A survey and experimental analysis of checkpointing techniques for energy harvesting devices

Singla, Priyanka; Sarangi, Smruti R.

doi:10.1016/j.sysarc.2022.102464

Cited by 11 publications

(6 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Checkpointing: The checkpoint technique of failure handling was used in the experiment to handle the transient failure of an application during runtime as a baseline approach 29,30 …”

Section: Methodsmentioning

confidence: 99%

“…The study introduces the fully polynomial approximate time scheme (FPTAS) to solve the knapsack task assignment problem. It accepts input as an exponential size and ensures a failure or success process 29 . It is essential to make the solution based on the decision knapsack problem under the polynomial time when the exponential log's input size is measured.…”

Section: Proposed Schemesmentioning

confidence: 99%

“…It accepts input as an exponential size and ensures a failure or success process. 29 It is essential to make the solution based on the decision knapsack problem under the polynomial time when the exponential log's input size is measured. There is a simplistic brute-force resolution to the knapsack problem.…”

Section: Lata Ptasmentioning

confidence: 99%

“…Checkpointing: The checkpoint technique of failure handling was used in the experiment to handle the transient failure of an application during runtime as a baseline approach. 29,30 The exploited existing open-source architecture of the respective studies is defined as follows.…”

Section: Baseline Approachesmentioning

confidence: 99%

See 3 more Smart Citations

Microservices architectural based secure and failure aware task assignment schemes in fog‐cloud assisted Internet of things

Lakhan

Grønli

2022

Int J of Intelligent Sys

View full text Add to dashboard Cite

The Internet of Things (IoT) paradigm has applications in many domains and is growing these days progressively. The applications are e-business, e-healthcare, and e-transportation. Recently, the container microservices-based Mobile Cloud Computing (MCC) has gained popularity, a lightweight framework compared to the monolithic virtual machine-based system. MCC combines fog nodes or cloud nodes with a base station to run the applications. However, storing the sensitive data of IoT applications on the untrusted nodes and failure of services are critical challenges in the existing architecture. This study proposes a novel microservices-based by combining fog and cloud services with efficient schemes. The first scheme is the Latency Aware Task Assignment Algorithm, which determines the optimal assignment of tasks to minimize the makespan of all applications. The second scheme is Fully Homomorphism Encryption, which ensures data security before an offload to any external assistance for execution. The final one is the Failure Aware, which handles any transient failure during application execution in the architecture. The experimental results show that the recommended architecture improved resource utilization, and the proposed schemes satisfied the

show abstract

“…Checkpointing: The checkpoint technique of failure handling was used in the experiment to handle the transient failure of an application during runtime as a baseline approach 29,30 …”

Section: Methodsmentioning

confidence: 99%

Section: Proposed Schemesmentioning

confidence: 99%

Section: Lata Ptasmentioning

confidence: 99%

Section: Baseline Approachesmentioning

confidence: 99%

See 2 more Smart Citations

Microservices architectural based secure and failure aware task assignment schemes in fog‐cloud assisted Internet of things

Lakhan

Grønli

2022

Int J of Intelligent Sys

View full text Add to dashboard Cite

show abstract

“…When program execution reaches a checkpoint, the system states will be copied from the volatile main memory to the NVM, where the program can be resumed. This technique was widely used in intermittent computing [3]- [13], [60]. The checkpointing technique is program-friendly since it typically does not require the programmer to specify the specific checkpointed data.…”

Section: B Intermittent Computingmentioning

confidence: 99%

Deep Learning on Energy Harvesting IoT Devices: Survey and Future Challenges

2022

IEEE Access

View full text Add to dashboard Cite

Internet-of-Things (IoT) devices are becoming both intelligent and green. On the one hand, Deep Neural Network (DNN) compression techniques make it possible to run deep learning applications on devices equipped with low-end microcontrollers (MCUs). By performing deep learning on IoT devices, in-situ decision-making can be made, which can improve the responsiveness of such devices to the environment and reduce data uploading to edge servers or clouds to save valuable network bandwidth. On the other hand, many IoT devices in the future will be powered by energy harvesters instead of batteries to reduce environmental pollution and achieve permanent service free of battery maintenance. As the energy output of energy harvesters is tiny and unstable, energy harvesting IoT (EH-IoT) devices will experience frequent power failures during their execution, making the software task hard to progress. The deep learning tasks running on such devices must face this challenge and, at the same time, ensure satisfactory execution efficiency. We believe deploying deep learning on EH-IoT devices that execute intermittently will be a challenging yet promising research direction. To motivate research in this direction, this paper summarizes existing solutions and provides an in-depth discussion of future challenges that deserve further investigation. With IoT devices becoming more intelligent and green, DNN inference on EH-IoT devices will generate a much more significant impact in the future in academia and industry.

show abstract