Optimal Task Allocation and Coding Design for Secure Edge Computing with Heterogeneous Edge Devices
ABSTARCT :
In recent years, edge computing has attracted significant attention because it can effectively support many delay-sensitive applications. Despite such a salient feature, edge computing also faces many challenges, especially for efficiency and security, because edge devices are usually heterogeneous and may be untrustworthy. To address these challenges, we propose a unified framework to provide efficiency and confidentiality by coded distributed computing. Within the proposed framework, we use matrix multiplication, a fundamental building block of many distributed machine learning algorithms, as the representative computation task. To minimize resource consumption while achieving information-theoretic security, we investigate two highly-coupled problems, (1) task allocation that assigns data blocks in a computing task to edge devices, and (2) linear code design that generates data blocks by encoding the original data with random information. Specifically, we first theoretically analyze the necessary conditions for the optimal solution. Based on the theoretical analysis, we develop an efficient task allocation algorithm. Using the task allocation, we then design secure coded computing schemes, for two cases, (1) with redundant computation and (2) without redundant computation. Moreover, we also theoretically analyze the optimization of the proposed scheme. Finally, we conduct extensive simulation experiments to demonstrate the effectiveness of the proposed schemes.
EXISTING SYSTEM :
? In existing cloud infrastructures, the data are sent to cloud servers for further processing and then returned to the devices.
? We provide an analysis of solutions to these challenges from the existing literature.
? Collect research studies regarding the architectural overview for cloud, fog, and edge computing and research on existing resource management techniques.
? There is a certain amount of time that is needed to accomplish the communication between the cloud and the existing IoT devices, which will be automatically added to the processing time.
? Their results represent a mapping between the proposed taxonomy and existing literature on the cloud, fog, and edge computing paradigm.
DISADVANTAGE :
? In this paper, we study task allocation and coding design, two highly-coupled problems in secure coded edge computing, in a unified framework.
? Therefore, in this paper, we formulate an optimization problem to minimize the total resource usage in Secure Coded Edge Computing (SCEC) with ITS guarantee.
? We also prove the lower bound of the MCSCEC problem, which enables us to further design the optimal task allocation schemes.
? We will show the optimal strategy for the MCSCEC problem, which can be divided into two stages, task allocation and coding design respectively.
PROPOSED SYSTEM :
• To accommodate some of these features, the computational paradigm fog computing was proposed.
• However, the study could be further extended by providing a proper detailed list of proposed solutions from the reviewed literature, and respectively their classification.
• To manage a large set of tasks that are working together and are dependent on a certain set of resources, task scheduling algorithms have been proposed to define a schedule to service tasks to avoid conditions such as deadlocks.
• The proposed methodology was tested with iFogSim and analyzed with different existing dynamic algorithms.
ADVANTAGE :
? We conduct simulation to evaluate the performance of the proposed solution for the MCSCEC problem.
? In particular, we compare the performance of the proposed solution for the MCSCEC problem with the lower bound shown in Theorem 1 and the following baseline algorithms.
? It also shows that the performance of MCSCEC is very close to the LB, and the relative difference between the total cost of MCSCEC and LB is less than 0.5% when all the paramters are sufficiently large.
? We then design an efficient secure coded computing scheme to achieve information theoretical security with minimal cost and low decoding complexity.
? It is important to carefully design code such that the user can successfully decode the final result while achieving security requirements.
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