Security modeling and efficient computation offloading for service workflow in mobile edge computing

Abstract

It is a big challenge for resource-limited mobile devices (MDs) to execute various complex and energy-consumed mobile applications. Fortunately, as a novel computing paradigm, edge computing (MEC) can provide abundant computing resources to execute all or parts of the tasks of MDs and thereby can greatly reduce the energy of MD and improve the QoS of applications. However, offloading workflow tasks to the MEC servers are liable to external security threats (e.g., snooping, alteration). In this paper, we propose a security and energy efficient computation offloading (SEECO) strategy for service workflows in MEC environment, the goal of which is to optimize the energy consumption under the risk probability and deadline constraints. First, we build a security overhead model to measure the execution time of security services. Then, we formulate the computation offloading problem by incorporating the security, energy consumption and execution time of workflow application. Finally, based on the genetic algorithm (GA), the corresponding coding strategies of SEECO are devised by considering tasks execution order and location and security services selection.Extensive experiments with the variety of workflow parameters demonstrate that SEECO strategy can achieve the security and energy efficiency for the mobile applications.

Security and Cost-Aware Computation Offloading via Deep Reinforcement Learning in Mobile Edge Computing

“…We can observe from Figure 3 that confidential service (denoted as E) is first employed to encrypt the offloaded task. e security levels and processing rates of cryptographic algorithms for confidential service [20,30,36,37] can be found in Table 2. en, to protect the offloaded computation task from alteration attacks, integrity service (denoted as H) is successively employed to implement a hash algorithm to it.…”

“…en, to protect the offloaded computation task from alteration attacks, integrity service (denoted as H) is successively employed to implement a hash algorithm to it. e security levels and processing rates of hash functions for integrity service [20,30,36,37] can be seen in Table 3. After the encrypted task is delivered to the ith edge server, it is firstly decrypted (denoted as DE) and its integrity is verified (denoted as IV), and then it is executed, and finally the computation result is sent to the mobile device.…”

“…Security service incurs sometime overhead. According to [20], the total encryption overheads of task t offloaded to the ith edge server can be calculated by the following equation: ) denote the processing rates of the security levels sl cf and sl ig , respectively. e total decryption overheads of the offloaded task t in the ith edge server can be computed by the following equation:…”

“…(i) We select appropriate security servers to guarantee the offloaded tasks' security. e security overhead model [20] is exploited to quantify the security time. Based on it, the security workload can be measured.…”

“…e remaining (a e c,i (τ) − d e c,i (τ)) tasks wait to be executed in execution queue. e total waiting time tt w c can be calculated by equation (20). At last, they are further decrypted and executed.…”