Energy cost minimization with job security guarantee in Internet data center

Li, Zhongjin; Ge, Jidong; Li, Chuanyi; Yang, Hongji; Hu, Haiyang; Luo, Bin; Chang, Victor

doi:10.1016/j.future.2016.12.017

Cited by 30 publications

(10 citation statements)

References 55 publications

(71 reference statements)

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“…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.…”

Section: Mobile Edge Computing Model As Depicted Inmentioning

confidence: 99%

“…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.…”

Section: Mobile Edge Computing Model As Depicted Inmentioning

confidence: 99%

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

Huang

et al. 2019

Wireless Communications and Mobile Computing

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With the explosive growth of mobile applications, mobile devices need to be equipped with abundant resources to process massive and complex mobile applications. However, mobile devices are usually resource-constrained due to their physical size. Fortunately, mobile edge computing, which enables mobile devices to offload computation tasks to edge servers with abundant computing resources, can significantly meet the ever-increasing computation demands from mobile applications. Nevertheless, offloading tasks to the edge servers are liable to suffer from external security threats (e.g., snooping and alteration). Aiming at this problem, we propose a security and cost-aware computation offloading (SCACO) strategy for mobile users in mobile edge computing environment, the goal of which is to minimize the overall cost (including mobile device’s energy consumption, processing delay, and task loss probability) under the risk probability constraints. Specifically, we first formulate the computation offloading problem as a Markov decision process (MDP). Then, based on the popular deep reinforcement learning approach, deep Q-network (DQN), the optimal offloading policy for the proposed problem is derived. Finally, extensive experimental results demonstrate that SCACO can achieve the security and cost efficiency for the mobile user in the mobile edge computing environment.

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Section: Mobile Edge Computing Model As Depicted Inmentioning

confidence: 99%

Section: Mobile Edge Computing Model As Depicted Inmentioning

confidence: 99%

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

Huang

et al. 2019

Wireless Communications and Mobile Computing

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“…Li et al developed a resource optimization architecture for minimizing the energy cost in data centers while assuring system security and meeting delay targets. This approach considers the use of frameworks for adaptively timing computation to capitalize on utilizing resources that are the lowest cost or alternately most available [25].…”

Section: B Resource Efficiencymentioning

confidence: 99%

The Future Internet of Things: Secure, Efficient, and Model-Based

Siegel

Kumar

Sarma

2018

IEEE Internet Things J.

102

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The Internet of Things' (IoT's) rapid growth is constrained by resource use and fears about privacy and security. A solution jointly addressing security, efficiency, privacy, and scalability is needed to support continued expansion. We propose a solution modeled on human use of context and cognition, leveraging cloud resources to facilitate IoT on constrained devices. We present an architecture applying process knowledge to provide security through abstraction and privacy through remote data fusion. We outline five architectural elements and consider the key concepts of the "Data Proxy" and the "Cognitive Layer." The Data Proxy uses system models to digitally mirror objects with minimal input data, while the Cognitive Layer applies these models to monitor the system's evolution and to simulate the impact of commands prior to execution. The Data Proxy allows a system's sensors to be sampled to meet a specified "Quality of Data" (QoD) target with minimal resource use. The efficiency improvement of this architecture is shown with an example vehicle tracking application. Finally, we consider future opportunities for this architecture to reduce technical, economic, and sentiment barriers to the adoption of the IoT.

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“…Some works do not try to minimize the energy consumption but the energy cost. Li et al [26] present a scheduling approach to minimize the cost of the energy consumed while guaranteeing security-related QoS. The security guarantee is obtained by adding security services at runtime according to the level of SLA of each task.…”

Section: Related Workmentioning

confidence: 99%

“…Our approach differs from most of the energy-efficient schedulers as the first goal is not to reduce to total energy consumed, but the non-renewable energy consumed. Therefore, it has more similarities with approaches such as [26], which tries to minimize the energy cost while having dynamic electricity pricing.…”

Section: Related Workmentioning

confidence: 99%