Low Energy Consumption and Time Deterministic Energy-saving Workflow Task Scheduling Algorithm based on DVFS

Nowadays, the expanding computational capabilities of the cloud system rely on the minimisation of the consumed power to make them sustainable and economically productive. Power management of cloud data centres received a great attention from industry and academia as it consumes high energy and thus increases the operational cost. One of the core approaches for the conservation of energy in the cloud data centre is the task scheduling. This task allocation in a heterogeneous environment is a well known NP-hard problem due to which researchers pay attention for proposing various heuristic techniques for the problem. In this paper, a technique is proposed based on dynamic voltage frequency scaling (DVFS) for optimising the energy consumption in the cloud environment. The basic idea is to address the trade-off between energy consumption and makespan of the system. Here, we formally introduce a model that includes various subsystems and assess the implementation of the algorithm in the heterogeneous environment.

Modern information and communication technology along with the broad adaptation of computing paradigms, namely cloud computing has significant impact on environment due to huge amount of CO 2 emission. In the recent decade, a significant growth in the research domain of “green” and low power consumption network has been observed. With the recent advancements of compute intensive machine learning frameworks like deep learning, the energy consumption by GPUs is a challenging issue. In all of these cases, Dynamic Voltage Frequency Scaling (DVFS) has gained significant research interests and shown encouraging outcome. Dynamic Voltage and Frequency Scaling (DVFS) is utilized to reduce the energy consumption in data centers without impacting the Quality of Service. On the other side, spatio-temporal dataset is huge in size and requires compute and time intensive algorithms and techniques for different spatio-temporal query processing. In all aspects, the correlations among energy, DVFS, consolidation, and performance are the key-enablers of energy-efficient management. In this chapter, we explore different existing DVFS techniques and challenges to analyse and process spatio-temporal query in cloud based paradigm. We have considered movement datasets for demonstrating energy consumption and utility of dynamic voltage and frequency scaling.KeywordsDynamic voltage and frequency scalingSpatio-temporal QueryCloud datacenterScheduling algorithmPowerEnergy

SummaryIn the modern Internet of Things (IoT) era, several applications generate a vast amount of data and that needs to be handled appropriately. The conventional cloud computing system delivers us with enormous resources to manage such voluminous data. Despite that, the growing demands of IoT applications on minimal energy consumption, minimal latency, the privacy of data, data processing based on location, and maximum Quality of Service(QoS) impels the advent of fog computing. As the devices in the fog layer are heterogeneous, distributed, and resource‐constrained, how the fog resources are effectively utilized for executing latency and time‐sensitive applications is a primary challenge. The data generated by the IoT devices are voluminous and produce overhead in network bandwidth during transmission and slow down the response time. This article addresses the task scheduling problem in fog computing to minimize the makespan and energy consumption. The proposed work consists of two phases. In the first phase, the tasks are ordered based on the heuristic method, heterogeneous earliest finish time (HEFT). Then the ordered tasks are scheduled by applying the improved gaining sharing knowledge (IGSK) based algorithm. To reduce the energy consumption dynamic voltage frequency scaling (DVFS) is applied in the proposed scheme. To evaluate the proposed scheduling method the simulations are performed on different workflows with varying sizes. The performance evaluation results exhibit that the proposed work outperforms the task scheduling with similar methods in terms of makespan and energy consumption.

Power awareness has become a critical issue in real-time scheduling of embedded systems. In the context of control applications, the goal of high control performance and low energy consumption are at odds with each other. While dynamic voltage/frequency scaling (DVS) has proved to be promising in energy saving while preserving task schedulability, traditional DVS algorithms use either open loop or ad hoc solutions, and hence cannot perform well for dynamic systems where the workload varies significantly. By targeting these systems, a novel scheme, namely DVS-FS, which combines DVS and feedback scheduling, is suggested. The objective is to save CPU energy as much as possible, while still providing control performance guarantees, which largely depends on successful schedule of the control task set. DVS-FS exploits feedback control methodology, and facilitates tradeoffs between energy consumption and control performance through controlling the CPU utilization at a considerably high level. Simulation experiments demonstrate that DVS-FS can easily reduce significant energy consumption at the expense of only minor control performance degradation.

The research on green cloud computing is indispensable to reduce the emission of carbon as well as to rescue the environment from the hands of global warming, as the climatic change due to the emission of carbon is a critical issue now a days. The green cloud computing mainly concentrates on reducing the amount of cloud resources to condense the emission of carbon. So an effective and efficient scheduling algorithm, Green Task Scheduling (GTS) Algorithm, is proposed in this paper to bring down the amount of cloud resources in use. The proposed algorithm also reduces the hardware cost significantly. As green cloud computing also concentrates to save the energy consumption by providing only the required amount of voltage based on the frequency of work, a technique called as Dynamic Voltage Frequency Scaling (DVFS) is also used in this paper to control the voltage as well as the frequency of the processor without affecting the working performance. The numerical results prove that the makes pan, energy and cost are optimally minimized by implementing GTS along with DVFS in cloud computing environment.

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

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.

DVFS (Dynamic voltage frequency scaling) is one of the most widely used power management technologies employed to improve the performance or minimize the power consumption by controlling voltages and frequencies in real time. When applying device-level DVFS in graphics processing units (GPUs) that support spatial multitasking, it is difficult to determine the optimal DVFS status when multiple running kernels have different characteristics. To solve the problem, we created a GPU simulator that can operate at different streaming multiprocessor frequencies according to the characteristics of the assigned kernel and compared it with a single-clock-based spatial multitasking GPU simulator.

In this paper, we have proposed three new metrics, Speedup per Watt (SPW), Power per Speedup (PPS) and Energy per Target (EPT), to guide task schedulers to select the best task schedules for energy saving in multicore computers. Based on these metrics, we have proposed the novel Sharing Policies, the Hare and the Tortoise Policies, which have taken into account parallelism and Dynamic Voltage Frequency Scaling (DVFS) in their schedules. Our experiments show that, on a modern multicore computer, the Hare Policy can save energy up to 72% in a system with low utilization. On a busier system the Sharing Policy can make a saving up to 20% of energy over standard scheduling policies.

Cloud computing is the combination of grid computing, distributed computing and utility computing. Cloud computing provides various types of services (servers and storage facility) in the on demand basis. The main goal of the cloud computing is to preserve and organize the very huge data center or data forms. The data forms are composed of thousands of servers that absorb the giant (ample) amount of electricity in the word of energy. The decreasing the energy consumption in datacenter is the major challenge in cloud computing now a day. This research article is going to address the problem of high energy consumption at datacenter. Concentrate on virtual machine scheduling in cloud datacenter with Dynamic Voltage Frequency Scaling (DVFS) approach. We have combined shortest job first and Round Robin algorithms with Vibrant Quantum. This combination of algorithm is considered as shortest round vibrant queue (SRVQ) algorithm. SRVQ reduces the waiting time of the scheduling process and minimize the starvation. The DVFS and SRVQ worked together and produced fruitful results in the final experiments. This work reduced the server’s energy consumption in the cloud data center. In the final results, our proposed framework exhibits 45% of energy efficiency compare to other previously proposed algorithms. 33% of QoS performance were enhanced by our framework.

Reducing energy consumption without affecting computational performance is a significant research driver in computer engineering. The Partitioned Global Address Space (PGAS) programming model provides a global address space for ease-of-use while providing locality-awareness for efficient execution. For symmetric multiprocessor (SMP) clusters, PGAS locality-awareness offers opportunities for intelligent energy management due to the inherent latencies in the interconnection network fabric. In this paper, the second in a series that explores PGAS power optimization, we present a detailed examination of how data locality-awareness can be exploited to improve the energy consumption of workload sharing on SMP clusters. We present a systematic methodology for analyzing a given algorithm's potential for locality-aware power optimization. We show how to automate the code modifications for an iterative stencil loop algorithm using a profiler that analyzes the algorithms' non-local access patterns and optimizes the code for thread-local data pre-fetch. We explore the use of dynamic voltage frequency scaling (DVFS) in conjunction with time-based optimizations and provide an extensible and precise power measurement framework for generating the experimental results. We compare optimized and CPU-default approaches to DVFS application at the program-execution and processor architecture level. We show how the latency of the cluster's interconnection network can impact the opportunities for power optimization by comparing the programs' energy/time behavior when the cluster nodes are connected by Ethernet or Infiniband network fabrics. For our representative iterative stencil loop algorithm, locality aware power optimizations show significant improvements in energy efficiency over native code running in default processor power modes.

Emerging trends in applications with the requirement of considerable computational performance and decreasing time-to-market have urged the need of multiprocessor systems. With the increase in number of processor there is an increased demand to efficiently control the energy and power budget of such embedded systems as well. Dynamic voltage frequency scaling (DVFS) strategies permits to control this budget by actively changing the power consumption profile of the system. These techniques exploit the execution times variation of Real-time applications for dynamically adjusting the voltage and frequency of processors in order to reduce power and energy consumption. This paper presents one DVFS strategy called Dynamic slack reclamation (DSR) for real time applications. It is based on detecting early completion of tasks. DSR determines the amount of dynamic slack (if any) by comparing the worst-case execution requirement of a tasks job with its actual execution requirements. Experimental results will be extracted from the simulation of MPSoC architectures using the SoCLib platform; they show that DSR approach gives better power consumption performance.

Allocation problem has always been one of the fundamental issues of building the applications in real-time computing systems. For real-time applications, the allocation problem should directly address the issues of task and communication scheduling. In this context, the allocation of tasks has to fully utilize the available processors and the scheduling of tasks has to meet the specified timing constraints. Clearly, the execution of tasks under the allocation and schedule has to satisfy the precedence, resources, and other synchronization constraints among them. Recently, the timing requirements of the real-time systems emerge that the relative timing constraints are imposed on the consecutive executions of each task and the inter-task temporal relationships are specified across task periods. In this paper we consider the allocation and scheduling problem of the periodic tasks with such timing requirements. Given a set of periodic tasks, we consider the least common multiple (LCM) of the task periods. Each task is extended to several instances within the LCM. The scheduling window for each task instance is derived to satisfy the timing constraints. We develop a simulated annealing algorithm as the overall control algorithm. An example problem of the sanitized version of the Boeing 777 Aircraft Information Management System is solved by the algorithm. Experimental results show that the algorithm solves the problem in a reasonable time complexity.

Power consumption increasingly presents an upper bound on sustainable large scale computing performance and reliability. The Partitioned Global Address Space (PGAS) programming model is a family of parallel programming paradigms with a global address space for ease-of-use while providing locality awareness for efficient execution. Very little exploration has been done to determine the potential of PGAS programming models in improving scalable energy efficient computation for high performance computing (HPC) clusters. This paper examines features of the PGAS programming model that may support predictively reducing power consumption in distributed clusters via dynamic voltage frequency scaling (DVFS). These concepts are tested with Unified Parallel C (UPC) codes running on a cluster of commodity PCs which have been instrumented to measure power at the CPU socket level. We have also explored approaches to automating these power optimization techniques at compile time. Benchmarking results show a tangible reduction in power consumption without impacting the overall execution time of the program.

The use of power management techniques is mandatory in embedded devices, which must provide high performance with low energy consumption. Due to the high variability present in the applications workload executed by these devices, this management should be executed dynamically. The use of traditional dynamic voltage and frequency scaling (DVFS) techniques proved to be useful in several scenarios to save energy. Nonetheless, due to technology scaling that limits the voltage variation and slow response of the DVFS schemes, the use of such technique may become inadequate. As alternative, the use of dynamic frequency scaling (DFS) may provide a good trade-off between power savings and power overhead. This paper proposes a distributed DFS scheme for NoC-based MPSoCs. Both NoC and PEs have an individual controlling scheme. The DFS scheme for PEs takes into account its computation and communication load to dynamically change the operating frequency. In the NoC, the DFS controller uses packet information to decide the router operating frequency. Real and synthetic applications were used to evaluate the proposed scheme. Results show that the number of executed instructions is reduced up to 41%, with an execution time overhead up to 18%. The power dissipation is reduced in PEs up to 26% and in the NoC up to 76%.