Securing Real-Time Internet-of-Things

Modern embedded and cyber-physical systems are ubiquitous. A large number of critical cyber-physical systems have real-time requirements (e.g., avionics, automobiles, power grids, manufacturing systems, industrial control systems, etc.). Recent developments and new functionality requires real-time embedded devices to be connected to the Internet. This gives rise to the real-time Internet-of-things (RT-IoT) that promises a better user experience through stronger connectivity and efficient use of next-generation embedded devices. However RT- IoT are also increasingly becoming targets for cyber-attacks which is exacerbated by this increased connectivity. This paper gives an introduction to RT-IoT systems, an outlook of current approaches and possible research challenges towards secure RT- IoT frameworks

Overhead Based Cluster Scheduling of Mixed Criticality Systems on Multicore Platform

The cluster-based technique is gaining focus for scheduling tasks of mixed-criticality (MC) real-time multicore systems. In this technique, the cores of the MC system are distributed in groups known as clusters. When all cores are distributed in clusters, the tasks are partitioned into clusters, which are scheduled on the cores within each cluster using a global approach. In this study, a cluster-based technique is adopted for scheduling tasks of real-time mixed-criticality systems (MCS). The Decreasing Criticality Decreasing Utilization with the worst-fit (DCDU-WF) technique is used for partitioning of tasks to clusters, whereas a novel mixed-criticality cluster-based boundary fair (MC-Bfair) scheduling approach is used for scheduling tasks on cores within clusters. The MC-Bfair scheduling algorithm reduces the number context switches and migration of tasks, which minimizes the overhead of mixed-criticality tasks. The migration and context switch overhead time is added at the time of each migration and context switch respectively for a task. In low critical mode, the low mode context switch and migration overhead time is added to task execution time, while the high mode overhead time of migration and context switch is added to the execution time of a task in high critical mode. The results obtained from experiments show the better schedulablity performance of proposed cluster-based technique as compared to cluster-based fixed priority (CB-FP), MC-EKG-VD-1, global and partitioned scheduling techniques e.g., for target utilization U=0.6, the proposed technique schedule 66.7% task sets while MC-EKG-VD-1, CB-FP, partitioned and global techniques schedule 50%, 33.3%, 16.7% and 0% task sets respectively

Partitioned EDF Scheduling in Multicore systems with Quality of Service constraints

International audienceIn this paper we study the partitioned EDF scheduling in a homogeneous multiprocessor environment with Quality of Service (QoS) constraints. The system considered here is a real-time multiprocessor system assumed to be powered by rechargeable batteries. We address the issue of how to best partition a set of firm real-time tasks that can occasionally skip one instance according to a predefined QoS threshold. The main goal is to minimize the energy consumption of the system while offering solutions with respect to transient energy starvation situations the system can experiment. The contribution of the paper is twofold. First, we present a schedulability analysis of firm multiprocessor task sets under QoS constraints. Second we propose new partitionning heuristics integrating skips. The evaluation is conducted from several points of view (minimization of the total processor number, maximization of the spare capacity on each processor)

Thermal aware task assignment for multicore processors using genetic algorithm

Microprocessor power and thermal density are increasing exponentially. The reliability of the processor declined, cooling costs rose, and the processor's lifespan was shortened due to an overheated processor and poor thermal management like thermally unbalanced processors. Thus, the thermal management and balancing of multi-core processors are extremely crucial. This work mostly focuses on a compact temperature model of multicore processors. In this paper, a novel task assignment is proposed using a genetic algorithm to maintain the thermal balance of the cores, by considering the energy expended by each task that the core performs. And expecting the cores’ temperature using the hotspot simulator. The algorithm assigns tasks to the processors depending on the task parameters and current cores’ temperature in such a way that none of the tasks’ deadlines are lost for the earliest deadline first (EDF) scheduling algorithm. The mathematical model was derived, and the simulation results showed that the highest temperature difference between the cores is 8 °C for approximately 14 seconds of simulation. These results validate the effectiveness of the proposed algorithm in managing the hotspot and reducing both temperature and energy consumption in multicore processors

Energy-Efficient Scheduling for Homogeneous Multiprocessor Systems

We present a number of novel algorithms, based on mathematical optimization formulations, in order to solve a homogeneous multiprocessor scheduling problem, while minimizing the total energy consumption. In particular, for a system with a discrete speed set, we propose solving a tractable linear program. Our formulations are based on a fluid model and a global scheduling scheme, i.e. tasks are allowed to migrate between processors. The new methods are compared with three global energy/feasibility optimal workload allocation formulations. Simulation results illustrate that our methods achieve both feasibility and energy optimality and outperform existing methods for constrained deadline tasksets. Specifically, the results provided by our algorithm can achieve up to an 80% saving compared to an algorithm without a frequency scaling scheme and up to 70% saving compared to a constant frequency scaling scheme for some simulated tasksets. Another benefit is that our algorithms can solve the scheduling problem in one step instead of using a recursive scheme. Moreover, our formulations can solve a more general class of scheduling problems, i.e. any periodic real-time taskset with arbitrary deadline. Lastly, our algorithms can be applied to both online and offline scheduling schemes.Comment: Corrected typos: definition of J_i in Section 2.1; (3b)-(3c); definition of \Phi_A and \Phi_D in paragraph after (6b). Previous equations were correct only for special case of p_i=d_