Computation Migration Oriented Resource Allocation in Mobile Social Clouds
ABSTARCT :
The rapid growth of mobile device (e.g., smart phone and bracelet) has spawned a lot of new applications, during which the requirements of applications are increasing, while the capacities of some mobile devices are still limited. Such contradiction drives the emergency of computation migration among mobile edge devices, which is a lack of research currently. In this work, we focus on addressing the computation migration oriented resource allocation problem among mobile edge devices. Specifically, we first construct a framework for Mobile Social Cloud(MSC), in which the mobile devices with rich resources are abstracted as resource suppliers and those resource-lacking devices are abstracted as resource demanders. Then, a mathematical model is formulated and an evolutionary algorithm is proposed to effectively solve this model based on decomposition, dominance and genetic operations. Moreover, the parallel computing is introduced to further improve the efficiency of the proposed algorithm. The experimental results indicate that the proposed algorithm outperforms the other state-of-the-art methods and it improves the calculation efficiency by about 178% (2 cores) and 262% (3 cores) by introducing parallel computing.
EXISTING SYSTEM :
? This allows MEC to be an alternative host for existing services in cloud computing, and allows it to host new types of services [Wan+18; Mei+19].
? In this case ?i can model the upper bound of the sum utilization of the VSs, such that there exists a schedule that allows each VS to access the required computation time per period.
? However, existing c loud-based schemes increase network complexity and can’t operate effectively without modifying current IP-based network elements2 (see the “Current Work in Resource Management in Cloud Networks” sidebar).
? To exploit existing network infrastructures, access nodes can join the network in the form of a gateway via the SDN-enabled user terminal equipment.
DISADVANTAGE :
? The timing and data requirements exacerbate the efficient task scheduling and resource allocation problems.
? The scheduling problem is divided into three parts: the processing environment (cloud virtual machines), the nature of the real-time task (fixed priority system), and the optimization criteria (time- and cost-efficient allocation).
? The problem becomes more challenging when such systems need data from external sources.
? The reason for using MATLAB is that it provides a multiprocessing environment for solving complex mathematical problems demanding powerful computations.
PROPOSED SYSTEM :
• In the proposed solution, the constraints of physical locations are relaxed by allowing an arbitrary placement of computational resources.
• The resulting problem is NP-hard, and an approximation algorithm is proposed. However, in the proposed solution resilience is only considered as part of the objective, and thus the resilience requirement of each user may not be guaranteed.
• By relating to the set cover problem, the authors show that the proposed service placement problem is NP-hard, and then propose an approximation algorithm.
• The proposed solution first computes the placement of services, and then computes the optimal flow distribution.
ADVANTAGE :
? The lower the makespan value, the better the performance of the RA scheme.
? The other performance measurement criterion is the execution cost minimization.
? In addition to the makespan and cost values, two more performance measures considered in the evaluation results are the percent share of the data transfer time and local data access.
? In this case, the more locally accessed files decrease the impact of remote data files transfer on the performance.
? The Greedy approach exhibits degraded performance because there is a very less probability of finding appropriate computing resource for tasks assignment.
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