Energy-Aware Scheduling for Tasks with Target-Time in Blockchain based Data Centres

Cloud computing infrastructures have intended to provide computing services to end-users through the internet in a pay-per-use model.The extensive deployment of the Cloud and continuous increment in the capacity and utilization of data centers(DC)leads to massive power consumption.This intensifying scale of DCs has made energy consumption a critical concern.This paper emphasizes the task scheduling algorithm by formulating the system model to minimize the makespan and energy consumption incurred in a data center.Also,an energy-aware task scheduling in the Blockchain-based data center was proposed to offer an optimal solution that minimizes makespan and energy consumption.The estab-lished model was analyzed with a target-time responsive precedence scheduling algorithm.The observations were analyzed and compared with the traditional scheduling algorithms.The outcomes exhibited that the developed solution incurs better performance with a response to resource utilization and decreasing energy consumption.The investigation revealed that the applied strategy considerably enhanced the effectiveness of the designed schedule.

An Energy-Aware Clustering Approach for Wireless Sensor Networks

Energy conservation is an essential and critical requirement for a wireless sensor network with battery oper-ated nodes intended for long term operations. Prior work has described different approaches to routing protocol designs that achieve energy efficiency in a wireless sensor network. Several of these works involve variations of mote-to-mote routing (flat routing) while some make use of leader nodes in clusters to perform routing (hierarchical routing). A key question then arises as to how the performance of an energy-aware, flat routing protocol compare with that of one based on hierarchical routing. This paper demonstrates a hierarchical routing protocol design that can conserve significant energy in its setup phase as well as during its steady state data dissemination phase. This paper describes the design of this protocol and evaluates its performance against existing energy-aware flat routing protocols. Simulation results show that it exhibits competitive performance against the flat routing protocols.

An Energy-aware Hierarchical Architecture Design Scheme

Compared with the flat architecture in the design of sensor networks, the hierarchical architecture gains much attractive for the reason of scalability, management and energy efficiency. In order to distribute the energy evenly, nodes act the cluster head in some orders. The existing approaches don’t pay a critical attention to the overhead during the role rotations. And the duration of a round is a priori, which is very application-specific. An energy-aware hierarchical architecture design scheme is put forward in this paper, namely, Adaptive Minimum Rotational Cost (AMRC) cluster formation scheme. The decision of beginning a new round is made adaptively by the cluster head itself. It combines the dynamic and static advantages in the clustering architecture. The simulation results demonstrate AMRC outperforms some other clustering protocols in many aspects.

Decomposition-Based Multi-Objective Optimization for Energy-Aware Distributed Hybrid Flow Shop Scheduling with Multiprocessor Tasks

This paper addresses the Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Multiprocessor Tasks(EADHFSPMT)by considering two objectives simultaneously,i.e.,makespan and total energy consumption.It consists of three sub-problems,i.e.,job assignment between factories,job sequence in each factory,and machine allocation for each job.We present a mixed inter linear programming model and propose a Novel MultiObjective Evolutionary Algorithm based on Decomposition(NMOEA/D).We specially design a decoding scheme according to the characteristics of the EADHFSPMT.To initialize a population with certain diversity,four different rules are utilized.Moreover,a cooperative search is designed to produce new solutions based on different types of relationship between any solution and its neighbors.To enhance the quality of solutions,two local intensification operators are implemented according to the problem characteristics.In addition,a dynamic adjustment strategy for weight vectors is designed to balance the diversity and convergence,which can adaptively modify weight vectors according to the distribution of the non-dominated front.Extensive computational experiments are carried out by using a number of benchmark instances,which demonstrate the effectiveness of the above special designs.The statistical comparisons to the existing algorithms also verify the superior performances of the NMOEA/D.

一种基于共享路由的传感器网络能量感知服务选择算法(英文)

This paper presents a novel energy-aware algorithm for service composition based on sharing routes in Wireless Sensor Networks (WSNs). The method integrates the resource of the overlapping WSNs to a virtual resource pool in the execution cycles of the workflow. This approach chooses the suitable service instances according to the current execution environment and user requirements with minimum energy consumption. Finally, the performance of sharing routes service composition selection in WSNs has been evaluated.

EA-DFPSO:An intelligent energy-efficient scheduling algorithm for mobile edge networks

Cloud data centers have become overwhelmed with data-intensive applications due to the limited computational capabilities of mobile terminals.Mobile edge computing is emerging as a potential paradigm to host application execution at the edge of networks to reduce transmission delays.Compute nodes are usually distributed in edge environments,enabling crucially efficient task scheduling among those nodes to achieve reduced processing time.Moreover,it is imperative to conserve edge server energy,enhancing their lifetimes.To this end,this paper proposes a novel task scheduling algorithm named Energy-aware Double-fitness Particle Swarm Optimization(EA-DFPSO)that is based on an improved particle swarm optimization algorithm for achieving energy efficiency in an edge computing environment along with minimal task execution time.The proposed EA-DFPSO algorithm applies a dual fitness function to search for an optimal tasks-scheduling scheme for saving edge server energy while maintaining service quality for tasks.Extensive experimentation demonstrates that our proposed EA-DFPSO algorithm outperforms the existing traditional scheduling algorithms to achieve reduced task completion time and conserve energy in an edge computing environment.

一种基于能量感知的延迟容忍移动无线传感器网络多拷贝路由协议(英文)

The two-phase replication-based routing has great prospects for Delay Tolerant Mobile Sensor Network (DTMSN) with its advantage of high message delivery ratio, but the blind spraying and the low efficiency forwarding algorithm directly influences the overall network performance. Considering the characteristic of the constrained energy and storage resources of sensors, we propose a novel two-phase multi-replica routing for DTMSN, called Energy-Aware Sociality-Based Spray and Search Routing (ESR), which implements the quota-style message replication mechanism by utilizing the energy and speed information of sensors. In addition, based on the difference of history encounters, a sociality metric is defined to improve the forwarding efficiency in search phase. Simulation experiments show that ESR can reduce the message delay and improve the resource utilization while maximizing the message delivery ratio compared with the exiting popular two-phase routing protocols.

An Efficient Priority Based Routing Technique That Maximizes the Lifetime and Coverage of Wireless Sensor Networks

Recent development in sensor technologies makes wireless sensor networks (WSN) very popular in the last few years. A limitation of most popular sensors is that sensor nodes have a limited battery capacity that leads to lower the lifetime of WSN. For that, it raises the need to develop energy efficient solutions to keep WSN functioning for the longest period of time. Due to the fact that most of the nodes energy is spent on data transmission, many routing techniques in the literature have been proposed to expand the network lifetime such as the Online Maximum Lifetime heuristics (OML) and capacity maximization (CMAX). In this paper, we introduce an efficient priority based routing power management heuristic in order to increase both coverage and extend lifetime by managing the power at the sensor level. We accomplished that by setting priority metric in addition to dividing the node energy into two ratios;one for the sensor node originated data and the other part is for data relays from other sensors. This heuristic, which is called pERPMT (priority Efficient Routing Power Management Technique), has been applied to two well know routing techniques. Results from running extensive simulation runs revealed the superiority of the new methodology pERPMT over existing heuristics. The pEPRMT increases the lifetime up to 77% and 54% when compared to OML and CMAX respectively.

HAG： An Energy-Proportional Data Storage Scheme for Disk Array Systems

Energy consumption has been a critical issue for data storage systems, especially for modern data centers. A recent survey has showed that power costs amount to about 50%of the total cost of ownership in a typical data center, with about 27% of the system power being consumed by storage systems. This paper aims at providing an effective solution to reducing the energy consumed by disk storage systems, by proposing a new approach to reduce the energy consumption. Differing from previous approaches, we adopt two new designs. 1） We introduce a hotness-aware and group-based system model （HAG） to organize the disks, in which all disks are partitioned into a hot group and a cold group. We only make file migration between the two groups and avoid the migration within a single group, so that we are able to reduce the total cost of file migration. 2） We use an on-demand approach to reorganize files among the disks that is based on workload change as well as the change of data hotness. We conduct trace-driven experiments involving two real and nine synthetic traces and we make detailed comparisons between our method and competitor methods according to different metrics. The results show that our method can dynamically select hot files and disks when the workload changes and that it is able to reduce energy consumption for all the traces. Furthermore, its time performance is comparable to that of the compared algorithms. In general, our method exhibits the best energy e？ciency in all experiments, and it is capable of maintaining an improved trade-off between performance and energy consumption.

Key technologies of system on chip design

The most supreme characteristic of SoC （system on chip） era is the high complexity of the chips; architecture and software design have become the indivisible part of chip design. As semiconductor fabrication technology evolves into very deep sub-micron （DSM） level, power consumption has become the inevitable challenge in SoC design. In order to maximize the lifetime of portable system battery, SoC is required not only to be energy-efficient but also to work in an optimal and battery-aware manner. This paper intends to discuss some key technologies of SoC design from the above perspectives of view.

Toward the Optimal Configuration of Dynamic Voltage Scaling Points in Real-Time Applications

In real-time applications, compiler-directed dynamic voltage scaling （DVS） could reduce energy consumption efficiently, where compiler put voltage scaling points in the proper places, and the supply voltage and clock frequency were adjusted to the relationship between the reduced time and the reduced workload. This paper presents the optimal configuration of dynamic voltage scaling points without voltage scaling overhead, which minimizes energy consumption. The conclusion is proved theoretically. Finally, it is confirmed by simulations with equally-spaced voltage scaling configuration.

Minimization of total energy consumption in an m-machine flow shop with an exponential time-dependent learning effect

This study investigates an energy-aware flow shop scheduling problem with a time-dependent learning effect. The relationship between the traditional and the proposed scheduling problem is shown and objective is to determine a job sequence in which the total energy consumption is minimized. To provide an efficient solution framework, composite lower bounds are proposed to be used in a solution approach with the name of Boundsbased Nested Partition(BBNP). A worst-case analysis on shortest process time heuristic is conducted for theoretical measurement. Computational experiments are performed on randomly generated test instances to evaluate the proposed algorithms. Results show that BBNP has better performance than conventional heuristics and provides considerable computational advantage.