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 correspondingly 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 article, 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 DAG-structured application benchmarks. Our performance evaluation results demonstrate that SHIELD significantly outperforms two greedy baseline strategies SHIELDb in terms of solution generation times (i.e., runtimes) 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.

Many of today’s complex cyber–physical systems (CPSs) are represented as a set of independent co-executing real-time control applications, where each such application is represented as a precedence-constrained task graph. The applications execute in infinite loops, periodically acquiring data from the environment through sensors at a particular frequency, processing the same, and then producing processed data via actuators. These CPSs often execute under stringent resource constraints (such as limited energy budgets) in distributed networked environments and may be heterogeneous to be able to satisfactorily meet stipulated performance specifications. This work presents a list-based energy-aware scheduler called DVFS-enabled periodic multi-DAG real-time scheduler for heterogeneous systems (DPMRS) for a set of real-time control applications co-executing in a heterogeneous distributed environment.DPMRSintroduces a novel approach for the integrated behavioral representation of a set of co-executing real-time DAG-structured applications. Each task in this integrated representation is then scheduled by determining its relative execution start time on a particular processor, which operates at an appropriately chosen frequency when the task runs on this processor. The overall objective ofDPMRSis to minimize aggregate energy consumed in the execution of all tasks. The efficacy of the proposed scheduler has been exhibited through extensive simulation experiments using benchmark task graphs from different application domains. Additionally, a case study on automotive control systems has been included to show the applicability of the proposed work 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.

Scheduling real-time applications modelled as directed acyclic graphs on heterogeneous distributed platforms is known to be a challenging as well as a computationally demanding problem. This article deals with the design of an efficient scheduler for executing a real-time task graph on a distributed platform consisting of a set of fully connected heterogeneous processors. The objective of the scheduling strategy is to minimize ageneric penalty functionwhich can be amicably adopted toward its deployment in various application domains such as real-time embedded systems, cloud/fog computing, industrial automation and IoTs, smart grids, automotive and avionic systems, etc. We have first encoded the problem as a constraint satisfaction problem and then developed an efficient list-based heuristic scheduling algorithm calledPenalty-aware REal-time Scheduler for Task graphs on heterOgeneous platforms(PRESTO), to generate a minimal penalty deadline-meeting static schedule. The generic efficacy ofPRESTOis exhibited through extensive simulation-based experiments using standard benchmark task graphs. The practical applicability ofPRESTOin diverse scenarios have further been exhibited by using the scheme in two different real-world case studies, the first of which relates to automotive embedded systems, while the second is in the domain of fog computing.

Continuous demands for higher performance and reliability within stringent resource budgets is driving a shift from homogeneous to heterogeneous processing platforms for the implementation of today’s cyber-physical systems (CPSs). These CPSs are typically represented as Directed-acyclic Task Graph (DTG) due to the complex interactions between their functional components that are often distributed in nature. In this article, we consider the problem of scheduling a real-time application modelled as a single DTG, where tasks may have multiple implementations designated as quality-levels, with higher quality-levels producing more accurate results and contributing to higher rewards/Quality-of-Service for the system. First, we introduce an optimal solution using Integer Linear Programming (ILP) for a DTG with multiple quality-levels, to be executed on a heterogeneous distributed platform . However, this ILP-based optimal solution exhibits high computational complexity and does not scale for moderately large problem sizes. Hence, we propose two low-overhead heuristic algorithms called Global Slack Aware Quality-level Allocator ( G-SLAQA ) and Total Slack Aware Quality-level Allocator ( T-SLAQA ), which are able to produce satisfactorily efficient as well as fast solutions within a reasonable time. G-SLAQA , the baseline heuristic, is greedier and faster than its counter-part T-SLAQA , whose performance is at least as efficient as G-SLAQA . The efficiency of all the proposed schemes have been extensively evaluated through simulation-based experiments using benchmark and randomly generated DTGs. Through the case study of a real-world automotive traction controller , we generate schedules using our proposed schemes to demonstrate their practical applicability.

The problem of scheduling Directed Acyclic Graphs in order to minimizemakespan(schedule length), is known to be a challenging and computationally hard problem. Therefore, researchers have endeavored towards the design of various heuristic solution generation techniques both for homogeneous as well as heterogeneous computing platforms. This work first presentsHMDS-Bl, a list-based heuristicmakespanminimization algorithm for task graphs on fully connected heterogeneous platforms. Subsequently,HMDS-Blhas been enhanced by empowering it with a low-overhead depth-first branch and bound based search approach, resulting in a new algorithm calledHMDS.HMDShas been equipped with a set of novel tunable pruning mechanisms, which allow the designer to obtain a judicious balance between performance (makespan) and solution generation times, depending on the specific scenario at hand. Experimental analyses using randomly generated DAGs as well as benchmark task graphs, have shown thatHMDSis able to comprehensively outperform state-of-the-art algorithms such asHEFT,PEFT,PPTS, etc., in terms of archivedmakespanswhile incurring bounded additional computation time overhead.