Deep reinforcement learning enhanced greedy optimization for online scheduling of batched tasks in cloud HPC systems

In a large cloud data center HPC system, a critical problem is how to allocate the submitted tasks to heterogeneous servers that will achieve the goal of maximizing the system's gain defined as the value of completed tasks minus system operation costs. We consider this problem in the online setting that tasks arrive in batches and propose a novel deep reinforcement learning (DRL) enhanced greedy optimization algorithm of two-stage scheduling interacting task sequencing and task allocation. For task sequencing, we deploy a DRL module to predict the best allocation sequence for each arriving batch of tasks based on the knowledge (allocation strategies) learnt from previous batches. For task allocation, we propose a greedy strategy that allocates tasks to servers one by one online following the allocation sequence to maximize the total gain increase. We show that our greedy strategy has a performance guarantee of competitive ratio $\frac{1}{1+\kappa }$11+κ to the optimal offline solution, which improves the existing result for the same problem, where $\kappa$κ is upper bounded by the maximum cost-to-gain ratio of each task. While our DRL module enhances the greedy algorithm by providing the likely-optimal allocation sequence for each batch of arriving tasks, our greedy strategy bounds DRL's prediction error within a proven worst-case performance guarantee for any allocation sequence. It enables a better solution quality than that obtainable from both DRL and greedy optimization alone. Extensive experiment evaluation results in both simulation and real application environments demonstrate the effectiveness and efficiency of our proposed algorithm. Compared with the state-of-the-art baselines, our algorithm increases the system gain by about 10% to 30%. Our algorithm provides an interesting example of combining machine learning (ML) and greedy optimization techniques to improve ML-based solutions with a worst-case performance guarantee for solving hard optimization problems

Scheduling workflow tasks with unknown task execution time by combining machine-learning and greedy-optimization

Workflow tasks are time-sensitive and their task completion utility, i.e., value of task completion, is inversely proportional to their completion time. Existing solutions to the NP-hard problem of utility-maximization task scheduling were achieved under the assumptions of linear Time Utility Function (TUF), i.e., utility is inversely proportional to completion time following a linear function, and prior knowledge of task execution time, which is unrealistic for many applications and dynamic systems. This paper proposes a novel model of combining greedy optimization with machine learning for scheduling time-sensitive tasks with convex TUF and unknown task execution time on heterogeneous cloud servers offline nonpreemptively to maximize the total utility of input tasks. For a set of time-sensitive tasks with data dependencies, we first employ multi-layer perceptron neural networks to predict task execution time by utilizing historical data. Then, by solving a linear program after relaxing the disjunctive constraint introduced by the nonpreemption requirement to calculate maximum utility increment, we propose a novel greedy algorithm of marginal incremental utility maximization that jointly determines the task-to-processor allocation plan and tasks' execution sequence on each processor. We then show that our algorithm has an expected approximation ratio of (e−1)(τ−2)eτ for convex TUF and e−13e≈0.21 for linear TUF, where τ is the ratio of total completion utility over total delay cost under optimal scheduling. Our result presents the first polynomial-time approximation solution for this problem that achieves a performance guarantee of bounded ratio for convex TUF and constant ratio for linear TUF respectively. Extensive experiment results through both simulation and real cloud implementation demonstrate significant performance improvement of our algorithm over the known results

Optimizing Federated Learning With Deep Reinforcement Learning for Digital Twin Empowered Industrial IoT

The accelerated development of the Industrial Internet of Things (IIoT) is catalyzing the digitalization of industrial production to achieve Industry 4.0. In this article, we propose a novel digital twin (DT) empowered IIoT (DTEI) architecture, in which DTs capture the properties of industrial devices for real-time processing and intelligent decision making. To alleviate data transmission burden and privacy leakage, we aim to optimize federated learning (FL) to construct the DTEI model. Specifically, to cope with the heterogeneity of IIoT devices, we develop the DTEI-assisted deep reinforcement learning method for the selection process of IIoT devices in FL, especially for selecting IIoT devices with high utility values. Furthermore, we propose an asynchronous FL scheme to address the discrete effects caused by heterogeneous IIoT devices. Experimental results show that our proposed scheme features faster convergence and higher training accuracy compared to the benchmark

Developing analytics-driven maintenance data mining processes: a design science approach

Purpose: Data mining has been well-applied by maintenance service providers in identifying data patterns and supporting decision-making. However, when applying data mining for analytics-driven maintenance, maintenance service providers often adopt data mining with unstructured “trial-and-error” approaches. In response, we have followed design science to develop a comprehensive approach to diagnosing the problems with the existing data mining processes model for analytics-driven maintenance service. Design/methodology/approach: This study conducted an in-depth case study with Siemens in the UK for data collection in order to apply a two-cycle build-and-evaluate design process. Based on the literature, the preliminary model is built. It is evaluated through the case company in the first cycle. In the second cycle, the model is refined based on the comments from the case company and then re-evaluated from both business management and information technology perspectives to ensure the applicability of the designed model in a real business environment. Findings: Firstly, this study identifies three main shortcomings in the existing data mining process models for analytics-driven maintenance. Secondly, this study develops the “Gear-Wheel Model”, with a customer-oriented cycle, a project planning cycle and a machine comprehension cycle, to overcome all these shortcomings simultaneously and provide improvement solutions. Thirdly, this study highlighted that the data mining processes for analytics-driven maintenance service need interactions from different functional departments and supports of successive data collection. Originality/value: The study expands data mining analysis beyond a single business function to include interactions with other internal functions and external customers. It contributes to existing knowledge by focusing on the managerial aspects of data mining and integrating maintenance service providers with their business customers.</p

Correction to: Cross‑platform comparison of framed topics in Twitter and Weibo: machine learning approaches to social media text mining (Social Network Analysis and Mining, (2021), 11, 1, (75), 10.1007/s13278-021-00772-w)

Unfortunately, the caption of Table 2 is wrongly published in the original article and the correct title is Latent topics, categories, and probability scores identified by LDA (Weibo data). The original article has been corrected

Food waste management for the UK grocery retail sector—A supply chain collaboration perspective

Food waste is a consequence of the contemporary retail environment, where maintaining high product availability and customer choice is considered the basis of competition. It has become an especially focal issue in recent years for grocery retailers. Despite the evidence of retailers adopting many good practices to mitigate food waste, there are still many challenges facing the sector. This paper thus conducted a qualitative study drawing on secondary data on the big four UK supermarkets and twelve in-depth interviews with high-level supply chain (SC) managers in one of those four supermarkets to investigate UK retailers’ waste management practices and develop SC collaboration strategies for preventative waste management. The findings show that SC collaboration on the waste management needs to focus on forecasting, product range management, case size changes, performance measurement, information systems, and multi-stakeholder collaboration. This study recommends that retailers enhance their institutional collaboration with SC partners and social collaboration with other stakeholders

PSSPR: A Source Location Privacy Protection Scheme Based on Sector Phantom Routing in WSNs

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Cost effective dynamic data placement for efficient access of social networks

Social networks boast a huge number of worldwide users who join, connect, and publish various content, often very large, e.g. videos, images etc. For such very large-scale data storage, data replication using geo-distributed cloud services with virtually unlimited capabilities are suitable to fulfill the users’ expectations, such as low latency when accessing their and their friends’ data. However, service providers ideally want to spend as little as possible on replicating users’ data. Moreover, social networks have a dynamic nature and thus replicas need to be adaptable according to the environment, users’ behaviors, social network topology, and workload at runtime. Hence, it is not only crucial to have an optimized data placement and request distribution – meeting individual users’ acceptable latency requirements while incurring minimum cost for service providers – but the data placement must be adapted based on changes in the social network to keep it efficient and effective over time. In this paper, we model data placement as a dynamic set cover problem and propose a novel approach to solve this problem. We have run several experiments using two large-scale, open Facebook and Gowala datasets and real latencies derived from Amazon cloud datacenters to demonstrate our novel strategy's efficiency and effectiveness

Probabilistic Critical Path identification for cost-effective monitoring of Service-based Web Applications

The critical path of a composite Web application operating in volatile environments, i.e., the execution path in the service composition with the maximum execution time, should be prioritised in cost-effective monitoring as it determines the response time of the Web application. In volatile operating environments, the critical path of a Web application is probabilistic. As such, it is important to estimate the criticalities of the execution paths, i.e., the probabilities that they are critical, to decide which parts of the system to monitor. We propose a novel approach to the identification of Probabilistic Critical Path for Service-based Web Applications (PCP-SWA), which calculates the criticalities of different execution paths in the context of service composition. We evaluate PCP-SWA experimentally using an example Web application. Compared to random monitoring, PCP-SWA based monitoring is 55.67% more cost-effective on average. Copyright is held by the author/owner(s)

Wave propagation in two-dimensional anisotropic acoustic metamaterials of K4 topology

An acoustic metamaterial is envisaged as a synthesised phononic material the mechanical behaviour of which is determined by its unit cell. The present study investigates one aspect of mechanical behaviour, namely the band structure, in two-dimensional (2D) anisotropic acoustic metamaterials encompassing locally resonant mass-in-mass units connected by massless springs in a K4 topology. The 2D lattice problem is formulated in the direct space (r-space) and the equations of motion are derived using the principle of least action (Hamilton's principle). Only proportional anisotropy and attenuation-free shock wave propagation have been considered. Floquet-Bloch's principle is applied, therefore a generic unit cell is studied. The unit cell can represent the entire lattice regardless of its position. It is transformed from the direct lattice in r-space onto its reciprocal lattice conjugate in Fourier space (k-space) and point symmetry operations are applied to Wigner-Seitz primitive cell to derive the first irreducible Brillouin Zone (BZ). The edges of the first irreducible Brillouin Zone in the k-space have then been traversed to generate the full band structure. It was found that the phenomenon of frequency filtering exists and the pass and stop bands are extracted. A follow-up parametric study appreciated the degree and direction of influence of each parameter on the band structure.</p