3.2 Energy Aware Frame Based Fair Scheduling (EAFBFS)
3.2.4 Experiment and Results
3.2.4.3 Performance Comparison with EA-DPFair Algorithm
3.2 Energy Aware Frame Based Fair Scheduling (EAFBFS)
factor is varied from 1:5 to 4:5. Naturally, the ratio varies from 4:5 to 1:5 in the second subset. From Figure 3.4b, we observe that power consumption on type 2 systems only varies slightly with respect to the variation in skewness. However, the variation is significant for type 1 systems. It may be noted that for this task distribution, the task weights will be maximally uniform with least skewness when the ratio is 5:1. Hence, the power consumption of the EAFBFS algorithm in a type 1system has been observed to be lowest at point 80:20 (as shown in Figure 3.4b). As the uniformity among task weights decrease (i.e. skewness increase), the resultant power consumption rises intype 1 systems. We further observe that power consumption in type 1 systems is higher when the number of tasks is low (n= 32) than when it is high (n= 96). This is because as the number of tasks is increased while keeping the utilization factor constant, the average weight of individual tasks decrease. Thus, we have less cases where the individual frequency demand (Equation 3.9) of a task is higher than the average system frequency (Equation 3.8). In particular, when skewness is raised from 80:20 to 20:80 in type 1 system with n = 32, the power consumption increased from 81.26% to 94.49%. The average difference in the consumption of power betweentype 1 and type 2 systems with n = 32 and n= 96 was found to be 45.71% and 36.46% respectively.
(a)Fairness Measurement (b) Normalized Power Consumption
(c) Cost of Context Switch
Figure 3.5: Result Comparison: EAFBFS vs EA-DPFair
time slot. We observe from Figure 3.5a that with it’s ERFair-like scheduling strategy within time-slices, EAFBFS offers close to optimal fairness at all time-slots. On the other hand EA-DPFair, with it’s EDF-like intra-time-slice scheduling strategy guaran- tees ERFairness only at time-slice boundaries while allowing arbitrary unfairness within the time-slices. Also, EA-DPFair’s fairness decreases as the number of tasks increase.
However, as the number of tasks become significantly high and correspondingly average individual task shares become considerably less, the fairness deviations stabilises pro- gressively towards a certain value. In particular, when n is raised from 32 to 160 while keeping the utilization factor constant at 90%, EAFBFS is seen to achieve near perfect fairness (Fairness deviation∼0.007;φ = 0.1|T Sr|) while EA-DPFair suffers a significant fairness deviation (∼0.097) on 16 core type 2 systems. Also, even when the value ofφis
3.2 Energy Aware Frame Based Fair Scheduling (EAFBFS)
relaxed from 0.01|T Sr| toT Sr| (the length of an entire time-slice), the achieved fairness degrades only slightly. Our experimental results show that EAFBFS scheduling is able to achieve 10 to 15 times higher fairness (on average) than EA-DPFair. Hence, com- pared to EA-DPFair, EAFBFS may be considered to be more suitable for QoS sensitive applications which require execution progress at precise rates over short time scales.
Experiment 5 (Power Consumption): Power consumption of EAFBFS has been compared with EA-DPFair for data sets consisting of 96 tasks with different utilization factors. We observe from Figure 3.5b that power consumptions of EAFBFS and EA- DPFair follow a very similar trend, although EA-DPFair perform slightly better than EAFBFS. This is due to the fact that EAFBFS attempts to achieve the highest possible fairness for all tasks at all time-slots within time-slices. Additionally, it has to satisfy the constraint of limiting the maximum intra-time-slice transient unfairness of any task below a stipulated threshold φ in the presence of migrating tasks. EAFBFS achieves this by minimally increasing the operating frequency above the lowest frequency that is sufficient to guarantee schedulability of all tasks. EA-DPFair on the other hand al- lows unrestricted fairness deviations within time-slices and hence, do not require such frequency enhancement. Consequently EAFBFS’s power consumption is slightly higher than EA-DPFair as observed. In particular, EA-DPFair was seen to outperform EAF- BFS by 2.10%, 1.75% and 0.02% on 16 core type 2 systems for φ = 0.01|T Sr|,0.1|T Sr| and |T Sr|, respectively.
Experiment 6 (Cost of Context Switches): In our experiments, we have assumed the delay corresponding to a single context switch to be 5.24 µs [29], which may be considered to be a conservative assumption on most systems under typical workloads.
We counted the total number of context switches in the system over the entire simu- lation duration. The total delay due to context switches was then obtained by multi- plying the number of context switches with the delay caused by a single context switch (5.24µs [29]). This value is then normalized by computing the average context switch
overhead (in µs) per core per time slot for both EAFBFS and EA-DPFair algorithms.
As observed from Figure 3.5c, EA-DPFair incurs significantly fewer context switches compared to EAFBFS. This is because EA-DPFair uses an EDF based scheduling strat- egy within time slices, whereas EAFBFS uses ERFair. Higher proportional fairness as achieved by EAFBFS is essentially effected by appropriately switching task executions on cores (using preemptions and migrations) so that stipulated rates of progress may be guaranteed for all tasks across short time spans. To incorporate the cost of context switches into the overall power consumption, the operating frequencies of the individual cores were adjusted accordingly. For example from Figure 3.5c, we observe that for 64 tasks, EAFBFS suffers a context switch overhead of ∼ 3µs while EA-DPFair suffers a context switch overhead of only ∼ 0.5µs. So, EAFBFS incurs an extra overhead of 2.5µs per core per task per time slot with respect to EA-DPFair. Given a time slot size of 1ms, EAFBFS will therefore be able to complete as much work in 401msas EA- DPFair will complete in (1ms/2.5µs =) 400ms. To overcome this additional context switching overhead, EAFBFS must execute at 401/400 times the operating frequency of EA-DPFair. As all our experiments have been conducted assuming only 12 available discrete frequency levels (refer Table 3.2.4.1), this additional overhead in terms of calcu- lated frequency very rarely translates into an increase in the discrete frequency level of a core. Hence, the cost of context switching did not have any significant effect on power consumption in our experiments.