Maintaining the Feasibility of Hard Real–Time Systems with a Reduced Number of Priority Levels

Abstract

When there is a mismatch between the cardinality of a periodic task set and the priority levels supported by the underlying hardware systems, multiple tasks are grouped into one class so as to maintain a specific level of confidence in their accuracy. However, such a transformation is achieved at the expense of the loss of schedulability of the original task set. We further investigate the aforementioned problem and report the following contributions: (i) a novel technique for mapping unlimited priority tasks into a reduced number of classes that do not violate the schedulability of the original task set and (ii) an efficient feasibility test that eliminates insufficient points during the feasibility analysis. The theoretical correctness of both contributions is checked through formal verifications. Moreover, the experimental results reveal the superiority of our work over the existing feasibility tests by reducing the number of scheduling points that are needed otherwise.

Time and Cost Efficient Cloud Resource Allocation for Real-Time Data-Intensive Smart Systems

“…The advantage of the task grouping mechanism is to reduce the total number of priority levels [25]. Additionally, t N ∈ NP for a higher priority task task j ∈ Y x remains the member of NP for all lower priority tasks than task j in Y x , since tasks in the same group are also sorted on the basis of RM priorities.…”

“…If adding a task changes the inequality to GU ≥ n(2 1/n − 1), then the incoming tasks are added to another group. The authors in [25] refer the LL test as the sufficient condition test. They claim that it is not necessary that the tasks in a group are not schedulable if Equation (11) does not hold.…”

“…These resources need intelligent scheduling and allocation of the tasks such that all tasks in a batch can be processed within the stipulated time span. The research community focuses on searching mechanisms for scheduling and allocating distributed cloud resources in a systematic way which can satisfy formulated objective function such as load balancing, makespan minimization, and cost-efficiency with respect to the user-defined QoS criteria [3,24,25]. But, instead of the vast study in the cloud resource allocation domain, the existing literature is not mature in providing suitable scheduling mechanisms for real-time systems generated by smart devices due to the high deadline-miss ratio by a number of tasks in a batch [26].…”

A comparative analysis of resource allocation schemes for real-time services in high-performance computing systems

“…In reduced priority levels technique, 46 the hard RT tasks are grouped into multiple classes. Priority is assigned to a class instead of individual task which means that if there are total n tasks in a set, then the number of priority levels will be less than n which shows that one of the class has more than one task.…”