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Energy efficient scheduling and routing via randomized rounding.
 IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2013), volume 24 of Leibniz International Proceedings in Informatics,
, 2013
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Minimizing Energy Under Performance Constraints on Embedded Platforms Resource Allocation Heuristics for Homogeneous and SingleISA Heterogeneous MultiCores
"... This paper explores the problem of energy optimization in embedded platforms. Specifically, it studies resource allocation strategies for meeting performance constraints with minimal energy consumption. We present a comparison of solutions for both homogeneous and singleISA heterogeneous multico ..."
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This paper explores the problem of energy optimization in embedded platforms. Specifically, it studies resource allocation strategies for meeting performance constraints with minimal energy consumption. We present a comparison of solutions for both homogeneous and singleISA heterogeneous multicore embedded systems. We demonstrate that different hardware platforms have fundamentally different performance/energy tradeoff spaces. As a result, minimizing energy on these platforms requires substantially different resource allocation strategies. Our investigations reveal that one class of systems requires a racetoidle heuristic to achieve optimal energy consumption, while another requires a neveridle heuristic to achieve the same. The differences are dramatic: choosing the wrong strategy can increase energy consumption by over 2 × compared to optimal.
Slow Down & Sleep for Profit in Online Deadline Scheduling
, 2012
"... We present and study a new model for energyaware and profitoriented scheduling on a single processor. The processor features dynamic speed scaling as well as suspension to a sleep mode. Jobs arrive over time, are preemptable, and have different sizes, values, and deadlines. On the arrival of a n ..."
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We present and study a new model for energyaware and profitoriented scheduling on a single processor. The processor features dynamic speed scaling as well as suspension to a sleep mode. Jobs arrive over time, are preemptable, and have different sizes, values, and deadlines. On the arrival of a new job, the scheduler may either accept or reject the job. Accepted jobs need a certain energy investment to be finished in time, while rejected jobs cause costs equal to their values. Here, power consumption at speed s is given by P (s) = sα + β and the energy investment is power integrated over time. Additionally, the scheduler may decide to suspend the processor to a sleep mode in which no energy is consumed, though awaking entails fixed transition costs γ. The objective is to minimize the total value of rejected jobs plus the total energy. Our model combines aspects from advanced energy conservation techniques (namely speed scaling and sleep states) and profitoriented scheduling models. We show that rejectionoblivious schedulers (whose rejection decisions are not based on former decisions) have – in contrast to the model without sleep states – an unbounded competitive ratio w.r.t. the processor parameters α and β. It turns out that the worstcase performance of such schedulers depends linearly on the jobs ’ value densities (the ratio between a job’s value and its work). We give an algorithm whose competitiveness nearly matches this lower bound. If the maximum value density is not too large, the competitiveness becomes αα + 2eα. Also, we show that it suffices to restrict the value density of lowvalue jobs only. Using a technique from [12] we transfer our results to processors with a fixed maximum speed.
Energyefficient Mapping of Task Collections onto Manycore Processors
"... Streaming applications consist of a number of tasks that all run concurrently, and that process data at certain rates. On manycore processors, the tasks of the streaming application must be mapped onto the cores. While load balancing of such applications has been considered, especially in the MPSoC ..."
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Streaming applications consist of a number of tasks that all run concurrently, and that process data at certain rates. On manycore processors, the tasks of the streaming application must be mapped onto the cores. While load balancing of such applications has been considered, especially in the MPSoC community, we investigate energyefficient mapping of such task collections onto manycore processors. We first derive rules that guide the mapping process and show that as long as dynamic power consumption dominates static power consumption, the latter can be ignored and the problem reduces to load balancing. When however, as expected in the coming years, static power consumption will be a notable fraction of total power consumption, then an energyefficient mapping must take it into account, e.g. by temporary shutdown of cores or by restricting the number of cores. We validate our findings with synthetic and realworld applications on the Intel SCC manycore processor. 1.
MEANTIME: Achieving Both Minimal Energy and Timeliness with Approximate Computing MEANTIME: Achieving Both Minimal Energy and Timeliness with Approximate Computing
"... Abstract Energy efficiency and timeliness (i.e., predictable job latency) are two essential yet opposing concerns for embedded systems. Hard timing guarantees require conservative resource allocation while energy minimization requires aggressively releasing resources and occasionally violating ti ..."
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Abstract Energy efficiency and timeliness (i.e., predictable job latency) are two essential yet opposing concerns for embedded systems. Hard timing guarantees require conservative resource allocation while energy minimization requires aggressively releasing resources and occasionally violating timing constraints. Recent work on approximate computing, however, opens up a new dimension of optimization: application accuracy. In this paper, we use approximate computing to achieve both hard timing guarantees and energy efficiency. Specifically, we propose MEANTIME: a runtime system that delivers hard latency guarantees and energyminimal resource usage through small accuracy reductions. We test MEANTIME on a real Linux/ARM system with six applications. Overall, we find that MEANTIME never violates realtime deadlines and sacrifices a small amount (typically less than 2%) of accuracy while reducing energy to 54% of a conservative, full accuracy approach.
OnTheFly Computing: A Novel Paradigm for Individualized IT Services (Invited Paper)
"... Abstract In this paper we introduce "OnTheFly Computing", our vision of future IT services that will be provided by assembling modular software components available on worldwide markets. After suitable components have been found, they are automatically integrated, configured and brough ..."
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Abstract In this paper we introduce "OnTheFly Computing", our vision of future IT services that will be provided by assembling modular software components available on worldwide markets. After suitable components have been found, they are automatically integrated, configured and brought to execution in an OnTheFly Compute Center. We envision that these future compute centers will continue to leverage three current trends in large scale computing which are an increasing amount of parallel processing, a trend to use heterogeneous computing resources, andin the light of rising energy costenergyefficiency as a primary goal in the design and operation of computing systems. In this paper, we point out three research challenges and our current work in these areas.
An O(n²) Algorithm for Computing Optimal Continuous Voltage Schedules
, 2014
"... Dynamic Voltage Scaling techniques allow the processor to set its speed dynamically in order to reduce energy consumption. In the continuous model, the processor can run at any speed, while in the discrete model, the processor can only run at finite number of speeds given as input. The current be ..."
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Dynamic Voltage Scaling techniques allow the processor to set its speed dynamically in order to reduce energy consumption. In the continuous model, the processor can run at any speed, while in the discrete model, the processor can only run at finite number of speeds given as input. The current best algorithm for computing the optimal schedules for the continuous model runs at O(n2 log n) time for scheduling n jobs. In this paper, we improve the running time to O(n2) by speeding up the calculation of sschedules using a more refined data structure. For the discrete model, we improve the computation of the optimal schedule from the current best O(dn log n) to O(n log max{d, n}) where d is the number of allowed speeds.
Evangelos Markakis and Ioannis MilisOn Multiprocessor TemperatureAware Scheduling Problems
"... We study temperatureaware scheduling problems under the model introduced by Chrobak et al. in [6]. We consider a set of parallel identical processors and three optimization criteria: makespan, maximum temperature and (weighted) average temperature. On the positive side, we present polynomial time a ..."
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We study temperatureaware scheduling problems under the model introduced by Chrobak et al. in [6]. We consider a set of parallel identical processors and three optimization criteria: makespan, maximum temperature and (weighted) average temperature. On the positive side, we present polynomial time approximation algorithms for the minimization of the makespan and the maximum temperature, as well as, optimal polynomial time algorithms for minimizing the average temperature and the weighted average temperature. On the negative side, we prove that there is no ( 4 3 − ϵ)approximation algorithm for the problem of minimizing the makespan for any ϵ> 0, unless P = N P. 1
Energy Efficient Scheduling and Routing via Randomized Rounding
"... We propose a unifying framework based on configuration linear programs and randomized rounding, for different energy optimization problems in the dynamic speedscaling setting. We apply our framework to various scheduling and routing problems in heterogeneous computing and networking environments. W ..."
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We propose a unifying framework based on configuration linear programs and randomized rounding, for different energy optimization problems in the dynamic speedscaling setting. We apply our framework to various scheduling and routing problems in heterogeneous computing and networking environments. We first consider the energy minimization problem of scheduling a set of jobs on a set of parallel speedscalable processors in a fully heterogeneous setting. For both the preemptivenonmigratory and the preemptivemigratory variants, our approach allows us to obtain solutions of almost the same quality as for the homogeneous environment. By exploiting the result for the preemptivenonmigratory variant, we are able to improve the best known approximation ratio for the single processor nonpreemptive problem. Furthermore, we show that our approach allows to obtain a constantfactor approximation algorithm for the poweraware preemptive job shop scheduling problem. Finally, we consider the minpower routing problem where we are given a network modeled by an undirected graph and a set of uniform demands that have to be routed on integral routes from their sources to their destinations so that the energy consumption is minimized. We improve the best known approximation ratio for this problem. 1