constraints, we propose different scheduling strategies for real-time systems.
1.4 Contributions
We have designed the following resource efficient low-overhead semi-partitioned sched- ulers for multiprocessor/multi-core systems:
1.4.1 EAFBFS: An Energy Aware Frame Based Fair Scheduler
In this work, we propose a new semi-partitioned scheduling strategy for hard real- time homogeneous multi-core systems called Energy Aware Frame Based Fair Schedul- ing (EAFBFS), which combines the benefits of high resource utilization and restricted migrations as in the Energy-Aware DPFair (EA-DPFair) algorithm [57] while provid- ing accurate as well as tunable proportional fairness for all tasks across all time slots.
EAFBFS employs a semi-partitioning strategy with a two-level hierarchical scheduling scheme, where the outer level divides time into frames/slices, demarcated by the arrivals and departures of all tasks in the system. This approach helps it to significantly restrict migration and preemption overheads. The inner level scheduler supervises task execu- tions within time-slices. Given a specific fairness deviation bound as input, the algorithm automatically adjusts itself to meet such a demand, albeit at the cost of possibly higher energy dissipation. Experimental results show that EAFBFS is able to achieve higher fairness accuracy (10 to 15 times on average) with respect to state-of-the-art [57] while saving almost the same percentage of energy on heavily loaded systems.
1.4.2 DPFair Scheduling with Slowdown and Suspension
A major class of real-time systems typically experience short bursts of heavy CPU ac- tivity interleaved with long durations of significantly lower workloads. TheDVFS based DP-Wrap [57] algorithm provides an important scheduling solution for such resource constrained hard real-time systems due to its ability to provide optimal resource utiliza- tion, controlled migrations and minimized dynamic energy dissipation. However, DVFS based schedulers are only able to decrease dynamic energy consumption of the system
by reducing the voltage/frequency of a processor. With exponential growth in chip transistor densities over technology generations, static energy dissipation due to leak- age drain from transistors has steeply increased over the years [14]. Since DVFS based DP-Wrap only focusses on reduction of dynamic energy, it allows significant static en- ergy dissipation whenever system workloads are not high enough to demand operation above the critical frequency [69]. This work therefore proposes an integrated DVFS- cum-DPM based DPFair based scheduling strategy for homogeneous multi-core systems called,DPFair with slowdown and suspension (DPFair-SS), in order to minimize overall system-wide energy consumption combining both static and dynamic energy dissipation.
Experimental results show that our proposed scheduling technique DPFair-SS, exhibits appreciable energy savings over the state-of-the-art [57], in situations when the system experiences low workloads over significantly long durations.
1.4.3 A Cluster-Oriented Scheduling Technique for Heteroge- neous Multi-cores
Development of efficient resource allocation strategies for real-time tasks on heteroge- neous platforms has traditionally proved to be a challenging as well as a computationally expensive problem. However, strategies which can efficiently schedule real-time task sets on generic heterogeneous platforms having an arbitrary number of processor types, are rare. Most of the existing strategies [91,108] are oriented towards systems with restricted number of processing core types. Hence, this work proposes an effective low-overhead heuristic approach called COST: A Cluster-Oriented Scheduling Technique for Hetero- geneous Multi-cores, for scheduling a set of periodic tasks executing on a heterogeneous multi-core system. The proposed technique works in three-phases namely, Core Clus- tering, Task Partitioning, and Task Scheduling. The Core Clustering step attempts to combine the available processing cores into a group of clusters. Each cluster consists of two cores and a disjoint subset of the given task set is assigned to it. The tasks assigned to a cluster are then allocated to the processing cores of the cluster in the Task Parti- tioning phase and scheduled fairly in the Task Scheduling phase. Experimental studies show that our proposed scheme provides high resource utilization by scheduling more
1.4 Contributions
number of task sets with respect to state-of-the-art [96].
1.4.4 A Low Overhead Scheduler for Real-Time Periodic Tasks on Heterogeneous Multi-core Systems
This work proposes an effective low-overhead heuristic approach named HETERO- SCHED, for scheduling a set of periodic tasks executing on heterogeneous multi-core systems. The proposed approach first applies deadline partitioning [78] to obtain a set of discrete time-slices. Over each such time-slice, HETERO-SCHED conducts the follow- ing two phase operation: First, it determines the fractions of the computation demand of each task to be assigned onto the platform. Next, it assigns valid start and finish times to all tasks, according to the allocation prescribed in the first phase. Experimental stud- ies show that our proposed scheduling mechanism is able to schedule significantly higher number of tasks sets, compared to the state-of-the-art [108].
1.4.5 An Energy-Aware Scheduler for Heterogeneous Multi- core real-time systems
Devising energy-efficient scheduling strategies for real-time periodic tasks on hetero- geneous platforms is a challenging as well as a computationally demanding problem.
As a consequence, today we face a scarcity of real-time energy-aware scheduling tech- niques which are applicable to heterogeneous platforms. Hence, this work proposes a low-overhead heuristic strategy called HEALERS, for DVFS enabled energy-aware scheduling of a set of periodic tasks executing on a heterogeneous multi-core system.
The presented strategy first appliesdeadline-partitioning [78] to acquire a set of distinct time-slices. At any time-slice boundary, the following three phase operation is applied to obtain schedule for the next time-slice: First, it computes the fragments of the execution demands of all tasks on to each of the different processing cores in the platform. Next, it generates a schedule for each task on one or more processing cores such that the total execution demand of all tasks are satisfied. Finally, HEALERS applies DVFS on all processing cores so that energy consumption within the time-slice may be minimized while not jeopardizing execution requirements of the scheduled tasks. Experimental
results show that our scheme is not only able to achieve appreciable energy savings with respect to state-of-the-art [16] (5% to 42% on average) but also enables significant improvement in resource utilization (as high as 58%).
1.4.6 A Temperature-Aware Real-Time Semi-partitioned Sched- uler
Modern multi-core systems, which execute complex functionalities at high frequencies on densely packed multi-million gate platforms, are often prone to unacceptable surges in core temperatures, if not effectively managed. Increase in temperature beyond stip- ulated thresholds not only results in high cooling costs but also leads to high leakage power dissipation [80, 117], along with reduced efficiency and lower life-span for the sys- tem. Given, a set of periodic real-time tasks to be executed on a thermally constrained multi-core system, proportional fair schedulers form an attractive scheduling alternative.
This is because of their flexibility and the ability to deliver efficient resource utilization, which can potentially enable accurate control over the timeliness of all tasks as well as stipulated temperature upper bounds on all processing cores, over the entire schedule length. In this paper, we propose a low-overhead two-level hierarchical temperature- aware semi-partitioned proportional fair scheduler, calledTemperature-Aware Real-Time Semi-partitioned Scheduler (TARTS). The first level in TARTS partitions time into dis- crete slices based on task deadlines, such that accurate proportional fairness is main- tained at all slice boundaries. The second level performs intra-slice scheduling with the objective of maximizing resource utilization while not breaching a stipulated temperature threshold. Our experimental results show that TARTS is able to perform appreciably under various realistic scenarios.