Bio-inspired negotiation approach for smart-grid colocation datacenter operation

<abstract><p>Demand response programs allow consumers to participate in the operation of a smart electric grid by reducing or shifting their energy consumption, helping to match energy consumption with power supply. This article presents a bio-inspired approach for addressing the problem of colocation datacenters participating in demand response programs in a smart grid. The proposed approach allows the datacenter to negotiate with its tenants by offering monetary rewards in order to meet a demand response event on short notice. The objective of the underlying optimization problem is twofold. The goal of the datacenter is to minimize its offered rewards while the goal of the tenants is to maximize their profit. A two-level hierarchy is proposed for modeling the problem. The upper-level hierarchy models the datacenter planning problem, and the lower-level hierarchy models the task scheduling problem of the tenants. To address these problems, two bio-inspired algorithms are designed and compared for the datacenter planning problem, and an efficient greedy scheduling heuristic is proposed for task scheduling problem of the tenants. Results show the proposed approach reports average improvements between $ 72.9\% $ and $ 82.2\% $ when compared to the business as usual approach.</p></abstract>

CHiPReP—A Compiler for the HiPReP High-Performance Reconfigurable Processor

This article presents CHiPReP, a C compiler for the HiPReP processor, which is a high-performance Coarse-Grained Reconfigurable Array employing Floating-Point Units. CHiPReP is an extension of the LLVM and CCF compiler frameworks. Its main contributions are (i) a Splitting Algorithm for Data Dependence Graphs, which distributes the computations of a C loop to Address-Generator Units and Processing Elements; (ii) a novel instruction clustering and scheduling heuristic; and (iii) an integrated placement, pipeline balancing and routing optimization method based on Simulated Annealing. The compiler was verified and analyzed using a cycle-accurate HiPReP simulation model.

Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

The cloud computing systems are sorts of shared collateral structure which has been in demand from its inception. In these systems, clients are able to access existing services based on their needs and without knowing where the service is located and how it is delivered, and only pay for the service used. Like other systems, there are challenges in the cloud computing system. Because of a wide array of clients and the variety of services available in this system, it can be said that the issue of scheduling and, of course, energy consumption is essential challenge of this system. Therefore, it should be properly provided to users, which minimizes both the cost of the provider and consumer and the energy consumption, and this requires the use of an optimal scheduling algorithm. In this paper, we present a two-step hybrid method for scheduling tasks aware of energy and time called Genetic Algorithm and Energy-Conscious Scheduling Heuristic based on the Genetic Algorithm. The first step involves prioritizing tasks, and the second step consists of assigning tasks to the processor. We prioritized tasks and generated primary chromosomes, and used the Energy-Conscious Scheduling Heuristic model, which is an energy-conscious model, to assign tasks to the processor. As the simulation results show, these results demonstrate that the proposed algorithm has been able to outperform other methods.

Improving Manufacturing Supply Chain by Integrating SMED and Production Scheduling

Globalization has led to a significant effect on today’s manufacturing sector. Manufacturers need to find new and innovative ways to increase efficiency and reduce waste in the manufacturing supply chain. Lean/six sigma tools can help companies increase production efficiency and stay in competition. Manufacturing in smaller batches can keep the supply chain lean and customizable. This leads to frequent changeovers and downtime. A changeover is usually required when a single machine produces different products based on the requirement. A large-scale industry can either install multiple individual production lines to cater to the demand (usually expensive) or make frequent machinery changes. Single Minute Exchange Die (SMED) is a system designed for reducing the changeover time for machines. It reduces the time taken to complete the activities and eliminates non-essential activities throughout the changeover. Scheduling an operating procedure within SMED in such case is a challenge. Project scheduling model with workforce constraints can be used to create a set of heuristics to provide us with an optimized list of tasks. The paper proposes to design a scheduling heuristic model to allocate tasks to the operators to get the least amount of operator idle time and reduce changeover downtime costs. The paper further illustrates the benefit of the model in a case study and proposes its integration within the existing SMED methodology. This results in a benefit-to-cost ratio of 7.5% for production scheduling compared to that of stages 4 and 5 in SMED, which is 1.2%.

Data Placement Oriented Scheduling Algorithm for Scheduling Scientific Workflow in Cloud: A Budget-Aware Approach

Background: A large number of communities and enterprises deploy numerous scientific workflow applications on cloud service. Aims: The main aim of the cloud service provider is to execute the workflows with a minimal budget and makespan. Most of the existing techniques for budget and makespan are employed for the traditional platform of computing and are not applicable to cloud computing platforms with unique resource management methods and pricing strategies based on service. Methods: In this paper, we studied the joint optimization of cost and makespan of scheduling workflows in IaaS clouds, and proposed a novel workflow scheduling scheme. Also, data placement is included in the proposed algorithm. Results: In this scheme, DPO-HEFT (Data Placement Oriented HEFT) algorithm is developed which closely integrates the data placement mechanism with the list scheduling heuristic HEFT. Extensive experiments using the real-world and synthetic workflow demonstrate the efficacy of our scheme. Conclusion: Our scheme can achieve significantly better cost and makespan trade-off fronts with remarkably higher hypervolume and can run up to hundreds times faster than the state-of-the-art algorithms.

Comparison Of Q-Coverage P-Connectivity Sensor Node Scheduling Heuristic Between Battery Powered Wsn & Energy Harvesting Wsn

: A serious problem in Wireless Sensor Networks (WSNs) is to attain high-energy efficiency as battery powers a node, which has limited stored energy. They can’t be suitably replaced or recharged. Appearance of renewable energy harvesting techniques and their combination with sensors gives Energy Harvesting Wireless Sensor Networks (EH-WSNs). Therefore, the area shifts from energy preservation to reliability of the network. For reliability, Coverage and Connectivity are important Quality of Service (QoS) parameters. Many sensor node scheduling heuristics have been developed in past. Some of them focus on more than single order of Coverage and Connectivity. If reliability is main concern for a WSN, then definitely there is a need to incorporate Q-Coverage and P-Connectivity in WSN. Lifetime of a WSN decreases while considering Q-Coverage and P-Connectivity. After some time network dies and it is of no use. Therefore, there is a trade-off between reliability and lifetime of a WSN. If EH-WSN is used, then lifetime of a WSN increases as well as Q-Coverage and P-Connectivity QoS parameters can used for achieving reliability. This paper proposes a sensor node scheduling heuristic for EH-WSN considering Q-Coverage and P-Connectivity. A comparison of a Q-Coverage and P-Connectivity sensor node scheduling heuristic when used between battery powered WSN & EH-WSN is done. Comparison shows that lifetime using EH-WSN is much greater as compared to battery powered WSN. This research is beneficial for real time WSN applications where EH-WSN provides power regularly without any intervention.