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) assuring 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 underloads, and tightly upper bounds inter and intratask completion times. Our simulation experiments confirm the algorithm's effectiveness and superiority

In this paper, we consider timeliness and energy optimization in battery-powered dynamic embedded real-time systems, which must remain functional during an operation/mission with a bounded energy budget. We consider application activities that are subject to time/utility function time constraints, statistical assurance requirements on timeliness behavior, and an energy budget which cannot be exceeded at runtime. To account for the inevitable variability in activity arrivals in dynamic systems, we describe arrival behaviors using the unimodal arbitrary arrival model (UAM) [15]. For such a model, we present a dynamic voltage scaling (DVS)-based CPU scheduling algorithm called the energy-bounded utility accrual algorithm (EBUA). Since the scheduling problem is intractable, EBUA allocates CPU cycles, scales clock frequency, and heuristically computes schedules using statistical estimates of cycle demands in polynomial time. We analytically establish EBUA's properties, including satisfaction of energy bounds, statistical assurances on individual activity timeliness behavior, optimal timeliness during underloads, and bounded time for mutually exclusively accessing shared non-CPU resources. Our simulation experiments validate our analytical results and illustrate the algorithm's effectiveness and superiority over past algorithms.

This paper presents a uni-processor real-time scheduling algorithm called the generic utility scheduling algorithm (which we refer to simply as GUS). GUS solves a previously open real-time scheduling problem-scheduling application activities that have time constraints specified using arbitrarily shaped time/utility functions and have mutual exclusion resource constraints. A time/ utility function are a time constraint specification that describes an activity's utility to the system as a function of that activity's completion time. Given such time and resource constraints, we consider the scheduling objective of maximizing the total utility that is accrued by the completion of all activities. Since this problem is NP-hard, GUS heuristically computes schedules with a polynomial-time cost of O(n/sup 3/) at each scheduling event, where n is the number of activities in the ready queue. We evaluate the performance of GUS through simulation and by an actual implementation on a real-time POSIX operating system. Our simulation studies and implementation measurements reveal that GUS performs close to, if not better than, the existing algorithms for the cases that they apply. Furthermore, we analytically establish several properties of GUS.

We present an energy-efficient, utility accrual, real-time scheduling algorithm called ReUA. ReUA considers an application model where activities are subject to time/utility function time constraints, mutual exclusion constraints on shared non-CPU resources, and statistical performance requirements on individual activity timeliness behavior. The algorithm targets mobile embedded systems where system-level energy consumption is also a major concern. For such a model, we consider the scheduling objectives of (1) satisfying the statistical performance requirements and (2) maximizing the system-level energy efficiency, while respecting resource constraints. Since the problem is NP-hard, ReUA allocates CPU cycles using statistical properties of application cycle demands, and heuristically computes schedules with a polynomial time cost. We analytically establish several timeliness and nontimeliness properties of the algorithm. Further, our simulation experiments illustrate ReUA'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 real-time scheduling algorithm called VCUA, for tasks whose execution times are functions of their starting times. We model such variable execution times with variable cost functions (or VCFs). The algorithm considers application activities that are subject to time/utility function time constraints, VCFs, and the scheduling objective of assuring that the maximum interval between any two consecutive successful completions of jobs of a task must not exceed a specified bound, and maximizing the system's total utility. We establish that VCUA achieves optimal timeliness during under-loads, and identify the conditions under which timeliness assurances hold. Our simulation experiments illustrate VCUA's effectiveness and superiority.

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 five decomposition techniques, called UT, SCEQF, SCALL, OPTCON, and TUFS, which are specific to different classes of UA scheduling algorithms, such as those that use utility density and those that use deadline as their key decision metric. Our experimental studies identify the decomposition technique that performs best for each class of UA scheduling algorithms. In particular, our studies show that OPTCON and TUFS perform best for utility density-based UA algorithms, while SCEQF and SCALL perform best for deadline-based UA algorithms.

We present an energy-efficient real-time scheduling algorithm called the Resource-constrained Energy-Efficient Utility Accrual Algorithm (or ReUA). ReUA considers an application model where activities are subject to time/utility function-time constraints, resource dependencies including mutual exclusion constraints, and statistical performance requirements including probabilistically satisfied, activity (timeliness) utility bounds. Further, ReUA targets mobile embedded systems where system-level energy consumption is a major concern. For such a model, we consider the scheduling objectives of (1) satisfying statistical performance requirements, and (2) maximizing system-level energy efficiency, while respecting resource dependencies. Since the problem is NP-hard, ReUA allocates resources using statistical properties of application cycle demands and heuristically computes schedules with a polynomial-time cost. We analytically establish several timeliness and non-timeliness properties of the algorithm. Further, our simulation experiments illustrate ReUA's effectiveness.

Summary form only given. 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.