Abstract
Hardware Trojans can compromise System-on-Chip (SoC) performance. Protection schemes implemented to combat these threats cannot guarantee 100% detection rate and may also introduce performance overhead. This paper defines the risk of running a job on an SoC as a function of the misdetection rate of the hardware Trojan detection methods implemented on the cores in the SoC. Given the user-defined deadlines of each job, our goal is to minimize the job-level risk as well as the deadline violation rate for both static and dynamic scheduling scenarios. We assume that there is no relationship between the execution time and risk of a task executed on a core. Our risk-aware scheduling algorithm first calculates the probability of possible task allocations and then uses it to derive the task-level deadlines. Each task is then allocated to the core with minimum risk that satisfies the task-level deadline. In addition, in dynamic scheduling, where multiple jobs are injected randomly, we propose to explicitly operate with a reduced virtual deadline to avoid possible future deadline violations. Simulations on randomly generated graphs show that our static scheduler has no deadline violations and achieves 5.1%–17.2% lower job-level risk than the popular Earliest Time First (ETF) algorithm when the deadline constraint is 1.2×–3.0× the makespan of ETF. In the dynamic case, the proposed algorithm achieves a violation rate comparable to that of Earliest Deadline First (EDF), an algorithm optimized for dynamic scenarios. Even when the injection rate is high, it outperforms EDF with 8.4%–10% lower risk when the deadline is 1.5×–3.0× the makespan of ETF.
- [1] . 2016. Low-time complexity budget–deadline constrained workflow scheduling on heterogeneous resources. Future Generation Computer Systems 55 (2016), 29–40. Google ScholarDigital Library
- [2] . 2019. Dynamic multi-workflow scheduling: A deadline and cost-aware approach for commercial clouds. Future Generation Computer Systems 100 (2019), 98–108.Google ScholarDigital Library
- [3] . 2020. DS3: A system-level domain-specific System-on-Chip simulation framework. IEEE Trans. Comput. 69, 8 (2020), 1248–1262.Google Scholar
- [4] . 2014. Hardware Trojan attacks: Threat analysis and countermeasures. Proc. IEEE 102, 8 (2014), 1229–1247.Google ScholarCross Ref
- [5] . 2009. OS support for detecting Trojan circuit attacks. In 2009 IEEE International Workshop on Hardware-Oriented Security and Trust. IEEE, 100–103. Google ScholarDigital Library
- [6] . 2005. Task scheduling strategies for workflow-based applications in grids. In CCGrid 2005. IEEE International Symposium on Cluster Computing and the Grid, 2005., Vol. 2. IEEE, 759–767. Google ScholarDigital Library
- [7] . 2019. A deadline-constrained scheduling algorithm for scientific workflows in clouds. In 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, 98–105.Google ScholarCross Ref
- [8] . [n.d.]. Hardware Trojan: Threats and emerging solutions. In 2009 IEEE International High Level Design Validation and Test Workshop. 166–171.Google Scholar
- [9] . 2017. A flexible online checking technique to enhance hardware Trojan horse detectability by reliability analysis. IEEE Transactions on Emerging Topics in Computing 5, 2 (2017), 260–270.Google ScholarCross Ref
- [10] . 2009. V12. 1: User’s manual for CPLEX. International Business Machines Corporation 46, 53 (2009), 157.Google Scholar
- [11] . 2018. Leveraging computational reuse for cost-and QoS-efficient task scheduling in clouds. In International Conference on Service-Oriented Computing. Springer, 828–836.Google ScholarDigital Library
- [12] . 2020. IPPTS: An efficient algorithm for scientific workflow scheduling in heterogeneous computing systems. IEEE Transactions on Parallel and Distributed Systems 32, 5 (2020), 1057–1071.Google ScholarDigital Library
- [13] . 2020. A decision theoretic approach for waveform design in joint radar communications applications. In 2020 54th Asilomar Conference on Signals, Systems, and Computers. IEEE, 6–11.Google ScholarCross Ref
- [14] . 2008. Fundamentals of Queueing Theory. John Wiley & Sons. Google ScholarDigital Library
- [15] . 2019. Energy-efficient real-time scheduling of DAGs on clustered multi-core platforms. In 2019 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS). IEEE, 156–168.Google ScholarCross Ref
- [16] . 2018. Hardware Trojan attacks in embedded memory. In 2018 IEEE 36th VLSI Test Symposium (VTS). IEEE, 1–6.Google Scholar
- [17] . 2011. Analysis and optimization of fault-tolerant task scheduling on multiprocessor embedded systems. In Proceedings of the Seventh IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis. 247–256. Google ScholarDigital Library
- [18] . 2021. A DVFS-weakly-dependent energy-efficient scheduling approach for deadline-constrained parallel applications on heterogeneous systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2021).
DOI: DOI: https://doi.org/10.1109/tcad.2021.3049688Google ScholarCross Ref - [19] . 2017. Design optimization of secure message communication for energy-constrained distributed real-time systems. J. Parallel and Distrib. Comput. 100 (2017), 1–15. Google ScholarDigital Library
- [20] . 2014. Real-time trust evaluation in integrated circuits. In 2014 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 1–6. Google ScholarDigital Library
- [21] . 2005. Non-preemptive earliest-deadline-first scheduling policy: A performance study. In 13th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, (Sep. 27). IEEE, 201–208. Google ScholarDigital Library
- [22] . 2011. Reconfiguration-aware real-time scheduling under QoS constraint. In 16th Asia and South Pacific Design Automation Conference (ASP-DAC’11). IEEE, 141–146. Google ScholarDigital Library
- [23] . 2020. Runtime task scheduling using imitation learning for heterogeneous many-core systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 39, 11 (2020), 4064–4077.Google ScholarCross Ref
- [24] . 2016. CVSS: A cost-efficient and QoS-aware video streaming using cloud services. In 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). IEEE, 106–115. Google ScholarDigital Library
- [25] . 2017. Cost-efficient and robust on-demand video transcoding using heterogeneous cloud services. IEEE Transactions on Parallel and Distributed Systems 29, 3 (2017), 556–571.Google ScholarCross Ref
- [26] . 2014. Shielding heterogeneous MPSoCs from untrustworthy 3PIPs through security-driven task scheduling. IEEE Transactions on Emerging Topics in Computing 2, 4 (2014), 461–472.Google ScholarCross Ref
- [27] . 2018. Sustainable service allocation using a metaheuristic technique in a fog server for industrial applications. IEEE Transactions on Industrial Informatics 14, 10 (2018), 4497–4506.Google ScholarCross Ref
- [28] . 2014. Novel techniques for high-sensitivity hardware Trojan detection using thermal and power maps. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 33, 12 (2014), 1792–1805.Google ScholarCross Ref
- [29] . 2016. Energy efficiency for clustered heterogeneous multicores. IEEE Transactions on Parallel and Distributed Systems 28, 5 (2016), 1315–1330. Google ScholarDigital Library
- [30] . 2020. A comparative analysis of resource allocation schemes for real-time services in high-performance computing systems. International Journal of Distributed Sensor Networks 16, 8 (2020), 1550147720932750.Google ScholarCross Ref
- [31] . 2006. Handbook of Constraint Programming. Elsevier. Google ScholarDigital Library
- [32] . 2018. PL-DVFS: Combining power-aware list-based scheduling algorithm with DVFS technique for real-time tasks in cloud computing. The Journal of Supercomputing 74, 10 (2018), 5578–5600. Google ScholarDigital Library
- [33] . 2020. Deadline constrained energy-efficient workflow scheduling heuristic for cloud. In Proceedings of International Conference on IoT Inclusive Life (ICIIL’19), NITTTR Chandigarh, India. Springer, Singapore, 365–382.Google ScholarCross Ref
- [34] . 2019. A learning automata-based scheduling for deadline sensitive task in the cloud. IEEE Transactions on Services Computing (2019).
DOI: DOI: https://doi.org/10.1109/TSC.2019.2906870Google Scholar - [35] . 2020. HiLITE: Hierarchical and lightweight imitation learning for power management of embedded SoCs. IEEE Computer Architecture Letters 19, 1 (2020), 63–67.Google ScholarCross Ref
- [36] . 2019. 2smart: A two-stage machine learning-based approach for run-time specialized hardware-assisted malware detection. In 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 728–733.Google ScholarCross Ref
- [37] . 2010. Efficient scheduling algorithms for real-time distributed systems. In 2010 First International Conference On Parallel, Distributed and Grid Computing (PDGC’10), (Oct. 28). IEEE, 44–48.Google ScholarCross Ref
- [38] . 2017. Benchmarking of hardware Trojans and maliciously affected circuits. Journal of Hardware and Systems Security 1, 1 (2017), 85–102.Google ScholarCross Ref
- [39] . 2015. A cost-effective and QoS-aware approach to scheduling real-time workflow applications in PaaS and SaaS clouds. In 2015 3rd International Conference on Future Internet of Things and Cloud. IEEE, 231–239. Google ScholarDigital Library
- [40] . 2016. Scheduling different types of applications in a SaaS cloud. In Proceedings of the 6th International Symposium on Business Modeling and Software Design (BMSD’16). 144–151.Google ScholarCross Ref
- [41] . 2018. Energy-aware scheduling of real-time workflow applications in clouds utilizing DVFS and approximate computations. In 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud). IEEE, 33–40.Google ScholarCross Ref
- [42] . 2019. Designing energy-efficient MPSoC with untrustworthy 3PIP cores. IEEE Transactions on Parallel and Distributed Systems 31, 1 (2019), 51–63.Google ScholarDigital Library
- [43] . 2010. A survey of hardware Trojan taxonomy and detection. IEEE Design & Test of Computers 27, 1 (2010), 10–25. Google ScholarDigital Library
- [44] . 2002. Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems 13, 3 (2002), 260–274. Google ScholarDigital Library
- [45] . 2020. Runtime identification of hardware Trojans by feature analysis on gate-level unstructured data and anomaly detection. ACM Transactions on Design Automation of Electronic Systems (TODAES) 25, 4 (2020), 1–23. Google ScholarDigital Library
- [46] . 2017. Deadline-constrained cost optimization approaches for workflow scheduling in clouds. IEEE Transactions on Parallel and Distributed Systems 28, 12 (2017), 3401–3412.Google ScholarDigital Library
- [47] . 2015. TPAD: Hardware Trojan prevention and detection for trusted integrated circuits. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 35, 4 (2015), 521–534.Google ScholarDigital Library
- [48] . 2016. Hardware Trojans: Lessons learned after one decade of research. ACM Transactions on Design Automation of Electronic Systems (TODAES) 22, 1 (2016), 1–23. Google ScholarDigital Library
- [49] . 2010. A novel security-driven scheduling algorithm for precedence-constrained tasks in heterogeneous distributed systems. IEEE Trans. Comput. 60, 7 (2010), 1017–1029. Google ScholarDigital Library
- [50] . 2017. Energy-efficient fault-tolerant scheduling of reliable parallel applications on heterogeneous distributed embedded systems. IEEE Transactions on Sustainable Computing 3, 3 (2017), 167–181.Google ScholarCross Ref
- [51] . 2017. Energy-efficient scheduling algorithms for real-time parallel applications on heterogeneous distributed embedded systems. IEEE Transactions on Parallel and Distributed Systems 28, 12 (2017), 3426–3442.Google ScholarDigital Library
- [52] . 2013. Multi-objective approach for energy-aware workflow scheduling in cloud computing environments. The Scientific World Journal 2013.
DOI: DOI: https://doi.org/10.1155/2013/350934Google ScholarCross Ref - [53] . 2019. Security-critical energy-aware task scheduling for heterogeneous real-time MPSoCs in IoT. IEEE Transactions on Services Computing 13, 4 (2019), 745–758.Google ScholarCross Ref
Index Terms
- Probabilistic Risk-Aware Scheduling with Deadline Constraint for Heterogeneous SoCs
Recommendations
A GEP-based reactive scheduling policies constructing approach for dynamic flexible job shop scheduling problem with job release dates
Flexible job shop scheduling problem (FJSSP) is generalization of job shop scheduling problem (JSSP), in which an operation may be processed on more than one machine each of which has the same function. Most previous researches on FJSSP assumed that all ...
A Novel Heterogeneous Scheduling Algorithm with Improved Task Priority
HPCC-CSS-ICESS '15: Proceedings of the 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conf on Embedded Software and SystemsEfficient application scheduling algorithms are important to obtain high performance in heterogeneous computing systems. However, most of current algorithms are of low efficiency in scheduling. Aiming at this problem, we propose a heterogeneous ...
Scheduling for heterogeneous Systems using constrained critical paths
A complex computing problem may be efficiently solved on a system with multiple processing elements by dividing its implementation code into several tasks or modules that execute in parallel. The modules may then be assigned to and scheduled on the ...
Comments