A Survey on Comparison of Performance Analysis on a Cloud-Based Big Data Framework

Cloud computing is offering various IT services to many users in the work on the basis of pay-as-you-use model. As the data is increasing day by day, there is a huge requirement for cloud applications that manage such a huge amount of data. Basically, a best solution for analyzing such amounts of data and handles a large dataset. Various companies are providing such framesets for particular applications. A cloud framework is the accruement of different components which is similar to the development tools, various middleware for particular applications and various other database management services that are needed for cloud computing deployment, development and managing the various applications of the cloud. This results in an effective model for scaling such a huge amount of data in dynamically allocated recourses along with solving their complex problems. This article is about the survey on the performance of the big data framework based on a cloud from various endeavors which assists ventures to pick a suitable framework for their work and get a desired outcome.

Towards a Social Cloud Framework for Collaborative eResearch

<p>Collaboration has always been an important aspect of scientific research. The coming of internet opened the doors for greater levels of collaboration for the research community, first enabled by email and then by web 2.0 based online portals called VREs. A new force, social networks, are bringing a paradigm shift to online research communities. Social networks could foster a more vibrant research environment powered by social activities such as sharing, community creation, tagging and community groups. This thesis explores the idea of using the power of social networks to create a social cloud to contribute and share computing resources. The prototype implementation, called the Social Collaborative Cloud (SoCC), uses facebook as the underlying social network. The prototype was evaluated using simulations of both real and synthetic datasets, as well as real world tests.</p>

Towards a Social Cloud Framework for Collaborative eResearch

<p>Collaboration has always been an important aspect of scientific research. The coming of internet opened the doors for greater levels of collaboration for the research community, first enabled by email and then by web 2.0 based online portals called VREs. A new force, social networks, are bringing a paradigm shift to online research communities. Social networks could foster a more vibrant research environment powered by social activities such as sharing, community creation, tagging and community groups. This thesis explores the idea of using the power of social networks to create a social cloud to contribute and share computing resources. The prototype implementation, called the Social Collaborative Cloud (SoCC), uses facebook as the underlying social network. The prototype was evaluated using simulations of both real and synthetic datasets, as well as real world tests.</p>

Elegant Computational Intensive Task Offloading Scenario for Android

Despite the technical changes and enormous day by day upgradiation in the field of mobile computing the smart devices as well as IOT devices had experienced tremendous technical glitch, which narrow’s the life span and survivability of small scale processing devices. Today, end users are becoming more demanding and are expecting to run computational intensive tasks on their Smart phone devices and IOT devices. Therefore, virtual cloud computing (VCC) integrates local device computing and Cloud Computing (CC) in order to extend computational capabilities of smart phone devices and IOT devices using cloud offloading techniques. Computation Offloading tackles limitations of Smart phone devices and IOT devices such as limited battery duration, limited computational capabilities, and limited storage capacity by offloading the execution and workload to cloud which has better systems with better computation and storage capabilities. This paper aims to present the techniques to offload computational intensive tasks to cloud framework and analyses them along with traditional local execution techniques and their issues. Furthermore, it explores other important parameters based on which the applications are implemented such as offloading technique and partitioning of tasks.

Research on breakpoint area detection of computer communication network transmission data based on cloud framework

Traditional detection method of data breakpoint in computer communication network has some disadvantages, such as time consuming, etc. Firstly, the data of computer transmission breakpoints are stored based on cloud framework, and the density distribution characteristics of the region are extracted according to the breakpoint data. Then the optimal data breakpoint detection path is selected. Finally, the similarity of each data breakpoint is detected by the computer, so that the detection of data breakpoints is realized by computer. After experiments, the data breakpoint detection is realized, the results show that the designed method can detect data breakpoints accurately, which is time-saving and has a certain significance of popularization.

Applying Task Scheduling For IOMT-Cloud Business Optimisation Using AI

The IoMT-cloud enables a surplus extent of customers to get disseminated, versatile, and virtualized gear just as programming structure over the Internet. The IoMT-cloud is one of the principal headway used recently, it grants customers to get cloud resources over the internet remotely. Hence, we need to complete a reasonable task scheduling estimation to tolerably and viably meet these requests. The scheduling of task issue is perhaps the most essential issue in the IoMT-cloud since cloud execution depends prevalently upon it. Capable task scheduling administration should meet customer's requirements and improve the resources used to overhaul the introduction of the IoMT-cloud framework. To deal with this issue, in this investigation, we attempt to show the two most notable static and one dynamic task scheduling execution separately, short job first (SJF), first come first serve (FCFS), and round-robin (RR). Likewise, it was advanced using the AI technique known as genetic algorithm (GA). The CloudSim simulation framework is used to measure their impact on total execution time (TET), algorithm complexity, throughput, resource utilization, total waiting time (TWT), availability of assets, total finish time (TFT), cost, and resource utilization. The model proposed is to improve the viability of task scheduling for the IoMT-cloud stage with the best execution rate of 32.47ms. The exploratory results show that GA cuts down the cost of planning and reduces the total time, which is a convincing computation for the IoMT-cloud task scheduling.

Threshold based VM Placement Technique for Load Balanced Resource Provisioning using Priority Scheme in Cloud Computing

The unbalancing load issue is a multi-variation, multi-imperative issue that corrupts the execution and productivity of processing assets. Workload adjusting methods give solutions of load unbalancing circumstances for two bothersome aspects over-burdening and under-stacking. Cloud computing utilizes planning and workload balancing for a virtualized environment, resource partaking in cloud foundation. These two factors must be handled in an improved way in cloud computing to accomplish ideal resource sharing. Henceforth, there requires productive resource, asset reservation for guaranteeing load advancement in the cloud. This work aims to present an incorporated resource, asset reservation, and workload adjusting calculation for effective cloud provisioning. The strategy develops a Priority-based Resource Scheduling Model to acquire the resource, asset reservation with threshold-based load balancing for improving the proficiency in cloud framework. Extending utilization of Virtual Machines through the suitable and sensible outstanding task at hand modifying is then practiced by intensely picking a job from submitting jobs using Priority-based Resource Scheduling Model to acquire resource asset reservation. Experimental evaluations represent, the proposed scheme gives better results by reducing execution time, with minimum resource cost and improved resource utilization in dynamic resource provisioning conditions.