Energy-efficient Task Scheduling Research Articles

HIGA: Harmony-inspired genetic algorithm for rack-aware energy-efficient task scheduling in cloud data centers

Reducing energy consumption in cloud data centers is one of the prime issue in the cloud community. It reduces energy related costs and increases lifespan of high performance computing resources deployed in cloud data centers and it also helps in reducing carbon emissions. Along with energy efficiency, problem of task scheduling is also one of the important problem considered in cloud data centers and it belongs to NP-class problems. With the energy consumption consideration, problem of task scheduling becomes more complex to solve. Metaheuristic algorithms are proven to generate near optimal solutions for task scheduling problem but their scheduling overhead increases vastly as the number of tasks or number of resources increases. In addition to this, metaheuristic-based scheduling algorithms lives most of time in local-optimal region of solution space and it is possible that it can finalizes the solution in the local-optimal region only. Primary motivation behind this work is from genetic algorithm itself, we have experimented with genetic algorithm in many ways to solve the problem of energy efficient task scheduling and come up with new hybrid scheme to solve it. In this work, we tackles the problem of energy efficient task scheduling on modern cloud data center architecture and proposes a novel hybrid metaheuristic scheme harmony-inspired genetic algorithm (HIGA). It also addresses issues associated with metaheuristic algorithm. HIGA combines the exploration capability of genetic algorithm and exploitation capability of harmony search by which it intelligently senses local as well as global optimal region without wasting time (iterations) in local or global optimal region and provides quick convergence. Our primary objectives in this work are to reduce makespan and computing energy and secondary objectives are to reduce the energy consumed by the resources other than computing resources and reduce execution overhead associated with scheduler. Collectively these objectives guides HIGA for better energy efficiency and performance while reducing the number of required resources (i.e. active racks). It indirectly also reduces cooling energy as we can switch off the rack components (air blower, cooling inlets) once racks become idle. Simulation analysis has been performed over independent task applications as well as real-world scientific applications like CyberShake, Epigenomics and Montage. The result clearly manifests that proposed HIGA provides by up to 33% of energy savings and 47% of improvement in application performance (makespan) that too with 39% less execution overhead.

Energy-Efficient Scheduling of Real-Time Tasks in Reconfigurable Homogeneous Multicore Platforms

This paper deals with real-time scheduling of homogeneous multicore platforms powered by a battery to be periodically recharged. A system is composed of reconfigurable real-time dependent and periodic tasks to be assigned to different cores interconnected by a network-on-chip (NoC). The system is subject to reconfigurations, which are automatic operations allowing the addition and/or removal of tasks as well as their exchanged messages on the NoC. Consequently, any reconfiguration can violate real-time and energy constraints on cores as well as the NoC when the energy is unavailable until the next recharge. A novel periodic task model based on elastic coefficients and a new scheduling strategy are proposed to compute useful temporal parameters allowing for tasks and messages to meet the related constraints while controlling the communication cost on the NoC. This strategy is compared with an integer linear programming-based optimal solution, and a tool named OptimalMappingTasks is developed to run different simulations that prove the originality of this paper’s contribution.

MEETS: Maximal Energy Efficient Task Scheduling in Homogeneous Fog Networks

A homogeneous fog network is defined as a group of peer nodes with sharable computing and storage resources, as well as spare spectrum for node-to-node/device-to-device communications and task scheduling. It promotes more intelligent applications and services in different Internet of Things (IoT) scenarios, thanks to effective collaborations among neighboring fog nodes via cognitive spectrum access techniques. In this paper, a comprehensive analytical model that considers circuit, computation, offloading energy consumptions is developed for accurately evaluating the overall energy efficiency (EE) in homogeneous fog networks. With this model, the tradeoff relationship between performance gains and energy costs in collaborative task offloading is investigated, thus enabling us to formulate the EE optimization problem for future intelligent IoT applications with practical constraints in available computing resources at helper nodes and unused spectrum in neighboring environments. Based on rigorous mathematical analysis, a maximal energy-efficient task scheduling (MEETS) algorithm is proposed to derive the optimal scheduling decision for a task node and multiple neighboring helper nodes under feasible modulation schemes and time allocations. Extensive simulation results demonstrate the tradeoff relationship between EE and task scheduling performance in homogeneous fog networks. Compared with traditional task scheduling strategies, the proposed MEETS algorithm can achieve much better EE performance under different network parameters and service conditions.

Optimal task execution speed setting and lower bound for delay and energy minimization

The current technology trend reveals that static power consumption is growing at a faster rate than dynamic power consumption. In this paper, energy-efficient task scheduling is studied when static power consumption is a significant part of energy consumption which cannot be ignored. The problems of scheduling a set of independent sequential tasks on identical processors so that the schedule length is minimized for a given energy consumption constraint or the energy consumption is minimized for a given schedule length constraint are investigated. For a given schedule, the optimal task execution speed setting for delay and energy minimization is found analytically. Lower bounds for the minimum schedule length of a set of tasks with a given energy consumption constraint and the minimum energy consumption of a set of tasks with a given schedule length constraint are established. Our lower bounds are applicable to sequential or parallel, and independent or precedence constrained tasks, on processors with discrete or continuous speed levels, and bounded or unbounded speed ranges. The significance of these lower bounds is that they can be used to evaluate the performance of any heuristic algorithms when compared with optimal algorithms. Experimental study on the performance of list scheduling algorithms is performed and it is shown that their performance is very close to the optimal. To the best of the author’s knowledge, this is the first paper that provides such analytical results for energy-efficient task scheduling with both dynamic and static power consumptions.

An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Network and Overloads

<p>Energy consumption in cloud computing occur due to the unreasonable way in which tasks are scheduled. So energy aware task scheduling is a major concern in cloud computing as energy consumption results into significant waste of energy, reduce the profit margin and also high carbon emissions which is not environmentally sustainable. Hence, energy efficient task scheduling solutions are required to attain variable resource management, live migration, minimal virtual machine design, overall system efficiency, reduction in operating costs, increasing system reliability, and prompting environmental protection with minimal performance overhead. This paper provides a comprehensive overview of the energy efficient techniques and approaches and proposes the energy aware resource utilization framework to control traffic in cloud networks and overloads.</p>

Soft error-aware energy-efficient task scheduling for workflow applications in DVFS-enabled cloud

Abstract Dynamic Voltage and Frequency Scaling (DVFS) has been widely used as a promising power management method to reduce the energy consumption of cloud workflows. However, due to the increasing chip density, lowering CPU voltages improperly in cloud data centers may inevitably increase soft error rate during workflow execution. Consequently, failures of timely completion of workflow applications may often take place, which raises serious concerns during the operation and maintenance of cloud data centers. To address such a problem, this paper proposes a soft error-aware energy-efficient task scheduling approach for workflow applications in DVFS-enabled cloud data centers. Under reliability and completion time constraints requested by tenants, our approach can generate energy-efficient task schedules for workflows by allocating tasks to appropriate virtual machines with specific operating frequencies. Comprehensive experiments on various well-known scientific workflow benchmarks show the effectivenss of our approach. Compared with state-of-the-art methods, our approach can achieve more than 30% energy savings while satisfying both reliability and completion time requirements.

Energy Aware Task Assignment with Cost Optimization in Mobile Cloud Computing

In this paper, we are investigating the power consumption of mobile device while performing offloading system. The offloading system is way in which mobile application can be divided into local and remote execution in order to alleviate the CPU energy consumption. However, existing offloading systems do not consider data transfer communication energy while performing mobile offloading system. They have just focused on mobile CPU energy consumption. In this paper, we are investigating the energy consumption mobile CPU and communication energy collaboratively while performing mobile offloading for complex application. To cope up with the above problem, we have proposed Energy Efficient Task Scheduler (EETS) algorithm, whose aim is to determine optimal tasks execution in offloading system in order to minimize mobile CPU and communication energy. Simulation results show that EETS outperforms as compared to baseline approaches.

Energy Efficient Task Scheduling in Cloud Environment

Cloud computing is a style of computing in which dynamically scalable and other virtualized resources are provided as a service over the Internet. The energy consumption and makespan associated with the resources allocated should be taken into account. This paper focuses on task scheduling using Clonal Selection Algorithm (TSCSA) to optimize energy and processing time. The result obtained by TSCSA was simulated by an open source cloud platform (CloudSim). Finally, the results were compared to existing scheduling algorithms and found that the proposed algorithm (TSCSA) provide an optimal balance results for multiple objectives

An evolutionary scheduling approach for trading-off accuracy vs. verifiable energy in multicore processors

Abstract This work addresses the problem of energy-efficient scheduling and allocation of tasks in multicore environments, where the tasks can allow a certain loss in accuracy in the output, while still providing proper functionality and meeting an energy budget. This margin for accuracy loss is exploited by using computing techniques that reduce the work load, and thus can also result in significant energy savings. To this end, we use the technique of loop perforation, that transforms loops to execute only a subset of their original iterations, and integrate this technique into our existing optimization tool for energy-efficient scheduling. To verify that a schedule meets an energy budget, both safe upper and lower bounds on the energy consumption of the tasks involved are needed. For this reason, we use a parametric approach to estimate safe (and tight) energy bounds that are practical for energy verification (and optimization applications). This approach consists in dividing a program into basic (‘branchless’) blocks, establishing the maximal (resp. minimal) energy consumption for each block using an evolutionary algorithm, and combining the obtained values according to the program control flow, by using static analysis to produce energy bound functions on input data sizes. The scheduling tool uses evolutionary algorithms coupled with the energy bound functions for estimating the energy consumption of different schedules. The experiments with our prototype implementation were performed on multicore XMOS chips, but our approach can be adapted to any multicore environment with minor changes. The experimental results show that our new scheduler enhanced with loop perforation improves on the previous one, achieving significant energy savings (31% on average for the test programs) for acceptable levels of accuracy loss.

Energy constrained scheduling of stochastic tasks

Energy-efficient scheduling of stochastic tasks is considered in this paper. The main characteristic of a stochastic task is that its execution time is a random variable whose actual value is not known in advance, but only its probability distribution. Our performance measures are the probability that the total execution time does not exceed a given bound and the probability that the total energy consumption does not exceed a given bound. Both probabilities need to be maximized. However, maximizations of the two performance measures are conflicting objectives. Our strategy is to fix one and maximize the other. Our investigation includes the following two aspects, with the purpose of maximizing the probability for the total execution time not to exceed a given bound, under the constraint that the probability for the total energy consumption not to exceed a given bound is at least certain value. First, we explore the technique of optimal processor speed setting for a given set of stochastic tasks on a processor with variable speed. It is found that the simple equal speed method (in which all tasks are executed with the same speed) yields high quality solutions. Second, we explore the technique of optimal stochastic task scheduling for a given set of stochastic tasks on a multiprocessor system, assuming that the equal speed method is used. We propose and evaluate the performance of several heuristic stochastic task scheduling algorithms. Our simulation studies identify the best methods among the proposed heuristic methods.

An Energy-Efficient Task Scheduling for Near-Realtime Systems with Execution Time Variation

An Energy-Efficient Task Scheduling for Near-Realtime Systems with Execution Time Variation

An energy-efficient task scheduling for mobile devices based on cloud assistant

Mobile cloud computing is an emerging service model to extend the capability and the battery life of mobile devices. Mostly one network application can be decomposed into fine-grained tasks which consist of sequential tasks and parallel tasks. With the assistance of mobile cloud computing, some tasks could be offloaded to the cloud for speeding up executions and saving energy. However, the task offloading results in some additional cost during the communication between cloud and mobile devices. Therefore, this paper proposes an energy-efficient scheduling of tasks, in which the mobile device offloads appropriate tasks to the cloud via a Wi-Fi access point. The scheduling aims to minimize the energy consumption of mobile device for one application under the constraint of total completion time. This task scheduling problem is reconstructed into a constrained shortest path problem and the LARAC method is applied to get the approximate optimal solution. The proposed energy-efficient strategy decreases 81.93% of energy consumption and 25.70% of time at most, compared with the local strategy. Moreover, the applicability and performance of the proposed strategy are verified in different patterns of applications, where the time constraint, the workload ratio between communication and computation are various.

Energy-efficient task scheduling and consolidation algorithm for workflow jobs in cloud

The energy consumption of underlying cloud hardware has dramatically increased. The cloud service providers need to adopt some cost-effective and energy-aware job scheduler without compromising the quality of service QoS specified in the service level agreement SLA. Based on a rigorous mathematical model, we formulate an energy efficient problem to improve the resource utilisation for high system throughput. A multiple-procedure heuristic workflow scheduling and consolidation strategy is proposed with objectives to maximise the resource utilisation and minimise the power. Several techniques have been utilised including dynamic voltage and frequency scaling DVFS with task module migration for workload balance and task consolidation for virtual machine VM overhead reduction. The simulation results illustrate that our approach consistently achieves a lower power consumption and higher resource utilisation rate within the execution time bound compared with other similar scheduling algorithms as well as our previous algorithm without the task migration based on VM threshold.

Energy-efficient task scheduling and consolidation algorithm for workflow jobs in cloud

The energy consumption of underlying cloud hardware has dramatically increased. The cloud service providers need to adopt some cost-effective and energy-aware job scheduler without compromising the quality of service (QoS) specified in the service level agreement (SLA). Based on a rigorous mathematical model, we formulate an energy efficient problem to improve the resource utilisation for high system throughput. A multiple-procedure heuristic workflow scheduling and consolidation strategy is proposed with objectives to maximise the resource utilisation and minimise the power. Several techniques have been utilised including dynamic voltage and frequency scaling (DVFS) with task module migration for workload balance and task consolidation for virtual machine (VM) overhead reduction. The simulation results illustrate that our approach consistently achieves a lower power consumption and higher resource utilisation rate within the execution time bound compared with other similar scheduling algorithms as well as our previous algorithm without the task migration based on VM threshold.

IASA: an energy-efficient scheduling algorithm for real-time tasks with lock-free objects

In this paper, we are interested in the energy-efficient scheduling of real-time tasks which may access lock-free objects in a non-ideal DVS processor. Compared to conventional lock-based real-time synchronisation protocols, lock-free objects have the advantage of the avoidance of priority inversion and deadlock. However, because lock-free objects permit concurrent operations to interfere with each other, the access to a lock-free object might take an arbitrarily long time to complete owing to repeated interferences. In this paper, we propose an algorithm, called interference-aware speed assignment (IASA), for scheduling of real-time tasks with lock-free objects. The IASA assigns proper execution speeds to tasks scheduled by the earliest deadline first (EDF) scheduling policy. The execution speeds are calculated based on the schedulability analysis of the EDF policy with the cost of the interferences of lock-free objects. As a result, the energy consumption can be reduced while meeting the timing constraints of real-time tasks.

Energy-efficient task scheduling for multi-core platforms with per-core DVFS

Energy-efficient task scheduling is a fundamental issue in many application domains, such as energy conservation for mobile devices and the operation of green computing data centers. Modern processors support dynamic voltage and frequency scaling (DVFS) on a per-core basis, i.e., the CPU can adjust the voltage or frequency of each core. As a result, the core in a processor may have different computing power and energy consumption. To conserve energy in multi-core platforms, we propose task scheduling algorithms that leverage per-core DVFS and achieve a balance between performance and energy consumption. We consider two task execution modes: the batch mode, which runs jobs in batches; and the online mode in which jobs with different time constraints, arrival times, and computation workloads co-exist in the system.For tasks executed in the batch mode, we propose an algorithm that finds the optimal scheduling policy; and for the online mode, we present a heuristic algorithm that determines the execution order and processing speed of tasks in an online fashion. The heuristic ensures that the total cost is minimal for every time interval during a task’s execution. Furthermore, we analyze and derive algorithms with low time complexity for each mode.

Energy Efficient Speed Scaling and Task Scheduling for Distributed Computing Systems

With the rapid development of the performance of computer systems, energy consumption has been increasing dramatically as well. Energy efficiency has been paid much attention, especially in large-scale distributed computing systems. We propose an effective approach for energy reduction, by dynamic speed scaling and task scheduling simultaneously. Markov models for distributed computing systems are proposed, and detailed analyses of the models are provided. Markov decision processes (MDP) are applied for problem formulation, and MDP algorithms for obtaining the optimal solutions are introduced. The efficacy of our approach is further validated by simulation experiments.

Energy-efficient scheduling of real-time tasks with shared resources

This paper explores the energy-efficient scheduling of real-time tasks on a non-ideal DVS processor in the presence of resource sharing. We assume that tasks are periodic, preemptive and may access to shared resources. When dynamic-priority and fixed-priority scheduling are considered, we use the earliest deadline first (EDF) algorithm and the rate monotonic (RM) algorithm to schedule the given set of tasks. Based on the stack resource policy (SRP), we propose an approach, called blocking-aware two-speed (BATS) algorithm, to synchronize the tasks with shared resources and to calculate appropriate execution speeds so that the shared resources can be accessed in a mutual exclusive manner and the energy consumption can be reduced. Particularly, BATS uses a static low speed to execute tasks initially, and then it switches to a high speed dynamically whenever a task blocks a higher priority task. More specifically, the processor runs at the high speed from the beginning of the blocking until the deadline of the blocked task or the processor becomes idle. In order to guarantee that the deadlines of tasks are met, the static low speed and the dynamic high speeds are derived based on the theoretical analysis of the schedulability of tasks. Compared with existing work, BATS achieves more energy saving because its dynamic high speeds are lower than that of existing work and the processor has less chance to execute tasks at the high speeds. The schedulability analysis and the properties of our proposed BATS are provided in this paper. We also evaluated the capabilities of BATS by a series of experiments, for which we have some encouraging results.

Greedy scheduling of tasks with time constraints for energy-efficient cloud-computing data centers

In this paper, we introduce a model of task scheduling for a cloud-computing data center to analyze energy-efficient task scheduling. We formulate the assignments of tasks to servers as an integer-programming problem with the objective of minimizing the energy consumed by the servers of the data center. We prove that the use of a greedy task scheduler bounds the constraint service time whilst minimizing the number of active servers. As a practical approach, we propose the most-efficient-server-first task-scheduling scheme to minimize energy consumption of servers in a data center. Most-efficient-server-first schedules tasks to a minimum number of servers while keeping the data-center response time within a maximum constraint. We also prove the stability of most-efficient-server-first scheme for tasks with exponentially distributed, independent, and identically distributed arrivals. Simulation results show that the server energy consumption of the proposed most-efficient-server-first scheduling scheme is 70 times lower than that of a random-based task-scheduling scheme.

Power Efficient Scheduling Scheme based on PSO for Real Time Systems

Power efficient design of real-time embedded systems based on multi-processors becomes more important as system functionality is increasingly realized through heuristic approaches. This paper targets energy-efficient scheduling of tasks over multiple processors, where tasks share a common deadline. It addresses the problem of energy-aware static partitioning of periodic real time tasks on heterogeneous multiprocessor platforms. A modified Particle Swarm Optimization variant based on priority assignment and minmin algorithms for task partitioning is proposed. The proposed approach aims to minimize the overall energy consumption, meanwhile avoid deadline violations. An energy-aware cost function is proposed to be considered in the proposed approach. Extensive simulated experiments and comparisons with related approaches are conducted in order to validate the effectiveness of the proposed technique. The achieved results demonstrate that the proposed partitioning scheme significantly outperforms in terms of the number of executed iterations to accomplish a specific task in addition to the energy savings.