Distribution slack allocation algorithm for energy aware task scheduling in cloud datacenters

Regarding the ever-increasing development of data and computational centers due to the contribution of high-performance computing systems in such sectors, energy consumption has always been of great importance due to CO2 emissions that can result in adverse effects on the environment. In recent years, the notions such as “energy” and also “Green Computing” have played crucial roles when scheduling parallel tasks in datacenters. The duplication and clustering strategies, as well as Dynamic Voltage and Frequency Scaling (DVFS) techniques, have focused on the reduction of the energy consumption and the optimization of the performance parameters. Concerning scheduling Directed Acyclic Graph (DAG) of a datacenter processors equipped with the technique of DVFS, this paper proposes an energy- and time-aware algorithm based on dual-phase scheduling, called EATSDCDD, to apply the combination of the strategies for duplication and clustering along with the distribution of slack-time among the tasks of a cluster. DVFS and control procedures in the proposed green system are mapped into Petri net-based models, which contribute to designing a multiple decision process. In the first phase, we use an intelligent combined approach of the duplication and clustering strategies to run the immediate tasks of DAG along with monitoring the throughput by concentrating on the reduction of makespan and the energy consumed in the processors. The main idea of the proposed algorithm involves the achievement of a maximum reduction in energy consumption in the second phase. To this end, the slack time was distributed among non-critical dependent tasks. Additionally, we cover the issues of negotiation between consumers and service providers at the rate of μ based on Green Service Level Agreement (GSLA) to achieve a higher saving of the energy. Eventually, a set of data established for conducting the examinations and also different parameters of the constructed random DAG are assessed to examine the efficiency of our proposed algorithm. The obtained results confirms that our algorithm outperforms compared the other algorithms considered in this study.

A Multi-Objective Approach for Optimal Energy Management in Smart Home Using the Reinforcement Learning

Maintaining a fair use of energy consumption in smart homes with many household appliances requires sophisticated algorithms working together in real time. Similarly, choosing a proper schedule for appliances operation can be used to reduce inappropriate energy consumption. However, scheduling appliances always depend on the behavior of a smart home user. Thus, modeling human interaction with appliances is needed to design an efficient scheduling algorithm with real-time support. In this regard, we propose a scheduling algorithm based on human appliances interaction in smart homes using reinforcement learning (RL). The proposed scheduling algorithm divides the entire day into various states. In each state, the agents attached to household appliances perform various actions to obtain the highest reward. To adjust the discomfort which arises due to performing inappropriate action, the household appliances are categorized into three groups i.e., (1) adoptable, (2) un-adoptable, (3) manageable. Finally, the proposed system is tested for the energy consumption and discomfort level of the home user against our previous scheduling algorithm based on least slack time phenomenon. The proposed scheme outperforms the Least Slack Time (LST) based scheduling in context of energy consumption and discomfort level of the home user.

Exploring the impact of various typologies of human capital on firms' productivity

PurposeAlthough the impact of human capital on productivity has long been discussed in prior studies, empirical evidence for African firms remains limited. The existing few studies have focussed on one type of human capital in isolation and failed to explore the distinct role of different types of human capital on productivity. The aim of this study is to examine the extent to which various typologies of human capital – schooling, on-the-job training (OJT) and slack time –, both in isolation and as a combination, contribute to the productivity of African firms.Design/methodology/approachTo this end, a cross-sectional firm-level data set from 13 African countries was used. To unravel the casual relationship, propensity score matching (PSM) and multinomial endogenous switching treatment regression (MESTR) techniques were employed.FindingsResults indicate that all typologies of human capital – schooling, slack time and OJT – have a significant and positive impact on firms' productivity. The findings of the study further point out that the highest payoff, in terms of increased productivity, is achieved when various typologies of human capital are used in combination, rather than in isolation, in the production process.Practical implicationsThe policy implications are that productivity of African firms can be improved by increasing the general level of schooling; encouraging firm-sponsored OJT; and giving employees time to develop new ideas.Originality/valueThe present study provides important insights into the distinct role of different types of human capital on productivity. In addition, it provides empirical evidence for a region where empirical evidence is scant.

INFLUENCE OF CONTROL SYSTEMS AND SLACK TIME ON PROCESS INNOVATION

ABSTRACT Purpose: This study analyzes the influence of the interactive and the diagnostic use of Management Control Systems (MCS) and slack time on process innovation in incubated companies. Originality/value: Organizational characteristics are associated with processes innovation in organizations with contemporary configuration. The study brings evidence to the contrasting findings of the MCS literature with slack time and process innovation. Design/methodology/approach: A survey was conducted with managers of companies listed on websites of incubators associated with the Associação Nacional de Entidades Promotoras de Empreendimentos Inovadores (National Association of Entities Promoting Innovative Enterprises), obtaining a sample of 106 valid responses. To analyze the data, the Structural Equation Modeling technique was applied to analyze the data. Findings: The results indicated that the interactive use of MCS promotes slack time at work, and the diagnostic use inhibits its creation. However, slack time was not significantly associated with processes innovation, which reveals a dysfunctional role of the slack. The interactive use of MCS had a significant influence on processes innovation. It is concluded that the interactive use prevails when the purpose is to promote processes innovation, as it favors contacts between different hierarchical levels and learning, while the diagnostic use highlights its relevance by inhibiting the creation of slack time, understood as a dysfunction for not leading to processes innovation. This denotes that the interactive and the diagnostic use of MCS are complementary in incubated companies.

Enhanced Slack Time based Price Driven Demand Response for Future Effectual Smart Communities

Evolution of smart grid concept aims to address the imbalance between electricity demand and supply. Owing to consideration on sustainable energy, user comfort, and cost efficiency, residential Demand Response (DR) has gained a remarkable popularity over the past few years. To further enhance these benefits, herein we propose a residential appliance scheduling algorithm inspired by Least Slack Time (LST) algorithm. The conventional LST algorithm is amended with consumption thresholds and waiting factor constraints to derive proposed Minimum Slack Time (MST) algorithm, which increase cost and comfort efficiency during DR. Proposed algorithm was experimented in a simulated residential community consists of 50 houses. Further experiments were conducted by aggregating renewable energy sources using aggregated MST (AMST) algorithm. All instances were compared with an existing scheduling mechanism to assure superiority of proposed MST and AMST algorithms, in terms of grid electricity consumption, cost, Peak-to-Average Ratio (PAR), and waiting time.

Penjadwalan Produksi pada Unit Daun Pintu dengan Menggunakan Metode Simulated Annealing

Masalah penjadwalan produksi di lantai pabrik terkait dengan kemampuan perusahaan untuk memenuhi seluruh order yang datang sesuai dengan batas waktu penyerahan (due date) yang telah ditentukan. PT. XYZ merupakan perusahaan manufaktur yang memproduksi produk daun pintu berdasarkan pesanan pelanggan (job order). Produk daun pintu model Colonial 8P merupakan produk unggulan perusahaan karena selalu dipesan pelanggan dalam jumlah besar. Produk-produk yang dihasilkan hanya untuk diekspor ke Singapura, Amerika, Australia, dan Afrika Selatan. Dalam penelitian ini, disajikan konsep pengurutan job aktual di perusahaan berdasarkan data waktu proses di 12 stasiun kerja dengan metode stopwatch time study. Perhitungan makespan aktual didasarkan atas metode Least Slack Time. Kemudian, akan dibangun sebuah simulasi model untuk mendapatkan urutan job usulan dengan metode Simulated Annealing (SA) untuk mendapatkan makespan terkecil. Solusi pemecahan masalah diperoleh berdasarkan kombinasi urutan job yang dilakukan secara random sebanyak 30 iterasi setiap penurunan temperatur sampai mencapai kondisi steady state, dimana tidak ada lagi solusi yang diterima. Kondisi steady state terjadi pada temperatur 180,5 dimana tidak lagi didapatkan makespan yang lebih kecil dari makespan sebelumnya. Nilai makespan terkecil terjadi pada urutan job Napoleon 6P-Carolina 6P-Colonial 6P-Colonial 8P-Colonial 4P. Dan dari hasil penelitian ini disimpulkan bahwa penjadwalan usulan dengan metode simulated annealing mendapatkan hasil yang lebih baik daripada penjadwalan aktual.