we have listed only a few trend-setting ideas in parallel and distributed computing. 

Current Trends of Parallel and Distributed Systems in Cloud Computing 
  • Energy-Aware Cloud Models and Algorithm for Parallel and Distributed Systems 
  • Real-time Cloud-based Parallel and Distributed Applications 
  • Development of Distributed or Parallel System 
  • Parallel Algorithms Performance Assessment in Distributed Environs

In addition, we have also given you developing and advanced technologies of parallel and distributed systems. For your ease, we have itemized the technologies based on important research areas. If you are curious to know more about technological developments in your interesting research areas, then connect with us. We are ready to give sufficient information in your requested aspects. To the end, we assure you that our proposed technologies are sure to create masterwork in parallel and distributed computing project ideas.

Emerging Technologies for Parallel and Distributed Systems 

  • Edge Computing
    • New Privacy and Security Solutions
    • Distributed Service Allocation and Management 
    • Fog-Edge Resource Allocation and Control
    • Networking Architecture and Fog-Cloud Models
    • Edge Caching / Analytics and Distributed Data Centers
    • Modern Protocols for Cloud-Edge Communication
  • Cloud Computing
    • IoT-enabled Green Cloud Systems
    • Big Cloud Data Processing and Analytics
    • Privacy and Security Challenges in Clouds
    • Low-Cost Cloud Resources Provisioning
    • Energy-Aware Mobile Cloud Computation
    • Cloud Resource Distribution and Maintenance
    • Cloud Storage and Capacity Management
  • Hadoop 
    • Automated ML Systems
    • Big Data Policies for Advanced Hadoop
    • Gravity-based Cloud Developments
    • Security Governance Solution for Big Data
    • Data Fabrics Architecture for Distributed Data
  • Big Data
    • Improved Persistent and Security Threat Control 
    • Privacy Preservation in Social Big Data Analytics
    • Secure Large-scale Smart City Data Management 
    • Blockchain Model for Cryptocurrency Transaction
    • Security Challenges in Huge Spatiotemporal Data 
    • Bioinspired-based Complex Ephemeral Platforms 
  • Spark
    • Interactive Data Management in Spark
    • Apache Spark-based Machine Learning Models
    • Light-weight Models for Fast Cluster Computing
    • Merging Graph-Parallel and Data-Parallel Analytics
    • Robust Stream Processing Model on Big-scale Clusters

Furthermore, we have also given you some important models that are highly demanded in parallel and distributed computing systems. All these models are very effective to achieve targeted research outcomes. Also, all these cloud supportive models assuredly provide you with a new dimension of parallel and distributed computing research. On knowing the importance of these models, we have designed several awestruck research ideas. Based on your demanded research areas, we are ready to share our collections with you.

Models of Parallel and Distributed Computing

  • LogP and BSP
  • Quantum Computing 
  • Mobile Computing 
  • Radio Communication 
  • GPU Computing and Programming 
  • Bio-inspired Computing
  • Cellular Automata 

Our developing team is constructed with strong experienced developers. Thus, our developing team is worth capable of solving any level of complex problems. Since we are familiar with all emerging algorithms and techniques to crack research issues. 

In a nutshell, our team will fulfill your expected results through their incredible programming skills. We assure you that our team will help you in all aspects till the end of your parallel and distributed systems in cloud computing study.

Recent parallel and distributed algorithms for cloud computing 

  • Optimization
  • Machine Learning
  • MapReduce
  • Numerical Techniques
  • GPU Applications 
  • Graph Theories
  • Randomized Techniques
  • Geometric Approaches
  • Approximation Methods
  • Distributed Network Algorithms
  • Combinatorial / Hybrid Algorithms

Our developing team encourages our clients to come up with their ideas which will be more helpful to meet your research expectation with a high-quality result. Thus, this will build a trustable and healthy bond between our clients and our team. 

For your knowledge, here we have given you some exciting research ideas that are hugely demanded by our connected current research scholars and final year students. Beyond this list of ideas, we also provide you with other growing research areas with their project ideas. 

Research Ideas for Parallel and distributed Computing

Latest Ideas on Parallel and Distributed Systems in Cloud Computing 

  • Large-scale Distributed Data Privacy Challenges and Solutions
  • Energy and Cost-Aware Distributed and Parallel Systems 
  • Robust Computation of Parallel Systems in Mixed Frameworks
  • Performance Assessment and Resource Control
  • Resources Scheduling and Allocation in Distributed Platform
  • Privacy and Trust Schemes for Super Computing Applications
  • Advanced Security Techniques for Distributed Resource Management

In the above section, we have already seen the models of parallel and distributed systems. To the continuation, now we can see about the core frameworks and programming models that are essential to developing different kinds of parallel and distributed computing models. 

For your handpicked project, it may vary based on your project requirements. Our developers are adept to help you in choosing a suitable one for your project. By the by, the majority of projects prefer to choose Hadoop and MapReduce frameworks while handling massive data.

Frameworks for Parallel and Distributed Programming 

  • Hadoop 
    • Well-known library for distributed system
    • Used for developing large business applications
    • Capable to perform on huge datasets
    • Characteristics
      • Reliability
      • Cost-effective
      • User Accessibility over clusters
      • Scalability
  • MPI
    • Used to develop parallel computing programs
    • Able to work on a distributed platform
    • The well-known library which can be accessed by FORTRAN / C
    • Characteristics
      • Support point-to-point and collective transmission
      • Enable both Asynchronous and Synchronous form
      • Allows message-passing execution
  • MapReduce
    • Used for web search
    • Able to process large-scale data over more clusters
    • The well-known web programming model
    • Characteristics
      • Produce a set of key/value pairs using the Map function
      • Combine all intermediate values through the same key using Reduce function

Moreover, we have also given you the future directions of cloud-enabled parallel and distributed computing systems. All these are accurately predicted by our team of experts based on current research demands. As well, we have collected advanced research ideas in these areas also. We assure you that we support you in all possible research perspectives for your PhD / MS study. So, communicate with a team to have your pearl of research topic from our project topics list.

Future Directions of Parallel and Distributed Systems

  • Parallelization in Social Network 
  • Inter-responsive Parallel and Distributed Models
  • Distributed Systems in the Internet of Things 
  • Large-scale Parallel Data Processing and Visualization
  • Parallel and Distributed Systems in Real-world
  • Quality of Service (QoS) Enhancement 

On the whole, we guarantee you that we provide flawless services in your entire research journey until you reach your research destination. We provide not only research and development services but also manuscript writing services. As well, our team is much concerned and aware of time management. Thus, you will receive your project on time as per schedule. Also, we update your state of project development related to parallel and distributed systems in cloud computing regularly in a certain time interval. Similarly, we give complete assistance on other services too.