An SOA-Based Framework of Computational Offloading for Mobile Cloud Computing

Mobile Computing is a technology that allows transmission of audio, video, and other types of data via a computer or any other wireless-enabled device without having to be connected to a fixed physical link. Despite increasing usage of mobile computing, exploiting its full potential is difficult due to its inherent problems such as resource scarcity, connection instability, and limited computational power. In particular, the advent of connecting mobile devices to the internet offers the possibility of offloading computation and data intensive tasks from mobile devices to remote cloud servers for efficient execution. This proposed thesis develops an algorithm that uses an objective function to adaptively decide strategies for computational offloading according to changing context information. By following the style of Service-Oriented Architecture (SOA), the proposed framework brings cloud computing to mobile devices for mobile applications to benefit from remote execution of tasks in the cloud. This research discusses the algorithm and framework, along with the results of the experiments with a newly developed system for self-driving vehicles and points out the anticipated advantages of Adaptive Computational Offloading

Mobile Cloud Computing Architecture Model for Multi-Tasks Offloading

In modern era the cell phones has born through the significant technological advancements. But this resides a low multi tasks entity. Many people use mobile devices instead of PC’s. Cell phones has limited number of resources like limited storage, battery time and processing. The cloud computing offloading deals with these limitations. Cloud computing become more attractive as it reduce the cost and also time efficient. Business of all sizes can’t afford to purchase hardware and softwares but cloud computing provide these resources and executes multiple tasks and allows the user to access their data and provide other control in each level of cloud computing.  All of these techniques save smart phones properties or capabilities but it also becomes the reasons of communication cost between cloud and smart phone devices. The main advantage of cloud computing is to provide multiple properties at different prices. These applications has goal to attain versatile performance objective. In this research work, an architecture model for multi tasks offloading designed to overcome this problem. For this purpose CloudSim simulator use with the NetBeans and implement the MCOP algorithm. This algorithm solves the execution timing issue and enhances the mobile system performance. In this tasks are partitioning into two parts and then implemented on cloud site or locally. It reduces the time response and communication cost or tasks execution cost. Keywords: Mobile Cloud Computing, Mobile Computing Offloading, Smart Mobile Devices, Optimal Partitioning Algorithm

VirtFogSim: A parallel toolbox for dynamic energy-delay performance testing and optimization of 5G Mobile-Fog-Cloud virtualized platforms

It is expected that the pervasive deployment of multi-tier 5G-supported Mobile-Fog-Cloudtechnological computing platforms will constitute an effective means to support the real-time execution of future Internet applications by resource- and energy-limited mobile devices. Increasing interest in this emerging networking-computing technology demands the optimization and performance evaluation of several parts of the underlying infrastructures. However, field trials are challenging due to their operational costs, and in every case, the obtained results could be difficult to repeat and customize. These emergingMobile-Fog-Cloud ecosystems still lack, indeed, customizable software tools for the performance simulation of their computing-networking building blocks. Motivated by these considerations, in this contribution, we present VirtFogSim. It is aMATLAB-supported software toolbox that allows the dynamic joint optimization and tracking of the energy and delay performance of Mobile-Fog-Cloud systems for the execution of applications described by general Directed Application Graphs (DAGs). In a nutshell, the main peculiar features of the proposed VirtFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the placement of the application tasks and the allocation of the needed computing-networking resources under hard constraints on acceptable overall execution times, (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall system; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operational environments, as those typically featuring mobile applications; (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering, and (v) itsMATLAB code is optimized for running atop multi-core parallel execution platforms. To check both the actual optimization and scalability capabilities of the VirtFogSim toolbox, a number of experimental setups featuring different use cases and operational environments are simulated, and their performances are compared

Efficient GPU Cloud architectures for outsourcing high-performance processing to the Cloud

The world is becoming increasingly dependant in computing intensive applications. The appearance of new paradigms, such as Internet of Things (IoT), and advances in technologies such as Computer Vision (CV) and Artificial Intelligence (AI) are creating a demand for high-performance applications. In this regard, Graphics Processing Units (GPUs) have the ability to provide better performance by allowing a high degree of data parallelism. These devices are also beneficial in specialized fields of manufacturing industry such as CAD/CAM. For all these applications, there is a recent tendency to offload these computations to the Cloud, using a computing offloading Cloud architecture. However, the use of GPUs in the Cloud presents some inefficiencies, where GPU virtualization is still not fully resolved, as our research on what main Cloud providers currently offer in terms of GPU Cloud instances shows. To address these problems, this paper first makes a review of current GPU technologies and programming techniques that increase concurrency, to then propose a Cloud computing outsourcing architecture to make more efficient use of these devices in the Cloud.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported by the Spanish Research Agency (AEI) under project HPC4Industry PID2020-120213RB-I00

The importance of granularity in multiobjective optimization of mobile cloud hybrid applications

Mobile devices can now support a wide range of applications, many of which demand high computational power. Backed by the virtually unbounded resources of cloud computing, today's mobile cloud (MC) computing can meet the demands of even the most computationally and resource‐intensive applications. However, many existing MC hybrid applications are inefficient in terms of achieving objectives like minimizing battery power consumption and network bandwidth usage, which form a trade‐off. To counter this problem, we propose a data‐driven technique that (1) does instrumentation by allowing class‐, method‐, and hybrid‐level configurations to be applied to the MC hybrid application and (2) measures, at runtime, how well the MC hybrid application meets these two objectives by generating data that are used to optimize the efficiency trade‐off. Our experimental evaluation considers two MC hybrid Android‐based applications. We modularized them first based on the granularity and the computationally intensive modules of the apps. They are then executed using a simple mobile cloud application framework while measuring the power and bandwidth consumption at runtime. Finally, the outcome is a set of configurations that consists of (1) statistically significant and nondominated configurations in collapsible sets and (2) noncollapsible configurations. The analysis of our results shows that from the measured data, Pareto‐efficient configurations, in terms of minimizing the two objectives, of different levels of granularity of the apps can be obtained. Furthermore, the reduction of battery power consumption with the cost of network bandwidth usage, by using this technique, in the two MC hybrid applications was (1) 63.71% less power consumption in joules with the cost of using 1.07 MB of network bandwidth and (2) 34.98% less power consumption in joules with the cost of using 3.73 kB of network bandwidth