Review on AMES-Cloud Using Preservation, Fetching and Decisive Video Streaming Over Cloud Computing

The video traffic demands are increasing over a mobile network through wireless link cannot corporate with the demand of video traffics. The increasing traffic demand is accounted by video streaming and downloading. Hence, there is a gap between link capacity and traffic demands along with the time varying condition which is result in the poor quality of video streaming service over a mobile network such as sending long buffering time and intermittent disruptions due to limited bandwidth and link condition. By leveraging cloud computing technology, we propose a new mobile video streaming framework which has two main parts : Efficient social video sharing and Adaptive mobile video streaming which built a private agent which provides video streaming service for each mobile user in the network efficiently. To demonstrate its performance we implement a prototype of AMES-Cloud framework. Thus, it is crucial to improve the video quality service of streaming while using the computing resource and networking efficiently and also provides preservation over cloud computing. DOI: 10.17762/ijritcc2321-8169.15010

Enhancing the Performance of Transmission in Cloud Based Multimedia using Fault Tolerance Technique

An analyze to increase the speed of transmission , task arrival rate, response time, distribution probability of the response time Specifically the response time of the cloud base multimedia is structured and the fault tolerance in multimedia is analyzed thereby distribution probability is derived imposing the retrying tasks arrival rate are analyzed taking innumerable examples. Probability distribution of the response time is derived using metric that reflects in a better way the requirements of the customers. Analyze carried out on the percentage of response time that characterizes threshold response time. Inter relationship among the number of service resources, service rate, system performance, task. were also analyzed Retrying for fault tolerance is compared with the check- pointing technique. In the competitive world of wireless communication and the growth of multimedia services like real-time conferencing, photo- sharing ,video-on- demand , editing, image search is on high demand for cloud computing. The slogan of access to serve billions of people those who use mobile and wireless transmission on any device, anytime, anywhere. The cloud computing emerged to facilitate the execution of complicated multimedia tasks and are able to store and process multimedia application and distribute them without any discrepancies thereby eliminating the complexity of software installation and maintenance in users devices. DOI: 10.17762/ijritcc2321-8169.15021

Directory-based incentive management services for ad-hoc mobile clouds

Mobile cloud computing is envisioned as a promising approach to augment the computational capabilities of mobile devices for emerging resource-intensive mobile applications. This augmentation is generally achieved through the capabilities of stationary resources in cloud data centers. However, these resources are mostly not free and sometimes not available. Mobile devices are becoming powerful day by day and can form a self-organizing mobile ad-hoc network of nearby devices and offer their resources as on-demand services to available nodes in the network. In the ad-hoc mobile cloud, devices can move after consuming or providing services to one another. During this process, the problem of incentives arises for a node to provide service to another device (or other devices) in the network, which ultimately decreases the motivation of the mobile device to form an ad-hoc mobile cloud. To solve this problem, we propose a directory-based architecture that keeps track of the retribution and reward valuations (in terms of energy saved and consumed) for devices even after they move from one ad-hoc environment to another. From simulation results, we infer that this framework increases the motivation for mobile devices to form a self-organizing proximate mobile cloud network and to share their resources in the network

Spatial Throughput Maximization of Wireless Powered Communication Networks

Wireless charging is a promising way to power wireless nodes' transmissions. This paper considers new dual-function access points (APs) which are able to support the energy/information transmission to/from wireless nodes. We focus on a large-scale wireless powered communication network (WPCN), and use stochastic geometry to analyze the wireless nodes' performance tradeoff between energy harvesting and information transmission. We study two cases with battery-free and battery-deployed wireless nodes. For both cases, we consider a harvest-then-transmit protocol by partitioning each time frame into a downlink (DL) phase for energy transfer, and an uplink (UL) phase for information transfer. By jointly optimizing frame partition between the two phases and the wireless nodes' transmit power, we maximize the wireless nodes' spatial throughput subject to a successful information transmission probability constraint. For the battery-free case, we show that the wireless nodes prefer to choose small transmit power to obtain large transmission opportunity. For the battery-deployed case, we first study an ideal infinite-capacity battery scenario for wireless nodes, and show that the optimal charging design is not unique, due to the sufficient energy stored in the battery. We then extend to the practical finite-capacity battery scenario. Although the exact performance is difficult to be obtained analytically, it is shown to be upper and lower bounded by those in the infinite-capacity battery scenario and the battery-free case, respectively. Finally, we provide numerical results to corroborate our study.Comment: 15 double-column pages, 8 figures, to appear in IEEE JSAC in February 2015, special issue on wireless communications powered by energy harvesting and wireless energy transfe

Scheduling Independent Parallel Jobs in Cloud Computing: A Survey

The impressive and rapid development of the internet and wireless networks leads to growing of users in the last decade. Therefore, the limited resources of these systems are now more evident than in the past. Cloud computing is the latest technology to handle the limitation of resources for users. Type of jobs play the main role in the design of scheduling algorithms. A job can be run simultaneously by multi-processor called parallel job, while the job can run by a single processor called serial job. In addition, based on dependency of jobs to each other, the jobs can be divided into dependent and independent jobs. Scheduling the independent parallel jobs is one of important challenges in cloud computing. Hence, in this paper, we classified the existing algorithms of scheduling independent parallel jobs into two main categories including Non-Layer and Two-Layer. This division is performed based on the number of jobs running on a processor simultaneously. Furthermore, the existing scheduling algorithms belong to each categories are divided into two subcategories based on their solving techniques including heuristic and metaheuristic. Then, the algorithms belong to each category are described in detail. After that, these algorithms are compared to each other based on their different attributes. Our analysis show that the existing Two-Layer scheduling algorithms focus on cost parameter to increase the performance of scheduling algorithms by reducing the waste time of CPU through simultaneous assigning more than one job to each physical machine, while Non-Layer scheduling algorithms didn't pay attention to this issue and only employ techniques to manage the scheduling queue in order to improve the different parameters such as cost, energy, load balancing and deadline

A Framework for Energy-efficient Mobile Cloud Offloading

Esilekerkivad nutitelefonide tehnoloogiad on kogenud geomeetrilist kasvu ja on praegu veel tõusuteel. Inimesed kasutavad nutitelefone oma igapäevastes tegevustes nagu e-maili saatmine, fotode ja videode jagamine läbi erinevate peer-to-peersotsiaalvõrgustiku jaoturite ja nii edasi. Viimastel aastatel on nutitelefonid kogenud suuri tehnoloogilisi edusamme ja innovatsiooni seoses töötlusvõimekusega ja saab nüüd kasutada keerukate ja ressursimahukate ülesannete täitmiseks rakendustes, näiteks videode monteerimine ja töötlemine ning objekti äratundmine. Kuigi enamus nutitelefone on oluliselt täiustatud, et hakkama saada suurendatud rakendustega, millel on keerukad arvutusvajadused, piiravad neid ikkagi nende energiavarud, näiteks aku kestvus. Akutehnoloogia ei ole arenenud nii kiirelt kui teised nutitelefoni valdkonnad ja seega arvutusintensiivsete ülesannete läbiviimine põhjustaks selle kiire kahanemise; tõestuseks vajadus pidevalt laadida seadme akut. Mitmeid meetodeid on pakutud välja energiasäästu maksimeerimiseks mobiilsetel seadmetel. Mõned neist aeglustavad keskprotsessor või lülitavad ekraani välja, kui on tegevusetud. Nende hulgast kõige märkimisväärsem tehnika nutitelefoni energia säästmiseks on arvutusvõimsuse koormuse jaotamine. See hõlmab teatud ülesannete töötluse üleviimist piiratud ressurssidega nutitelefonist kaugesse ressursirikkasse seadmesse hõlbustades seega nutitelefoni energia tarbimist. See on küllaltki lai uurimisvaldkond ja on hulganisti panustatud selle ala arendamiseks. Sellele vaatamata on veel palju tööd vaja teha seoses energia säästmisega läbi arvutusvõimsuse koormuse jaotamise korduva ressursimahuka töötlemise ajal. Selles teadusuuringus on me eesmärk vähendada energia tarbimist korduva energiamahuka töötlemise ajal. Me arvestame konteksti teadlikkust pakkudes välja plaanuri mudelit, mis saaks vähendada mobiilse seadme energia kiiret vähenemist seega saavutades meie eesmärgi. Pakume teenusele orienteeritud raamistikku eesmärgiga võimaldada energiatõhusa ülesande täitmist mobiilsel seadmel plaanuri käitumisalgoritmi abil. Me arendame kontseptsiooni tõestuse prototüüpi Android seadmel, et demonstreerida ja hinnata raamistiku energiasäästu võimekust.Emerging smartphone technologies has experienced a geometric increase and is currently still on the rise. People use the smartphone for their day-to-day activities such as sending emails, sharing photos and videos through various peer-to-peer social network hubs and so on. In the last few years, the smartphone has experienced massive technological advancements and innovation with respect to its processing capabilities and can now be used to perform complex, resource-intensive tasks in advanced applications like video editing and processing, and object recognition. Although most smartphones have been greatly augmented to handle advanced applications with complex computational needs, they are still limited in terms of their energy resources i.e. battery life. Battery technology has not evolved as rapidly as other areas of the smartphone and so the execution of computational-intensive tasks would cause its rapid depletion; evidenced by the need to constantly charge the device battery. Many techniques have been proffered to maximize energy conservation on mobile devices. Some of which are slowing down the CPU, or shutting off the screen when idle. Among these, the most notable technique for conserving smartphone energy is computation offloading. This basically involves the transfer of the processing of certain tasks from a resource-constrained smartphone to a remote, resource-rich device thereby facilitating energy conservation on the smartphone. This is a fairly large research area and numerous contributions have been made towards advancement in this field. However, much work is yet to be done with regards to energy conservation through offloading during recurrent resource-intensive processing. In this research study we aim to reduce energy consumption during continuous, energy-intensive processing. We consider context-awareness in proposing a scheduling model that could potentially minimize the speedy depletion of mobile device energy thus achieving our aim. We propose a service-oriented framework towards enabling energy-optimal task execution through a task scheduling offload algorithm. We develop a proof-of-concept prototype on an Android device to demonstrate and evaluate the framework’s energy conserving capabilities