Dynamic Voltage Frequency Scaling Technique Research Articles

Distributionally robust chance-constrained planning for regional integrated electricity–heat systems with data centers considering wind power uncertainty

This paper proposed an optimal planning method for regional integrated electricity–heat systems with data centers (DC-RIEHS) considering wind power uncertainty to reduce economic costs and improve system flexibility. First, a novel data center model is developed, where various flexible operating characteristics of the data center are modeled in detail, including thermal inertia of indoor air, rescheduling of delay-tolerant workloads, dynamic voltage frequency scaling technique of servers and heat recovery. The heat recovery system is specially built based on the electric cooling system of the studied data center and heat pump, which connects electric power systems and district heating systems. On this basis, a three-stage distributionally robust chance-constrained (DRCC) planning model of DC-RIEHS is proposed. It is converted into a tractable single-stage mixed-integer conic model by applying affine decision rules to hedge against wind power uncertainty. The proposed planning method determines the optimal capacities of energy equipment and the scheduling decisions of representative days considering wind power uncertainty. Simulation results demonstrate that the flexible resources in a data center can significantly reduce system costs and actively participate in coping with wind power uncertainty. Compared with the chance-constrained optimization method under Gaussian distribution, the proposed DRCC method exhibits better out-of-sample performance in terms of violation probability and system operation results under the wind uncertainty with spatial–temporal correlation.

Fault tolerance aware workload resource management technique for real‐time workload in heterogeneous computing environment

AbstractWorkload execution is composed by mapping larger tasks onto heterogeneous environments such as cloud platforms for enhancing the efficiency of workload resource management techniques. Execution of scientific workflow on a cloud platform is time‐consuming, expensive, and requires a fault‐tolerance guarantee. The existing methodology has emphasized on minimizing processing time to reduce costs. However, the processing cost can be reduced by minimizing energy consumption. In providing fault‐tolerance while meeting the workload quality of service requirement the task is offloaded to a new physical machine; Hence, this increases energy consumption and thereby increases the cost of workload execution. In addressing the research challenges this paper presents the fault‐tolerant aware (FTA) workload resource management (WRM) technique. First, the FTA‐WRM optimizes processing and communication costs as an energy constraint leveraging the dynamic voltage frequency scaling technique. Then, a task offloading mechanism is modeled as an energy constraint with an application delay requirement for providing fault tolerance into the FTA‐WRM. The experiment outcome shows the FTA‐WRM significantly improves processing and energy efficiency in comparison with the existing workload resource management technique.

Coordinated Batching and DVFS for DNN Inference on GPU Accelerators

Employing hardware accelerators to improve the performance and energy-efficiency of DNN applications is on the rise. One challenge of using hardware accelerators, including the GPU-based ones, is that their performance is limited by internal and external factors, such as power caps. A common approach to meet the power cap constraint is using the Dynamic Voltage Frequency Scaling (DVFS) technique. However, the functionally of this technique is limited and platform-dependent. To tackle this challenge, we propose a new control knob, which is the size of input batches fed to the GPU accelerator in DNN inference applications. We first evaluate the impact of batch size on power consumption and performance of DNN inference. Then, we introduce the design and implementation of a fast and lightweight runtime system, called BatchDVFS. Dynamic batching is implemented in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BatchDVFS</i> to adaptively change the batch size, and hence, trade-off throughput with power consumption. It employs an approach based on binary search to find the proper batch size within a short period of time. Combining dynamic batching with the DVFS technique, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BatchDVFS</i> can control the power consumption in wider ranges, and hence, yield higher throughput in the presence of power caps. To find near-optimal solution for long-running jobs that can afford a relatively significant profiling overhead, compared with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BatchDVFS</i> overhead, we also design an approach, called BOBD, that employs Bayesian Optimization to wisely explore the vast state space resulted by combination of the batch size and DVFS solutions. Conducting several experiments using a modern GPU and several DNN models and input datasets, we show that our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BatchDVFS</i> can significantly surpass the techniques solely based on DVFS or batching, regarding throughput (up to 11.2x and 2.2x, respectively), while successfully meeting the power cap.

Energy efficient mixed task handling on real-time embedded systems using FreeRTOS

With the increased use of embedded devices in Industrial electronics, like relays, battery life has gained more and more attention. Modern processors can use Dynamic Voltage Frequency Scaling (DVFS) techniques for energy reduction and temperature control. However, DVFS is not well supported by systems running with Real-Time operating systems like FreeRTOS, which is a widely used real-time operation system (RTOS) in the industry. Furthermore, energy-efficient DVFS techniques for Mixed Tasksets (including periodic and aperiodic tasks) are hardly investigated. This paper extends the classic well-known DVFS technique Cycle Conserving algorithm to handle Mixed Taskset (CCMT algorithm) and implements it on a real-time embedded platform powered by FreeRTOS. We describe our experience implementing CCMT on a real platform with limited DVFS and corresponding scheduler support. Results show that we can successfully apply CCMT to handle aperiodic requests while meeting the deadlines of the periodic tasks and saving energy on the FreeRTOS platform. The algorithm is tested on an ARM Cortex-M7 processor integrated with frequency scaling and power management. Over 5% to 10% energy savings can be achieved for a standard real-time scheduling mechanism without penalty in application throughput.

Concerto: Dynamic Processor Scaling for Distributed Data Systems with Replication

A surge of interest in data-intensive computing has led to a drastic increase in the demand for data centers. Given this growing popularity, data centers are becoming a primary contributor to the increased consumption of energy worldwide. To mitigate this problem, this paper revisits DVFS (Dynamic Voltage Frequency Scaling), a well-known technique to reduce the energy usage of processors, from the viewpoint of distributed systems. Distributed data systems typically adopt a replication facility to provide high availability and short latency. In this type of architecture, the replicas are maintained in an asynchronous manner, while the master synchronously operates via user requests. Based on this relaxation constraint of replica, we present a novel DVFS technique called Concerto, which intentionally scales down the frequency of processors operating for the replicas. This mechanism can achieve considerable energy savings without an increase in the user-perceived latency. We implemented Concerto on Redis 6.0.1, a commercial-level distributed key-value store, demonstrating that all associated performance issues were resolved. To prevent a delay in read queries assigned to the replicas, we offload the independent part of the read operation to the fast-running thread. We also empirically demonstrate that the decreased performance of the replica does not cause an increase of the replication lag because the inherent load unbalance between the master and replica hides the increased latency of the replica. Performance evaluations with micro and real-world benchmarks show that Redis saves 32% on average and up to 51% of energy with Concerto under various workloads, with minor performance losses in the replicas. Despite numerous studies of the energy saving in data centers, to the best of our best knowledge, Concerto is the first approach that considers clock-speed scaling at the aggregate level, exploiting heterogeneous performance constraints across data nodes.

Energy-minimized Scheduling of Real-time Parallel Workflows on Heterogeneous Distributed Computing Systems

Today's large-scale parallel workflows are often processed on heterogeneous distributed computing platforms. From an economic perspective, computing resource providers should minimize the cost while offering high service quality. It has become well-recognized that energy consumption accounts for a large part of a computing system's total cost, and timeliness and reliability are two important service indicators. This work studies the problem of scheduling a parallel workflow that minimizes the system energy consumption under the constraints of response time and reliability. We first mathematically formulate this problem as a Non-linear Mixed Integer Programming problem. Since this problem is hard to solve directly, we present some highly-efficient heuristic solutions. Specifically, we first develop an algorithm that minimizes the schedule length while meeting reliability requirement, on top of which we propose a processor-merging algorithm and a slack time reclamation algorithm using a dynamic voltage frequency scaling (DVFS) technique to reduce energy consumption. The processor-merging algorithm tries to turn off some energy-inefficient processors such that energy consumption can be minimized. The DVFS technique is applied to scale down the processor frequency at both processor and task levels to reduce energy consumption. Experimental results on two real-life workflows and extensive synthetic parallel workflows demonstrate their effectiveness.

A smart energy and reliability aware scheduling algorithm for workflow execution in DVFS-enabled cloud environment

The energy consumption is one of the major concerns addressed by recent researches in the green cloud environment. As a result, to decrease the enormous increase in energy consumption, one of the most promising scheduling techniques used nowadays is the Dynamic Voltage Frequency Scaling (DVFS) technique. DVFS reduces energy consumption by lowering the processors’ frequency for virtual machines (VMs); this results in an increase in the occurrence of errors during the execution of the workflow, which decreases the reliability of the system. As a consequence, this paper addresses the DVFS problem by proposing a new Smart Energy and Reliability Aware Scheduling algorithm (SERAS) for workflow execution in the cloud environment. The SERAS approach split the target deadline of workflow across tasks. Afterward, the proposed algorithm decreases the frequency of processors for VMs using the DVFS technique without missing the tasks’ deadline. As a consequence, the SERAS algorithm allocates the tasks to the most appropriate VMs with suitable frequencies levels while guaranteeing both the reliability and the completion time requirement of green cloud systems. To vindicate the effectiveness of the SERAS algorithm in real-world applications, we carried out a series of experiments on four real workflows generated using a scientific toolkit. Also, we performed comprehensive experiments with recent researches. The results showed that the SERAS algorithm outperforms its competitors while keeping both the reliability and completion time requirements. Furthermore, the estimated time complexity and average execution time show the applicability of the SERAS algorithm compared with their competitors.

Runtime adaptive Dynamic Voltage Frequency Scaling technique for reducing the power consumption in Multi Processor System On Chip

Runtime adaptive Dynamic Voltage Frequency Scaling technique for reducing the power consumption in Multi Processor System On Chip

A green SLA constrained scheduling algorithm for parallel/scientific applications in heterogeneous cluster systems

Cloud services utilize Heterogeneous Clusters as the hardware infrastructure at the backend. Minimal energy consumption in Heterogeneous Cluster System (HCS) is an important but challenging job. It can bring numerous benefits such as drop in operational cost, enhanced system reliability and green environment. Scheduling on HCS, with the objectives of minimizing the energy consumption, is being addressed by the research community at large. This work proposes a scheduling algorithm with green Service Level Agreement (SLA) constrained execution of parallel applications. The green SLA assumes that the tasks are to be scheduled on Dynamic Voltage Frequency Scaling (DVFS) enabled HCS. The proposed algorithm acts in two phases: first, it minimizes the energy consumption for the slacks of the schedule using DVFS technique, and second, a green SLA is made between a customer and service provider to maintain a good energy-performance trade-off. With this strategy, makespan of the schedule is extended a bit but the energy consumption is minimized for the period of extended makespan. It is observed that the decrease in one level of voltage and frequency of high-speed Processing Element (PE) saves more energy compared to that of low-speed PE. To utilize this, jobs running on higher voltage and frequency level of the PE are prioritized first. The performance of the proposed algorithm is studied by simulation on several standard Directed Acyclic Graphs (DAGs) and few other well-known real-world scientific workflows. The comparative study exhibits the effectiveness of the proposed algorithm over state of the art on four real heterogeneous PEs.

Energy and performance improvement using real-time DVFS for graph traversal on GPU

Graphical-processing unit (GPU) handles massive data, effectively, as compared to CPU but has high power consumption which is a primary concern recently. A real-time dynamic voltage frequency scaling technique (DVFS) to save energy as well as improve performance, and to set the core and the memory frequency at a run time are proposed. Real-time DVFS is well investigated for saving energy and improving the performance by executing a breadth-first search (BFS) graph traversal algorithm from scalable heterogeneous computing (SHOC) benchmark suite. Two modes: energy saving and performance mode, can be realised by implementation of the proposed real-time DVFS algorithm. In energy saving mode, 28.2% energy can be saved with a performance improvement of 1.4%. On the other hand, 14.2% performance improvement is accomplished while maintaining the energy saving of 11.2% in performance mode. The proposed work is realised on K40c GPU received as a grant from NVidia.

Energy and performance improvement using real-time DVFS for graph traversal on GPU

Graphical-processing unit (GPU) handles massive data, effectively, as compared to CPU but has high power consumption which is a primary concern recently. A real-time dynamic voltage frequency scaling technique (DVFS) to save energy as well as improve performance, and to set the core and the memory frequency at a run time are proposed. Real-time DVFS is well investigated for saving energy and improving the performance by executing a breadth-first search (BFS) graph traversal algorithm from scalable heterogeneous computing (SHOC) benchmark suite. Two modes: energy saving and performance mode, can be realised by implementation of the proposed real-time DVFS algorithm. In energy saving mode, 28.2% energy can be saved with a performance improvement of 1.4%. On the other hand, 14.2% performance improvement is accomplished while maintaining the energy saving of 11.2% in performance mode. The proposed work is realised on K40c GPU received as a grant from NVidia.

Fuzzy Decision Efficiency for Multiprocessor Architecture Configuration

Dynamic voltage/frequency scaling (DVFS) has been an efficient technique in exploiting multi-processors configurable characteristics. However, for large class of embedded real time applications, changing processors frequency interferes with tasks deadline guarantee. Under homogenous multiprocessor architecture, we are interested in intelligent offline/online reconfiguration based on fuzzy inference system.This paper investigates the efficiency of using fuzzy decision in multiprocessor architecture configuration. Goals of decision making are system schedulability guarantee and energy consumption optimization. Experiments concern real time periodic tasks scheduled with global EDF and realized over a developed simulation environment that supports DVFS technique. Different simulations were realized on H264 Video Decoder in order to evaluate the proposed approach response.

A new rule-based power-aware job scheduler for supercomputers

The fast processing speeds of the current generation of supercomputers provide a great convenience to scientists dealing with extremely large data sets. The next generation of exascale supercomputers could provide accurate simulation results for the automobile industry, aerospace industry, and even nuclear fusion reactors for the very first time. However, the energy cost of super-computing is extremely high, with a total electricity bill of 9 million dollars per year. Thus, conserving energy and increasing the energy efficiency of supercomputers have become critical in recent years. Many researchers have studied this problem and are trying to conserve energy by incorporating the dynamic voltage frequency scaling technique into their methods. However, this approach is limited, especially when the workload is high. In this paper, we developed a power-aware job scheduler by applying a rule-based control method and taking into consideration real-world power and speedup profiles to improve power efficiency while adhering to predetermined power constraints. The intensive simulation results showed that our proposed method is able to achieve the maximum utilization of computing resources as compared to baseline scheduling algorithms while keeping the energy cost under the threshold. Moreover, by introducing a power performance factor based on the real-world power and speedup profiles, we are able to increase the power efficiency by up to 75%.

A power saver scheduling algorithm using DVFS and DNS techniques in cloud computing data centres

Cloud computing is a fascinating and profitable area in modern distributed computing. Aside from providing millions of users the means to use offered services through their own computers, terminals, and mobile devices, cloud computing presents an environment with low cost, simple user interface, and low power consumption by employing server virtualisation in its offered services (e.g., Infrastructure as a Service). The pool of virtual machines found in a cloud computing data centre (DC) must run through an efficient task scheduling algorithm to achieve resource utilisation and good quality of service, thus ensuring the positive effect of low energy consumption in the cloud computing environment. In this paper, we present an energy-efficient scheduling algorithm for a cloud computing DC using the dynamic voltage frequency scaling technique. The proposed scheduling algorithm can efficiently reduce the energy consumption for executing jobs by increasing resource utilisation. GreenCloud simulator is used to simulate our algorithm. Experimental results show that, compared with other algorithms, our algorithm can increase server utilisation, reduce energy consumption, and reduce execution time.

A power saver scheduling algorithm using DVFS and DNS techniques in cloud computing data centres

Cloud computing is a fascinating and profitable area in modern distributed computing. Aside from providing millions of users the means to use offered services through their own computers, terminals, and mobile devices, cloud computing presents an environment with low cost, simple user interface, and low power consumption by employing server virtualisation in its offered services (e.g., Infrastructure as a Service). The pool of virtual machines found in a cloud computing data centre (DC) must run through an efficient task scheduling algorithm to achieve resource utilisation and good quality of service, thus ensuring the positive effect of low energy consumption in the cloud computing environment. In this paper, we present an energy-efficient scheduling algorithm for a cloud computing DC using the dynamic voltage frequency scaling technique. The proposed scheduling algorithm can efficiently reduce the energy consumption for executing jobs by increasing resource utilisation. GreenCloud simulator is used to simulate our algorithm. Experimental results show that, compared with other algorithms, our algorithm can increase server utilisation, reduce energy consumption, and reduce execution time.

A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters

Information and communication technology (ICT) has a profound impact on environment because of its large amount of CO2 emissions. In the past years, the research field of “green” and low power consumption networking infrastructures is of great importance for both service/network providers and equipment manufacturers. An emerging technology called Cloud computing can increase the utilization and efficiency of hardware equipment. The job scheduler is needed by a cloud datacenter to arrange resources for executing jobs. In this paper, we propose a scheduling algorithm for the cloud datacenter with a dynamic voltage frequency scaling technique. Our scheduling algorithm can efficiently increase resource utilization; hence, it can decrease the energy consumption for executing jobs. Experimental results show that our scheme can reduce more energy consumption than other schemes do. The performance of executing jobs is not sacrificed in our scheme. We provide a green energy-efficient scheduling algorithm using the DVFS technique for Cloud computing datacenters.

Optimisation de la consommation mémoire multibanc pour un système multitâches

Several techniques were developed to reduce processor consumption which was the predominant source of dissipation. However with the technology evolution and the development of new applications that makes heavy use of large memory data size, the energy saving obtained by these techniques become limited. In this article we showed that Dynamic Voltage Frequency Scaling technique (DVS) increases the main memory consumption. A multi-banked architecture memory, having the capability of setting banks in low poder mode when they are not accessed and only the accessed bank is maintained in activ mode, is adopted to reduce the memory consumption. An heuristic approach of tasks allocations and banks configuration is developed at system level. Experimental results sho that, when we bomeined DVS technique with an efficient multi-bank architecture, a system energy saving that reaches 35% is obtained.