Real-time Scheduling

In this paper, we proposed a Non-preemptive Utility Accrual Scheduling (or NUAS) algorithm that enhanced the existing General Utility Scheduling (or GUS) proposed by Peng Li [1]. These algorithms were designed for adaptive real time system environment where undesirable effects such as overloads and deadline misses are tolerable and do not have great consequences to the system. We consider the independent task models that are subject to deadline constraints specified using step Time/Utility Functions (or TUFs). The main idea of our proposed algorithm is to reduce the unnecessary abortions by eliminating the greedy scheduling decision identified in GUS. We consider the scheduling objective of maximizing the accrued utility by completion of all tasks. Simulation results reveal that the proposed NUAS algorithm outperforms GUS algorithm with higher accrued utility in its entire load range.

In this paper, we propose a preemptive utility accrual scheduling (or PUAS) algorithm as an enhancement to General Utility Scheduling ( or GUS) algorithm proposed by Peng Li . These scheduling algorithms are designed for adaptive real time system environment where undesirable effects such as overload and deadline misses are tolerable. We consider independent task models that are subject to deadline constraints specified using step time/utility functions (or TUFs). The basic idea of our algorithm is to reduce the number of unnecessary abortion that occurs in GUS by preemption instead of abortion. We consider the scheduling objective of maximizing the utility that is accrued by the completion of all tasks. Simulation results revealed that the proposed algorithm outperforms GUS algorithm. By reducing the total number of task aborted together with lower abortion ratio, this in effect produced a higher utility and reduced the average response time, making it more suitable and efficient in time-critical application domain.

Heuristic algorithms have enjoyed increasing interests and success in the context of Utility Accrual (UA) scheduling. However, few analytical results, such as bounds on task-level and system-level accrued utilities are known. In this paper, we propose the S-UA algorithm that can provide probabilistic bounds on tasklevel accrued utilities. Lower bound on system-level accrued utility ratio (AUR) is also derived and maximized by S-UA.

In this paper, we proposed two recovery solutions over the existing error-free utility accrual scheduling algorithm known as General Utility Accrual Scheduling algorithm (or GUS) (Peng Li, 2004). A robust fault recovery algorithm called Backward Recovery GUS (or BRGUS) works by adapting the time redundancy model i.e., by re-executing the affected task after its transient error period is over. The BRGUS is compared with a less complicated recovery algorithm named as Abortion Recovery GUS (or ARGUS) that simply aborts all faulty tasks. Our main objectives are (1) to maximize the total accrued utility and (2) to ensure correctness of the executed tasks on best effort basis and achieve the fault free tasks as much as possible. Our simulation results reveal that BRGUS outperforms the ARGUS algorithm with higher accrued utility and less abortion ratio, making it more suitable and efficient in adaptive real time system.

We consider lock-free synchronization for dynamic embedded real-time systems that are subject to resource overloads and arbitrary activity arrivals. We model activity arrival behaviors using the unimodal arbitrary arrival model (or UAM). UAM embodies a stronger "adversary" than most traditional arrival models. We derive an upper bound on lock-free retries under the UAM with utility accrual scheduling-the first such result. We establish the tradeoffs between lock-free and lock-based sharing under UAM. These include conditions under which activities' accrued timeliness utility is greater under lock-free than lock-based, and the consequent lower and upper bound on the total accrued utility that is possible with lock-free and lock-based sharing. We confirm our analytical results with a POSIX RTOS implementation.

We extend Jensen's time/utility functions and utility accrual model with the concept of joint utility functions (or JUFs) that allow an activity's utility to be described as a function of the completion times of other activities and their progress. We also specify the concept of progressive utility that generalizes the previously studied imprecise computational model, by describing an activity's utility as a function of its progress. Given such an extended utility accrual model, we consider the scheduling criterion of maximizing the weighted sum of completion time, progressive, and joint utilities. We present an algorithm called the Combined Utility Accrual algorithm (or CUA) for this criterion. Experimental measurements with an implementation of CUA on a POSIX RTOS illustrate the effectiveness of JUFs in a class of applications of interest to us.

We extend time/utility functions and utility accrual model with the concept of joint utility functions (or JUFs) that allow an activity's utility to be described as a function of the completion times of other activities and their progress. We also specify the concept of progressive utility that generalizes the previously studied imprecise computational model, by describing an activity's utility as a function of its progress. Given such an extended utility accrual model, we consider the scheduling criterion of maximizing the weighted sum of completion time, progressive, and joint utilities. We present an algorithm called the Combined Utility Accrual algorithm (or CUA) for this criterion. Experimental measurements with an implementation of CUA on a POSIX RTOS illustrate the effectiveness of JUFs in a class of applications of interest to us.

We consider Real-Time CORBA 1.2's distributable threads (DTs), whose time constraints are specified using time/utility functions (TUFs), operating in legacy environments. In legacy environments, system node resources-both physical and logical-are shared among time-critical DTs and local applications that may also be time-critical. Hence, DTs that are scheduled using their propagated TUFs, as mandated by Real-Time CORBA 1.2's Case 2 approach, may suffer performance degradation, if a node utility accrual (UA) scheduler achieves higher locally accrued utility by giving higher eligibility to local threads than to DTs. To alleviate this, we consider decomposing TUFs of DTs into "sub-TUFs" for scheduling segments of DTs. We present decomposition techniques called U T , SCEQF , SCALL, OP T CON , and T U F S, which are specific to different classes of UA algorithms, such as those that use utility density, and those that use deadline as their key decision metric. Our experimental studies show that OP T CON and T U F S perform best for utility density-based UA algorithms, and SCEQF and SCALL perform best for deadline-based UA algorithms.

This paper summarizes a novel paradigm for expressing, enforcing, and formally reasoning about time-criticality of machine-to-machine resource management in battle management (BM) and C 2 systems. Such systems are largely dynamic and asynchronous, and have time frames of O(10-1) seconds and higher. Thus they fall into a neglected gap between traditional static periodic "real-time" systems, and traditional "any time" scheduling/planning systems (e.g., for logistics). The paradigm uses application-level QoS metrics (such as track quality, circular error probable, etc.) to derive utility functions for completing tasks, and then uses those utility functions for resource management. This paradigm has been successfully employed in several experimental BM/C 2 demonstration systems, and is the topic of ongoing research by industry and academia.

We present a CPU scheduling algorithm, called the Energy-Bounded Utility Accrual Algorithm (or EBUA). EBUA is a polynomial-time algorithm that satisfies bounds on system-level energy consumption and activities' accrued timeliness utility. We analytically establish several timeliness properties of EBUA. Our simulation experiments using AMD's (DVS-enabled) k6 processor model confirm the algorithm's effectiveness and superiority.

We present an energy-efficient real-time scheduling algorithm called EUA * , for the unimodal arbitrary arrival model (or UAM). UAM embodies a "stronger" adversary than most arrival models. The algorithm considers application activities that are subject to time/utility function time constraints, UAM, and the multi-criteria scheduling objective of probabilistically satisfying utility lower bounds, and maximizing system-level energy efficiency. Since the scheduling problem is intractable, EUA * allocates CPU cycles, scales clock frequency, and heuristically computes schedules using statistical estimates of cycle demands, in polynomial-time. We establish that EUA * achieves optimal timeliness during under-loads, and identify the conditions under which timeliness assurances hold. Our simulation experiments illustrate EUA * 's superiority.

We present a utility accrual real-time scheduling algorithm called CIC-VCUA, for tasks whose execution times are functions of their starting times (and potentially other factors). We model such variable execution times using variable cost functions (or VCFs). The algorithm considers application activities that are subject to time/utility function time constraints, execution times described using VCFs, and mutual exclusion constraints on concurrent sharing of non-CPU resources. We consider the twofold scheduling objective of (1) assure that the maximum interval between any two consecutive, successful completions of job instances in an activity must not exceed the activity period (an application-specific objective), and (2) maximizing the system's total accrued utility, while satisfying mutual exclusion resource constraints. Since the scheduling problem is intractable, CIC-VCUA is a polynomial-time heuristic algorithm. The algorithm statically computes worst-case task sojourn times, dynamically selects tasks for execution based on their potential utility density, and completes tasks at specific times. We establish that CIC-VCUA achieves optimal timeliness during under-loads, and tightly upper bounds inter-and intra-task completion times. Our simulation experiments confirm the algorithm's effectiveness and superiority.

We consider RealTime CORBA 2.0 (Dynamic Scheduling) distributable threads, whose time constraints are specified using time/utility functions (TUFs), operating in legacy environments. In legacy environments, system node resources-both physical (processor, disk, I/O, etc.) and logical (locks, etc.)-are shared among timecritical distributable threads and local applications that may or may not be timecritical. Thus, in such environments, distributable threads that are scheduled using their propagated TUFs and scheduling parameters, as mandated by RealTime CORBA 2.0's Case 2 approach, may suffer performance degradation, if a node scheduler can achieve higher local accrued utility by giving higher eligibility to local threads than to distributable threads. To alleviate this, we consider decomposing TUFs of distributable threads into "subTUFs" that are used for scheduling segments of distributable threads. We present methods for decomposing TUFs. Furthermore, we identify conditions under which TUF decomposition can alleviate perfor mance degradation. Our experimental results reveal that the most important factors that affect the performance of TUF decomposition include the properties of node scheduling algorithms, TUF shapes, task load, Global Slack F actor, local threads and resource dependencies, and that these factors interact.

We present an energy-efficient real-time scheduling algorithm called EUA * , for the unimodal arbitrary arrival model (or UAM). UAM embodies a "stronger" adversary than most arrival models. The algorithm considers application activities that are subject to time/utility function time constraints, UAM, and the multi-criteria scheduling objective of probabilistically satisfying utility lower bounds, and maximizing system-level energy efficiency. Since the scheduling problem is intractable, EUA * allocates CPU cycles, scales clock frequency, and heuristically computes schedules using statistical estimates of cycle demands, in polynomial-time. We establish that EUA * achieves optimal timeliness during under-loads, and identify the conditions under which timeliness assurances hold. Our simulation experiments illustrate EUA * 's superiority.

We present the first Utility Accrual (or UA) real-time scheduling algorithm for multiprocessors, called gMUA. The algorithm considers an application model where real-time activities are subject to time/utility function time constraints, variable execution time demands, and resource overloads where the total activity utilization demand exceeds the total capacity of all processors. We consider the scheduling objective of (1) probabilistically satisfying lower bounds on each activity's maximum utility and (2) maximizing the system-wide, total accrued utility. We establish several properties of gMUA including optimal total utility (for a special case), conditions under which individual activity utility lower bounds are satisfied, a lower bound on system-wide total accrued utility, and bounded sensitivity for assurances to variations in execution time demand estimates. Our simulation experiments validate our analytical results and confirm the algorithm's effectiveness and superiority.

Problem statement: This study proposed two utility accrual real time scheduling algorithms named as Preemptive Utility Accrual Scheduling (PUAS) and Non-preemptive Utility Accrual Scheduling (NUAS) algorithms. These algorithms addressed the unnecessary abortion problem that was identified in the existing algorithm known as General Utility Scheduling (GUS). It is observed that GUS is inefficient for independent task model because it simply aborts any task that currently executing a resource with lower utility when a new task with higher utility requests the resource. The scheduling optimality criteria are based on maximizing accrued utility accumulated from execution of all tasks in the system. These criteria are named as Utility Accrual (UA). The UA scheduling algorithms are design for adaptive real time system environment where deadline misses are tolerable and do not have great consequences to the system. Approach: We eliminated the scheduling decision to abort a task in GUS and proposed to preempt a task instead of being aborted if the task is preemptive able. We compared the performances of these algorithms by using discrete event simulation. Results: The proposed PUAS algorithm achieved the highest accrued utility for the entire load range. This is followed by the NUAS and GUS algorithms. Conclusion: Simulation results revealed that the proposed algorithms were more efficient than the existing algorithm, producing with higher accrued utility ratio and less abortion ratio making it more suitable and efficient for real time application domain.

We present an implementation of Real-Time CORBA's distributable threads (DTs) as a first-class, end-to-end realtime programming and scheduling abstraction in the Linux kernel. We use Ingo Molnar's PREEMPT RT kernel patch, which enables nearly complete kernel pre-emption, and add local (real-time) scheduling support to the Linux kernel, atop which we build DT scheduling support. We implement DTs using Linux's threading capabilities. Our implementation of a suite of independent and collaborative DT schedulers confirm the effectiveness of our implementation.

In this paper, we proposed two recovery solutions over the existing error-free utility accrual scheduling algorithm known as General Utility Accrual Scheduling algorithm (or GUS) (Peng Li, 2004). A robust fault recovery algorithm called Backward Recovery GUS (or BRGUS) works by adapting the time redundancy model i.e., by re-executing the affected task after its transient error period is over. The BRGUS is compared with a less complicated recovery algorithm named as Abortion Recovery GUS (or ARGUS) that simply aborts all faulty tasks. Our main objectives are (1) to maximize the total accrued utility and (2) to ensure correctness of the executed tasks on best effort basis and achieve the fault free tasks as much as possible. Our simulation results reveal that BRGUS outperforms the ARGUS algorithm with higher accrued utility and less abortion ratio, making it more suitable and efficient in adaptive real time system.

Predictable systems are systems in which correctness arguments consider both the appropriateness and the timeliness of delivered results. Such systems exhibit both statistical timing as well as bounded duration timing ex- pectations. Such systems require suitable architectural techniques that do not preclude meeting the timing ex- pectations. This paper provides a summary of some of these issues and the architectural concerns that surface when addressing timing constraints in performance critical systems. The currently well-known design meth- ods are examined for their suitability to describe and re- cord architectures with predictable performance. Rele- vant changes resulting in a suitable method for real-time object-oriented analysis to are suggested.

The NASA Jet Propulsion Laboratory (JPL) is developing the Mission Data System (MDS) for potential use in future space missions, where it is expected to reduce the complexity and effort required to produce mission and groundsupport software, while also enabling greater autonomy for remotely deployed systems, including future planetary rovers. The MDS software architecture includes two levels of processor scheduling: a high-level planner and a lowlevel execution manager. JPL and MITRE are investigating the use of Time-Utility Functions to manage low-level scheduling, since they promise to enable adaptive, shortterm processor scheduling based on mission utility. Recent development work on MDS has focused on organizing the software so that low-level processor scheduling will be most effective. This paper describes the major organizing principles that have shaped this work.

The goal of the Rialto project at Microsoft Research is to build a system architecture supporting coexisting independent real-time (and non-real-time) programs. Unlike traditional embedded-systems real-time environments, where timing and resource analysis among competing tasks can be done off-line, it is our goal to allow multiple independently authored real-time applications with varying timing and resource requirements to dynamically coexist and cooperate to share the limited physical resources available to them, as well as also coexisting with nonreal-time applications. This paper gives an overview of the Rialto real-time architecture as it is implemented today and reports on some of the early results obtained. In particular, it describes the use of time constraints, activities, CPU and other resource reservation, and the system resource planner, and how they work together to achieve our goal of providing a flexible, dynamic real-time computing environment. * Daniela RoÒu and Marcel-Ctlin RoÒu are Ph.D. students at the Georgia Institute of Technology.

It is quite possible that in a process set, process may be having earliest deadline but its performance contribution is low in process set. Scheduling such processes with highest priority does not carry any meaning. Majority of today’s commercial operating system schedule task based on a single parameter however recent research on flexible scheduling showed that a single parameter is not enough to expressall the application requirement .In order to provide effective support to QoS management ,an algorithm for offline scheduling of communicating tasks with precedence constraints suggested on uniprocessor Since in real time system processes are communicating to each other, if any message is failed then performance of executing successor process is reduced up to some extent .Contribution of each process in process network can be evaluated offline using multistate system (MSS) analysis. This paper suggests the policy to convert task precedence and communication constraints into pseudo d...

Abstract-Parallel Data processing has emerged to be one of the killer applications for Infrastructure-as-a-Service (IaaS) clouds. One of an IaaS cloud's key feature is the provisioning of compute resources on demand. The computer resources available in the ...

In this paper, we proposed two recovery solutions over the existing error-free utility accrual scheduling algorithm known as General Utility Accrual Scheduling algorithm (or GUS) (Peng Li, 2004). A robust fault recovery algorithm called Backward Recovery GUS (or BRGUS) works by adapting the time redundancy model i.e., by re-executing the affected task after its transient error period is over. The BRGUS is compared with a less complicated recovery algorithm named as Abortion Recovery GUS (or ARGUS) that simply aborts all faulty tasks. Our main objectives are (1) to maximize the total accrued utility and (2) to ensure correctness of the executed tasks on best effort basis and achieve the fault free tasks as much as possible. Our simulation results reveal that BRGUS outperforms the ARGUS algorithm with higher accrued utility and less abortion ratio, making it more suitable and efficient in adaptive real time system.

We argue that the key underpinning of the current stateof-the real-time practice-the priority artifact-and that of the current state-of-the real-time art-deadline-based timeliness optimality-are entirely inadequate for specifying timeliness objectives, for reasoning about timeliness behavior, and for performing resource management that can dependably satisfy timeliness objectives in many dynamic real-time systems. We argue that time/utility functions and the utility accrual scheduling paradigm provide a more generalized, adaptive, and flexible approach. Recent research in the utility accrual paradigm have significantly advanced the state-of-the-art of that paradigm. We survey these advances.

This paper summarizes a novel paradigm for expressing, enforcing, and formally reasoning about time-criticality of machine-to-machine resource management in battle management (BM) and C 2 systems. Such systems are largely dynamic and asynchronous, and have time frames of O(10-1) seconds and higher. Thus they fall into a neglected gap between traditional static periodic "real-time" systems, and traditional "any time" scheduling/planning systems (e.g., for logistics). The paradigm uses application-level QoS metrics (such as track quality, circular error probable, etc.) to derive utility functions for completing tasks, and then uses those utility functions for resource management. This paradigm has been successfully employed in several experimental BM/C 2 demonstration systems, and is the topic of ongoing research by industry and academia.

We present the first wait-free utility accrual (UA) real-time scheduling algorithms for embedded real-time systems. UA scheduling algorithms allow application activities to be subject to time/utility function (TUF) time constraints, and optimize criteria such as maximizing the sum of the activities' attained utilities. We present UA algorithms that use wait-free synchronization for mutually exclusively accessing shared data objects. We derive upper bounds on the maximum possible increase in activity utility with wait-free over their lock-based counterparts, while incurring the minimum possible additional space costs, and thereby establish the tradeoffs between wait-free and lock-based object sharing for UA scheduling. Our implementation measurements on a POSIX RTOS reveal that (during under-loads), the wait-free algorithms yield optimal utility for step TUFs and significantly higher utility (than lock-based) for non-step TUFs. Kopetz et al. present one of the earliest wait-free protocols. Chen et al. build upon [8] and present an efficient waitfree protocol in [2]. Huang et al. improve the time costs of Chen's protocol in [7]. Chen's protocol is further improved by Cho et al. to develop the space-optimal wait-free protocol for the single-writer/multiple-reader problem in [3].

We consider Real-Time CORBA 1.2 (Dynamic Scheduling) distributable threads operating in multihop networks. When distributable threads are subject to time/utility function-time constraints, and timeliness optimality criteria such as maximizing accrued system-wide utility is desired, utility accrual real-time channels must be established. Such channels transport messages that are generated as distributable threads transcend nodes, in a way that maximizes system-wide, message-level utility. We present 1) a localized utility accrual channel establishment algorithm called Localized Decision for Utility accrual Channel Establishment (or LocDUCE) and 2) a distributed utility accrual channel establishment algorithm called Global Decision for Utility accrual Channel Establishment (or GloDUCE). Since the channel establishment problem is N P-complete, LocDUCE and GloDUCE heuristically compute channels, with LocDUCE making decisions based on local information pertaining to the node and GloDUCE making global decisions. We simulate the performance of the algorithms and compare them with the Open Shortest Path First (OSPF) routing algorithm and the optimal algorithm. We also implement these algorithms in a prototype testbed and experimentally compare their performance with OSPF. Our simulation and experimental measurements reveal that GloDUCE and LocDUCE accrue significantly higher utility than OSPF and also perform close to the optimal for some cases. Furthermore, GloDUCE outperforms LocDUCE under high downstream traffic. Index Terms-Real-time systems, multihop networks, real-time channels, time/utility functions.

This paper considers object-based real-time embedded systems on MPSoCs. Objects provide system services to the real-time tasks. Each task is subject to a time/utility function (TUF) which determines the accrued utility of the task according to its completion time. One major problem in such systems is to place the objects on the processing elements (PEs) in the MPSoC so as to maximize the total accrued utility. In this regard, we propose a utility accrual object distribution (UAOD) algorithm consisting of two phases. In the first phase, the PEs are reserved for the most beneficial tasks in an offline manner. The reservation is constituted of some proposed methods for object placement, object replication, deadline decomposition, and deadline adaptation. For the objects which are not placed in the reservation, UAOD follows a load-balancing approach to place them on the PEs. As the second phase, UAOD performs an online scheduling over the tasks assigned to each PE in the offline object placement. An extension of this algorithm, namely UAOD * is also proposed for the situations where the exact task execution-times are not known a priori. Simulation results reveal that the total accrued utility is improved with the proposed algorithms comparing to the traditional object placement methods.

This paper presents some models and concepts for developing smart power grid control systems based on holonic concepts and the open standards IEC 61850, IEC 61499. Along with the proposed holonic models for different levels of control, we present a simple fault protection application illustrating how the IEC 61499 artifacts can be used for modeling and implementation of IEC 61850 compliant applications.

Imprecise computation model is used in dynamic scheduling algorithm having heuristic function to schedule task sets. A task is characterized by ready time, worst case computation time, deadline and resource requirements. A task failing to meet its deadline and resource requirements on time is split into mandatory part and optional part. These sub-tasks of a task can execute concurrently on multiple cores, thus achieving parallelization provided by the multi-core system. Mandatory part produces acceptable results while optional part refines the result further. To study the effectiveness of proposed scheduling algorithm, extensive simulation studies have been carried out. Performance of proposed scheduling algorithm is compared with myopic and improved myopic scheduling algorithm. The simulation studies shows that schedulability of task split myopic algorithm is always higher than myopic and improved myopic algorithm.

While most embedded systems are designed for real-time applications, they suffer from resource constraints. Many techniques have been proposed for real-time task scheduling to reduce energy consumption. A combination of Dynamic Voltage Scaling (DVS) and feedback scheduling can be used to scale dynamically the frequency by adjusting the operating voltage, and improve the run-time reliability of embedded systems. We present in this paper a novel hybrid contribution that handles real-time scheduling of embedded systems and low power consumption based on the combination of DVS and Neural Feedback Scheduling NFS with the priorityenergy earliest-deadline-first (PEDF) algorithm.

It is well known that in a firm real time system with a renewal arrival process, exponential service times and independent and identically distributed deadlines till the end of service of a job, the earliest deadline first (EDF) scheduling policy has smaller loss ratio (expected fraction of jobs, not completed) than any other service time independent scheduling policy, including the first come first served (FCFS). Various modifications to the EDF and FCFS policies have been proposed in the literature, with a view to improving performance. In this article, we compare the loss ratios of these two policies along with some of the said modifications, as well as their counterparts with deterministic deadlines. The results include some formal inequalities and some counterexamples to establish non-existence of an order. A few relations involving loss ratios are posed as conjectures, and simulation results in support of these are reported. These results lead to a complete picture of dominance and non-dominance relations between pairs of scheduling policies, in terms of loss ratios.

To solve the configuration issue when using International Electrotechnical Commission (IEC) 61850 for distributed intelligence in Distribution Automation Systems (DAS), this paper proposes the configuration solution in terms of semantic models and processing methods. Firstly, the special requirements of the DAS configuration are analyzed, consisting of the system boundary of a configuration project, the topology configuration for distributed applications, and the automatic identification of the Intelligent Electronic Devices (IED). The new models of Process, Line, and other elements are then presented based on the System Configuration Language (SCL) to describe the distribution network topology. The planned contents are allocated into a new format of the Configured IED Description (CID) file to realize the distributed applications. A register service is designed, which fulfills the automatic identification of IEDs when they are remotely placed into a DAS. The service checks the configuration status in real-time and automates the whole configuration engineering process. The case study shows that the proposed solution allows an IED to detect the real-time topology and re-establish the data flow configuration with peer IEDs independently from the master station; thus the distributed applications can be performed more autonomously and efficiently.

In distributed real-time systems, an application is often modeled as a set of real-time transactions, where each transaction is a chain of precedence-constrained tasks. Each task is statically allocated to a processor, and tasks allocated on the same processor are handled by a single-processor scheduling algorithm. Precedence constraints among tasks of the same transaction are modeled by properly assigning scheduling parameters as offsets, jitters and intermediate deadlines. In this paper we address the problem of schedulability analysis of distributed real-time transactions under the earliest deadline first scheduling algorithm. We propose a novel methodology that reduces the pessimism introduced by previous methods by explicitly taking into account the offsets of the tasks. Moreover, we extend the analysis to account for blocking time due to shared resources. In particular, we propose two kinds of schedulability tests, CDO-TO and MDO-TO, and show, with an extensive set of simulations, that they provides improved schedulability conditions with respect to classical algorithms. Finally, we apply the methodology to an important class of systems: heterogeneous multiprocessor systems, with a general purpose processor and one or more coprocessors (DSPs).

JM Zain et al. (Eds.): ICSECS 2011, Part II, CCIS 180, pp. 618–626, 2011. © Springer-Verlag Berlin Heidelberg 2011 ... A Discrete Event Simulation for Utility Accrual ... Idawaty Ahmad, Shamala Subramaniam, Mohamad Othman, and Zuriati Zulkarnain

978-1-4244-2328-6/08/$25.00 © 2008 IEEE Improving Utility Accrual Scheduling Algorithm for Adaptive Real-Time System Idawaty Ahmad, Mohamad Othman and Zuriati Zulkarnain Muhammad Fauzan Othman Faculty of Comp Sc and Info Tech, Universiti Putra Malaysia ...

Problem statement: This study proposed two utility accrual real time scheduling algorithms named as Preemptive Utility Accrual Scheduling (PUAS) and Non-preemptive Utility Accrual Scheduling (NUAS) algorithms. These algorithms addressed the unnecessary abortion problem that was identified in the existing algorithm known as Gener al Utility Scheduling (GUS). It is observed that GU S is inefficient for independent task model because

In this paper, we propose a preemptive utility accrual scheduling (or PUAS) algorithm as an enhancement to General Utility Scheduling (or GUS) algorithm proposed by Peng Li [1]. These scheduling algorithms are designed for adaptive real time system environment where undesirable effects such as overload and deadline misses are tolerable. We consider independent task models that are subject to deadline constraints specified using step time/utility functions (or TUFs). The basic idea of our algorithm is to reduce the number of unnecessary abortion that occurs in GUS by preemption instead of abortion. We consider the scheduling objective of maximizing the utility that is accrued by the completion of all tasks. Simulation results revealed that the proposed algorithm outperforms GUS algorithm. By reducing the total number of task aborted together with lower abortion ratio, this in effect produced a higher utility and reduced the average response time, making it more suitable and efficient in time-critical application domain.

Problem statement: The heterogeneity in the choice of simulation platforms for real time scheduling stands behind the difficulty of developing a common simulation environment. A Discrete Event Simulation (DES) for a real time scheduling domain encompassing event definition, time advancing mechanism and scheduler has yet to be developed. Approach: The study focused on the proposed and the development of an event based discrete event simulator for the existing General Utility Scheduling (GUS) to facilitate the reuse of the algorithm under a common simulation environment. GUS is one of the existing TUF/UA scheduling algorithms that consider the Time/Utility Function (TUF) of the executed tasks in its scheduling decision. The scheduling optimality criteria are based on maximizing accrued utility accumulated from execution of all tasks in the system. These criteria are named as Utility Accrual (UA). The TUF/ UA scheduling algorithms are design for adaptive real time system environment. The developed GUS simulator has derived the set of parameter, events, performance metrics and other unique TUF/UA scheduling element according to a detailed analysis of the base model. Results: The Accrued Utility Ratio (AUR) is investigated and compared to the benchmark of the modeled domain. Successful deployment of the GUS simulator was proven by the generated results. Conclusion: Extensive performance analysis of GUS simulator can be deployed using the developed simulator with low computational overhead. Further enhancements were to extend the developed GUS simulator with detail performance metrics together with a fault tolerance mechanism to support a reliable real time application domain.

Problem statement: This study proposed a robust algorithm named as Backward Recovery Preemptive Utility Accrual Scheduling (BRPUAS) algorithm that implements the Backward Recovery (BR) mechanism as a fault recovery solution under the existing utility accrual scheduling environment. The problem identified in the TUF/UA scheduling domain is that the existing algorithms only considers the Abortion Recovery (AR) as their fault recovery solution in which all faulty tasks are simply aborted to nullify the erroneous effect. The decision to immediately abort the affected tasks is inefficient because aborted tasks produce zero utility causes the system to accrue lower utility. Approach: The proposed BRPUAS algorithm enabled the re-execution of the affected tasks rather than abortion to reduce the number of aborted task in the existing algorithm known as Abortion Recovery Preemptive Utility Accrual Scheduling (ARPUAS) algorithm that employed the AR mechanism. The BRPUAS ensure the correctness of the executed tasks in the best effort basis in such a way that the infeasible tasks are aborted and produced zero utility, while the feasible tasks are re-executed to produce positive utility and consequently maximized the total accrued utility to the system. The performances of these algorithms are measured by using discrete event simulation. Results: The proposed BRPUAS algorithm achieved higher accrued utility compared to ARPUAS for the entire load range. Conclusion: Simulation results revealed that the BR mechanism is more efficient than the existing AR mechanism, producing higher accrued utility ratio and less abortion ratio making it more reliable and efficient for adaptive real time application domain.

In this paper, we proposed two recovery solutions over the existing error-free utility accrual scheduling algorithm known as General Utility Accrual Scheduling algorithm (or GUS) (Peng Li, 2004). A robust fault recovery algorithm called Backward Recovery GUS (or BRGUS) works by adapting the time redundancy model i.e., by re-executing the affected task after its transient error period is over. The BRGUS is compared with a less complicated recovery algorithm named as Abortion Recovery GUS (or ARGUS) that simply aborts all faulty tasks. Our main objectives are (1) to maximize the total accrued utility and (2) to ensure correctness of the executed tasks on best effort basis and achieve the fault free tasks as much as possible. Our simulation results reveal that BRGUS outperforms the ARGUS algorithm with higher accrued utility and less abortion ratio, making it more suitable and efficient in adaptive real time system.

This paper presents the simulation design of Partition PUAS (PPUAS) that is a TUF/UA real time scheduling algorithm for multicore environment using partitioned scheduling scheme. PPUAS solved the overloaded problem in uniprocessor environment. Simulation results revealed that the PPUAS were more efficient compared to the uniprocessor PUAS algorithm, producing a higher utility ratio and suitable for real time system in multicore.

In this paper, we proposed two recovery solutions over the existing error-free utility accrual scheduling algorithm known as General Utility Accrual Scheduling algorithm (or GUS) (Peng Li, 2004). A robust fault recovery algorithm called Backward Recovery GUS (or BRGUS) works by adapting the time redundancy model i.e., by re-executing the affected task after its transient error period is over. The BRGUS is compared with a less complicated recovery algorithm named as Abortion Recovery GUS (or ARGUS) that simply aborts all faulty tasks. Our main objectives are (1) to maximize the total accrued utility and (2) to ensure correctness of the executed tasks on best effort basis and achieve the fault free tasks as much as possible. Our simulation results reveal that BRGUS outperforms the ARGUS algorithm with higher accrued utility and less abortion ratio, making it more suitable and efficient in adaptive real time system.

We consider RealTime CORBA 2.0 (Dynamic Scheduling) distributable threads, whose time constraints are specified using time/utility functions (TUFs), operating in legacy environments. In legacy environments, system node resources-both physical (processor, disk, I/O, etc.) and logical (locks, etc.)-are shared among timecritical distributable threads and local applications that may or may not be timecritical. Thus, in such environments, distributable threads that are scheduled using their propagated TUFs and scheduling parameters, as mandated by RealTime CORBA 2.0's Case 2 approach, may suffer performance degradation, if a node scheduler can achieve higher local accrued utility by giving higher eligibility to local threads than to distributable threads. To alleviate this, we consider decomposing TUFs of distributable threads into "subTUFs" that are used for scheduling segments of distributable threads. We present methods for decomposing TUFs. Furthermore, we identify con...