Online k-server routing problems

In an online k-server routing problem, a crew of k servers has to visit points in a metric space as they arrive in real time. Possible objective functions include minimizing the makespan (k-Traveling Salesman Problem) and minimizing the sum of completion times (k-Traveling Repairman Problem). We give competitive algorithms, resource augmentation results and lower bounds for k-server routing problems in a wide class of metric spaces. In some cases the competitive ratio is dramatically better than that of the corresponding single server problem. Namely, we give a 1+O((log¿k)/k)-competitive algorithm for the k-Traveling Salesman Problem and the k-Traveling Repairman Problem when the underlying metric space is the real line. We also prove that a similar result cannot hold for the Euclidean plane

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities. [Abstract copyright: © 2023. The Author(s).

The online-TSP against fair adversaries

In the online traveling salesman problem requests for visits to cities (points in a metric space) arrive online while the salesman is traveling. The salesman moves at no more than unit speed and starts and ends his work at a designated origin. The objective is to find a routing for the salesman which finishes as early as possible. We consider the online traveling salesman problem when restricted to the non-negative part of the real line. We show that a very natural strategy is 3/2-competitive which matches our lower bound. The main contribution of the paper is the presentation of a fair adversary , as an alternative to the omnipotent adversary used in competitive analysis for online routing problems. The fair adversary is required to remain inside the convex hull of the requests released so far. We show that on $R^+_0$ algorithms can achieve a strictly better competitive ratio against a fair adversary than against a conventional adversary. Specifically, we present an algorithm against a fair adversary with competitive ratio (1 + \sqrt 17)/4 ~ 1.28 and provide a matching lower bound. We also show competitiveness results for a special class of algorithms (called diligent algorithms) that do not allow waiting time for the server as long as there are requests unserved

Quality Metrics for Sustainability - The operational Energy Use of Application Software

In recent years, Information Technology (IT) has grown into a sector that is both as vital and, or possibly even more, resource consuming as Aviation. Combined with our ever-growing dependence of IT, the resulting energy costs as well as the inevitable depletion of materials, such as fossil fuels and rare earth materials, have driven the need for more sustainable solutions, both environmentally and economically. Consequently, the body of knowledge on IT Sustainability, or Green IT, is steadily growing, which has mainly yielded solutions for making hardware less resource costly and possibly more energy efficient. However, software can be seen as the true consumer of energy. As the design of software greatly determines the specific utilization of hardware components, during operation, software architects have the potential to influence the sustainability of software. However, this potential is yet inaccessible due to the lack of architectural tactics that address sustainability, let alone proper metrics to determine and validate the energy consumption of software. Hence, this research attempts to develop metrics for the operational energy use of application software and, subsequently, apply them to determine relations between software design decisions, system workloads and impacts on the energy use of software products, independently of systems. To achieve our primary objective, we measured the power consumption of different computer systems during base, application and maximum workload intervals, while in the meantime we also recorded the performance of the systems. The performance data was used afterwards to determine whether a specific measurement was acceptably clean (e.g. minimal amount of noise). Then, after collecting a sufficient amount of clean measurements, we applied a subtractive method to filter out unrelated base power consumptions and determine the operational energy use of an application. Two applications were used in this research to develop the metrics. First, we performed initial experiments with a CPU-intensive stress test application, which has a relatively simple workload and power usage profile. After this, we further evaluated the metrics by performing a case-study with a document batch processing application, which has a more complex design and deployment. To improve the reliability of the measurements, we remotely operated and monitored the test systems and we performed multiple iterations, or runs, per specific measurement to minimize the influence of noise. In addition, we considered different configurations, or versions, of the test applications to enable the analysis of architectural decisions, such as single vs. dual-core processing. Based on our findings, we propose a set of metrics that can be used to determine more precisely which portion of the system’s energy usage belongs to the execution of an application. Also, the metrics show that, although the total amount of energy which different systems require to execute an application may differ greatly, the operational energy use of the application can still be fairly similar. Lastly, it shows that multi-core processing often requires more power yet less total energy

A Resource Utilization Score for Software Energy Consumption

Software as the true consumer of power and its potential contribution to reach sustainability goals is increasingly being acknowledged. Studies so far have presented successful results and methods to address the energy consumption of the software, indicating that different stakeholders striving for green software have different information needs with respect to their goals. However, currently there is no uniform manner to communicate measurements to the different stakeholders such that key findings are clearly identifiable and easy to understand, which is likely to hamper green software practices. In this paper we propose a metric that expresses a score for the resource utilization, such as power consumption, of a software product. The metric is designed to be a single score and is flexible to encompass those aspects that a stakeholder considers relevant in the context of software energy consumption. The metric was applied on two applications and allowed for objective comparison of application configurations and versions. Also the behavior of these applications across different hardware configurations could be analyzed. In addition to the metric we investigate means to visualize measurements which enhances communication and helped with highlighting the key findings