Scheduling algorithms for optimizing the tradeoffs between delay, queue size and energy

Nowadays, chip multi-processors (CMP) are being widely used in embedded systems due to their superior performance for parallel computing. However, they also incur significantly large power consumption. To solve this issue, designers of embedded operating system must provide an efficient thread scheduling algorithm, which not only maximizes the system performance, but also minimizes the energy consumption. Further, if the scheduler makes decisions without considering the precedence relationships among threads, the decisions could conflict with the thread behavior which could result in poor performance and large energy consumption. In this paper, we propose a synchronization-aware dynamic thread scheduling algorithm (SA), which reduces the busy-waiting time caused by spinlock, and with performance improvement and energy saving. SA has two major objectives, including (1) overall high performance, in terms of less completion time and less turnaround time, and (2) low energy consumption for all threads. The experimental results show that SA indeed improves performance and reduces energy consumption compared to the original scheduling algorithm of Linux kernel version 2.6. In a real-world case, the Digital Video Recording (DVR) system, SA achieves performance speedup by at most 1.21 and saves the energy consumption by at most 28.6%.

Objectives: Current state-of-the-art task scheduling algorithms were mainly focused on deadline, load and energy factors in centralized cloud context. So, the proposed research objective focuses on dynamic and decentralized context. Methods/Statistical Analysis: Multi-objective task scheduling has become an important criterion for the dynamic and decentralized nature of cloud environment. Moreover, existing research works assumes that the resource load, energy and task execution time are known due its homogeneous nature. In order to improve the cloud consumer’s satisfaction, a novel Locality-Load-Prediction Aware Multi-objective Task Scheduling (LLPAMTS) algorithm is proposed to eventually distribute the tasks according to dynamic nature of cloud virtual machines. Findings: Proposed LLPAMTS algorithm will effectively schedule the tasks in an optimized manner by VM Scheduler component. This scheduling algorithm exploits the various monitoring parameters like locality, load and prediction parameters. It outperforms the existing deadline, load and energy aware scheduling algorithms in terms of task transfer time, task waiting time, task execution time, and task completion time. Applications/Improvements: The proposed LLPAMTS algorithm provides an average of 5 to 10% less task completion time compared to the existing deadline, load and energy aware scheduling algorithms. Keywords: Cloud Environment, Heterogeneous Cloud, Locality-Load-Prediction Aware Scheduling, Multi-Objective, Task Scheduling

In the past few years, with the rapid development of heterogeneous computing systems (HCS), the issue of energy consumption has attracted a great deal of attention. How to reduce energy consumption is currently a critical issue in designing HCS. In response to this challenge, many energy-aware scheduling algorithms have been developed primarily using the dynamic voltage-frequency scaling (DVFS) capability which has been incorporated into recent commodity processors. However, these techniques are unsatisfactory in minimizing both schedule length and energy consumption. Furthermore, most algorithms schedule tasks according to their average-case execution times and do not consider task execution times with probability distributions in the real-world. In realizing this, we study the problem of scheduling a bag-of-tasks (BoT) application, made of a collection of independent stochastic tasks with normal distributions of task execution times, on a heterogeneous platform with deadline and energy consumption budget constraints. We build execution time and energy consumption models for stochastic tasks on a single processor. We derive the expected value and variance of schedule length on HCS by Clark's equations. We formulate our stochastic task scheduling problem as a linear programming problem, in which we maximize the weighted probability of combined schedule length and energy consumption metric under deadline and energy consumption budget constraints. We propose a heuristic energy-aware stochastic task scheduling algorithm called ESTS to solve this problem. Our algorithm can achieve high scheduling performance for BoT applications with low time complexity $O(n(M+\log n))$ , where $n$ is the number of tasks and $M$ is the total number of processor frequencies. Our extensive simulations for performance evaluation based on randomly generated stochastic applications and real-world applications clearly demonstrate that our proposed heuristic algorithm can improve the weighted probability that both the deadline and the energy consumption budget constraints can be met, and has the capability of balancing between schedule length and energy consumption.

Cloud computing is growing massively in the world of information technology. As a result of which the number of servers and virtual machines is increasing day by day. It leads to huge energy consumption and carbon emission. In this regard, task scheduling has drawn the attention of cloud service providers to reduce energy consumption and increase cloud utilization. This is a crucial issue in a heterogeneous cloud environment. In this paper, energy consumption issues of cloud datacenters are addressed and an Energy-Aware Cloud Task Scheduling (EACTS) algorithm is proposed. It is taking the concept from traditional min-min, max-min, and sufferage heuristics and merge with the energy model. These heuristics are implemented on the heterogeneous cloud environment. EACTS energy model focuses on the estimation of energy consumption in cloud datacenters. The proposed algorithms estimated the makespan, cloud utilization, and energy consumption for a benchmark dataset. The experimental results obtained from EACTS algorithm provides a valid arbitration between energy efficiency and makespan. It has shown the comparison between above mentioned algorithms for various scheduling parameters.

Improving energy efficiency has become necessary to enable sustainable computational science. At the same time, scientific workflows are key in facilitating distributed computing in virtually all domain sciences. As data and computational requirements increase, I/O-intensive workflows have become prevalent. In this work, we evaluate the ability of twopopular energy-aware workflow scheduling algorithms to provide effective schedules for this class of workflow applications, that is, schedules that strike a good compromise between workflow execution time and energy consumption. These two algorithms make decisions based on a widely used power consumption model that simply assumes linear correlation to CPU usage. Previous work has shown this model to be inaccurate, in particular for modeling power consumption of I/O-intensive workflow executions, and has proposed an accurate model. We evaluate the effectiveness of the two aforementioned algorithms based on this accurate model. We find that, when making their decisions, these algorithms can underestimate power consumption by up to 360%, which makes it unclear how well these algorithm would fare in practice. To evaluate the benefit of using the more accurate power consumption model in practice, we propose a simple scheduling algorithm that relies on this model to balance the I/O load across the available compute resources. Experimental results show that this algorithm achieves more desirable compromises between energy consumption and workflow execution time than the two popular algorithms.KeywordsScientific workflowsEnergy-aware computingWorkflow schedulingWorkflow simulation

Machine-to-machine (M2M) communications protocols, e.g., Machine Type Communications (MTC) in 3GPP LTE, support a variety of Machine to Machine (M2M) applications, which requires real-time data reporting. Many uplink scheduling algorithms are primarily designed for human-to-human applications rather than for M2M applications. These scheduling algorithms typically are based on the fact that the machines in MTC are battery-powered and aim to reduce the energy consumption. We notice that the current literature considers either the problem of sleep-time maximization or transmission power minimization, but not both. In this paper, we systematically consider all energy-consumption factors of M2M real-time reporting. Using MTC in LTE as an example for M2M communications, in this paper, we propose two energy-efficient scheduling algorithms to minimize the total energy consumption of machines for M2M communications. The two proposed algorithms utilize the distance between a machine and the serving eNB (i.e., base station in LTE). Our performance study shows that our algorithms have performance enhancement in terms of energy consumption compared to other algorithms in the previous works. Furthermore, our algorithms can maintain high scheduling success ratio and fairness.

Cloud computing is the promising technology to support virtualization, resource management and provide services to IaaS, PaaS, SaaS. While cloud provides many features but still it has some short comings like energy consumption between clouds. So the major objective of the cloud computing is to provide the reliable service keeping minimum energy utilization. This paper focuses on analyzing the scheduling methods that can minimize the energy consumption. The round robin, FCFS and Priority based scheduling algorithms focuses on distributing the jobs in evenly manner, which in turn reduces the energy consumption. But they reduce energy consumption to some extent, so crossbreed algorithm has been developed in this work to minimize the energy consumption. The result shows that Crossbreed algorithm conserves more energy in comparison to individual scheduling algorithms.

Energy saving and emission reduction are of great importance for the development of transportation. Therefore, researchers should conduct precise measurements on energy consumption caused by vehicles to support the applications of green navigation. In most cases, energy consumption are resulted by vehicles driving on the roads, of which are numerous and great mobility and hard to be directly collected and monitored through energy-consumption sensing devices. So a lot of scholars used a model of vehicles' energy consumption based on computer technology and established a historical database through analyzing historic data to support the measurements of energy consumption. On hand, previous studies on energy-consumption models would collect a large amount of samples despite of the accuracy of data. And on the other hand, different structures of different road conditions which may influence the generation of energy consumption and result in the secondary loss of accurate data were neglected as well. Aimed at the problems mentioned above, I would like to set forth and conduct a measurement of energy consumption based on computer technologies and combined with a real-time measurement of city roads of RBFNN, which would measure different road sections. The traffic network of Beijing city would be chose as an example to establish an energy-consumption model of transportation and to offer support for the government to monitor and assess a traffic management strategy which is oriented at saving energy and reducing emission, as well as offer technical support for the application of intelligent traffic system of energy saving and emission reduction.

As sustainability related issues, such as energy consumption and environmental impact issues are becoming a more integrated part of operational and long-term planning decisions, simulation modeling and analysis tools are needed to aid in the decision-making process. In this paper we introduce the concept of a simulation-based sustainability toolkit and present a prototype of one portion of the toolkit that is being developed for modeling and simulating sustainability aspects of logistics and transportation systems. The sustainability simulation toolkit contains a flexible framework to enable the simulation modeling and analysis of sustainability-related factors and performance measures. This toolkit is designed for ease of implementation, so decision-makers can have sustainability measures as readily available as traditional performance measures when making logistics and transportation decisions.

At present, Multi-platform Avionics System (MPA) has been widely used. The existing adaptive scheduling algorithm based on Sliding-Scheduled Tenant (SST) simulates and verifies the resource management and task scheduling of MPA, and analyzes the task requirements of MPA. However, due to the shortcomings of SST algorithm in considering energy consumption and other aspects, it reduces the task acceptance rate, and does not consider the limitations of sensors and priorities, which makes al algorithm unable to meet the requirements of avionics system. This paper proposes a method of system resource selection, which considers the energy consumption, sensor and priority limit, so as to improve the acceptance rate of tasks, improve the acceptance rate of high priority, and get a scheduling algorithm with high acceptance rate of tasks. Finally, through the comprehensive analysis of the experimental results and experimental results in different scenes, it is shown that the algorithm proposed in this paper outperforms the existing algorithm in terms of the acceptance rate.

In smart grid, residential consumers adopt different load scheduling methods to manage their power consumption with specific objectives. The conventional load scheduling methods includes direct load control and demand bidding. This paper proposes a residential scheduling algorithm. The scheduling model can be integrated with the home energy scheduler interconnected to the smart meter, which facilitates the flow of price and energy consumption information to and from the household premise. Price-based demand response is modeled through fixed pricing and dynamic pricing scheme. The objective of proposed scheduling algorithm is to achieve minimum energy consumption cost to obtain periodic load schedule. The proposed scheduling is capable of scheduling the required power consumption for appliances according to their individual power consumption pattern. The proposed objective is solved using linear integer programming. For validation purpose simulation results show that the scheduling algorithm can reflect change and impact users consumption behavior and accomplish the improved energy consumption profile.

The energy consumption increases rapidly with the economic development and population change. Buildings are becoming the most important energy consumption sector in Shanghai. The gross domestic product per capita in Shanghai has reached to $13,500 in 2012 and the energy consumption is also amounted to 11,000×104ton of coal equivalent. Buildings energy consumption accounted for about 25% of the total energy consumption. Building energy saving will greatly contribute to the energy saving and environmental protection. By analyzing the trend of population growth, economic development and energy consumption and the present buildings structure and situation in Shanghai, this paper proposed the measurements of building energy saving and energy consumption reduction.

We study route selection for packet switching in the competitive throughput model. In contrast to previous papers which considered competitive algorithms for packet scheduling, we consider the packet routing problem (output port selection in a node). We model the node routing problem as follows: a node has an arbitrary number of input ports and an arbitrary number of output queues. At each time unit, an arbitrary number of new packets may arrive, each packet is associated with a subset of the output ports (which correspond to the next edges on the allowed paths for the packet). Each output queue transmits packets in some arbitrary manner. Arrival and transmission are arbitrary and controlled by an adversary. The node routing algorithm has to route each packet to one of the allowed output ports, without exceeding the size of the queues. The goal is to maximize the number of the transmitted packets. In this paper, we show that all non-refusal algorithms are 2-competitive. Our main result is an almost optimal $\frac{e}{e-1} \approx 1.58$-competitive algorithm, for a large enough queue size. For packets with arbitrary values (allowing preemption) we present a 2-competitive algorithm for any queue size.

Nowadays, big data analytics has been widely applied in addressing the growing cybercrime threats. However, energy consumption is explosive increasing with the fast growth of big data processing in anti-cybercrime. In this paper, an energy-efficient framework for big data applications is proposed to reduce energy consumption while satisfying deadline constrains. First, the problem of energy-efficient tasks scheduling of a single Spark job is modeled as an integer program. We design an energy-efficient tasks scheduling algorithm to minimize the energy consumption for big data application in Spark. To avoid service-level agreement violations for execution time, we propose an optimal task scheduling algorithm with deadline constrains by tradingoff execution time and energy consumption. Experiments on a Spark cluster are performed to determine the energy consumption and execution time for several workloads from the HiBench benchmark suite. Our algorithms consume less energy on average than FIFO and FAIR under deadlines. The optimal algorithm is able to find near optimal tasks schedules to trade off energy consumed and response time benefit in small shuffle partitions.

The article is devoted to the analysis of the potential for increasing the efficiency of electricity consumption in residential and administrative buildings in the presence of various energy sources and storage systems. This task is relevant in view of the spread of renewable energy sources both in centralized power supply systems and at local facilities and the significant impact of unstable generation on the cost of electricity in the market. In particular, the main consumers of electricity, which can be used as "consumers-regulators", as well as storage systems available for use in buildings, are considered. Taking into account that a significant amount of electricity is used or can be used in building air-conditioning systems, as well as for the needs of hot water supply, the construction of mathematical models of such systems for existing buildings will allow the creation of effective electrical load management systems to optimize energy costs for traditional buildings and ensure autonomy for buildings with close to zero energy consumption. The article describes the priority tasks to be solved to build an energy management system to minimize energy costs and increase the efficiency of the country's energy system and local energy systems of communities. The formulation of the tasks follows from the analysis of information on the actual consumption schedules of residential and public buildings and the analysis of their mutual correlation, which allows using these findings to develop algorithms for monitoring energy consumption and load management on the consumer side. Further stages of the study include additional measurements of actual energy consumption at the facilities with a discreteness of several seconds to an hour, while simultaneously recording potential influencing factors, which will allow creating a digital model of building energy consumption. A preliminary analysis of the research shows that the most promising mathematical apparatus for building such a model is the use of neural networks.