Performance Analysis of a Delay Constrained Data Offloading Scheme in an Integrated Cloud-Fog-Edge Computing System

Abstract : The recent growth in intensive services and applications demand has triggered the functional integration of cloud computing with edge computing capabilities. One of the main goals is to allow a fast processing to tasks with strict real time constraints in order to lower the task dropping probability due to expiration of the associated deadlines. This paper deals with the performance evaluation and optimization of a three layers cloud-fog-edge computing infrastructure by resorting to the use of queueing theory results. In particular, a Markov queueing system model with reneging is proposed for the cloud subsystem, in order to consider the premature computation requests departure due to their deadline expiration. Furthermore, a computational resources allocation method is proposed with the aim at maximizing the social welfare metric, constrained to specific quality of service requirements. Finally, the proposed queueing theory analysis as well as ofthe computational resources allocation approach is validated by comparing the obtained analytical predictions with simulation results.

 EXISTING SYSTEM :

 Mobile cloud offloading that migrates heavy computation from mobile devices to powerful cloud servers through communication networks can alleviate the hardware limitations of mobile devices thus providing higher performance and saving energy. Different applications usually give different relative importance to response time and energy consumption. If a delay-tolerant job is deferred up to a given deadline, or until a fast and energy-efficient network becomes available, the transmission time will be extended, which can save energy because a more energy-efficient communication channel and a less energy-restricted computation platform may become available. However, if the reduced service time fails to cover the extra waiting time, this policy may not be competitive.

 DISADVANTAGE :

 ? Energy consumption and transmission time increase in proportion to the transferred file sizes. When the same volume of data was transmitted, WiFi has relatively lower energy consumption than 3G. Moreover, the device’s energy consumption via each communication network is proportional to its data transmission time. ? The issues of time and energy saving on mobile devices are becoming increasingly relevant. Many research efforts have been devoted to offloading computation to remote servers in order to shorten execution time or save energy.

 PROPOSED SYSTEM :

 In this paper, we investigate two types of delayed offloading policies, the partial offloading model where jobs can leave from the slow phase of the offloading process and be executed locally on the mobile device, and the full offloading model, where jobs can a bandontheWiFi Queueand be offloaded via the Cellular Queue. In both models, we minimize the Energy-Response time Weighted Product (ERWP) metric. Not surprisingly, we find that jobs abandon the queue often when the availability of the WiFi network is low. In general, for delay-sensitive applications the partial offloading model is preferred under a suitable reneging rate, while for delay-tolerant applications the full offloading model shows very good results and outperforms the other offloading model when selecting a large deadline. From the perspective of energy consumption, the full offloading model will always be best, even if the deadline must be extremely long. Only if job response time is of high importance an optimal deadline to abort offloading in the partial offloading model or the WiFi transmission in the full offloading model can be found. For reduction of the energy consumption it will always be better to wait longer rather than compute locally or use the cellular network.

 ADVANTAGE :

 The models can be used to predict the average performance and energy consumption of mobile offloading under a given network environment deployment condition. It is possible to reduce the transmission time at the expense of some extra waiting time. The reduced transmission time at a later point in time directly translates to saving battery power of the mobile device.