Nowadays, many embedded systems such as mobiles and laptops to satellites and robotic systems, are often driven by limited energy sources like batteries. Hence, these devices are not only judged by their real-time and functional performance but also by their efficiencies in terms of energy management. Energy minimization is one of the primary design requirements for distributed embedded systems. The growing importance of complex applications in the distributed system introduces significant challenges in reducing energy consumption. This work addresses the problem of scheduling a real-time application abstracted as a directed acyclic graph (DAG), on a Dynamic Voltage and Frequency Scaling (DVFS) enabled uniform multiprocessor system, by proposing an efficient heuristic strategy called Energy-efficient Real-time DAG Scheduler (ERS). ERS effectively selects the appropriate processing frequency for each task-to-processor pair in the system such that overall energy saving is maximized while satisfying constraints related to resource, task precedence and deadline. We have evaluated the performance of the proposed framework using real-world benchmark applications. Obtained results reveal that ERS is able to deliver better performance in terms of energy savings than state-of-the-art works such as GSPM, SSPM, and PSPM.

Many control applications in real-time cyber-physical systems are represented as Directed Acyclic Graphs (DAGs) due to complex interactions among their functional components, and executed on distributed heterogeneous platforms. Data communication between dependent task nodes running on different processing elements are often realized through message transmission over a public network, and are hence susceptible to multiple security threats such as snooping, alteration and spoofing. Several alternative security protocols having varying security strengths and associated implementation overheads are available in the market, for incorporating confidentiality, integrity and authentication on the transmitted messages. While message size and conceptually its associated transmission overheads may be marginally increased due to the assignment of security protocols, significant computation overheads must be incurred for securing the message at the location of its source task node and for unlocking security/message extraction at the destination. Obtained security strengths and associated computation overheads vary depending on the set of protocols chosen for a given message from an available pool of protocols. Given lower bounds on the security demands of an application’s messages, selecting the appropriate protocols for each message such that a system’s overall security is maximized while satisfying constraints related to the resource, task precedence and deadline, is a challenging and computationally hard problem. In this paper, we propose an efficient heuristic strategy called SHIELD for security-aware real-time scheduling of DAG-structured applications to be executed on distributed heterogeneous systems. The efficacy of the proposed scheduler is exhibited through extensive simulation-based experiments using two DAGstructured application benchmarks. Our performance evaluation results demonstrate that SHIELD significantly outperforms two greedy baseline strategies SHIELDb in terms of solution generation times (i.e., run-times) and SHIELDf in terms of achieved security utility. Additionally, a case study on the Traction Control application in automotive systems has been included to exhibit the applicability of SHIELD in real-world settings.

To meet application-specific performance demands, recent embedded platforms often involve the use of intricate micro-architectural designs and very small feature sizes leading to complex chips with multi-million gates. Such ultra-high gate densities often make these chips susceptible to inappropriate surges in core temperatures. Temperature surges above a specific threshold may throttle processor performance, enhance cooling costs, and reduce processor life expectancy. This work proposes a generic temperature management strategy that can be easily employed to adapt existing state-of-the-art task graph schedulers so that schedules generated by them never violate stipulated thermal bounds. The overall temperature-aware task graph scheduling problem has first been formally modeled as a constraint optimization formulation whose solution is shown to be prohibitively expensive in terms of computational overheads. Based on insights obtained through the formal model, a new fast and efficient heuristic algorithm called TMDS has been designed. Experimental evaluation over diverse test case scenarios shows that TMDS is able to deliver lower schedule lengths compared to the temperature-aware versions of four prominent makespan minimizing algorithms, namely, HEFT, PEFT, PPTS, and PSLS. Additionally, a case study with an adaptive cruise controller in automotive systems has been included to exhibit the applicability of TMDS in real-world settings.