MRFS: A Multi-Resource Fair Scheduling Algorithm in Heterogeneous Cloud Computing

Task scheduling in cloud computing is considered as a significant issue that has attracted much attention over the last decade. In cloud environments, users expose considerable interest in submitting tasks on multiple Resource types. Subsequently, finding an optimal and most efficient server to host users’ tasks seems a fundamental concern. Several attempts have suggested various algorithms, employing Swarm optimization and heuristics methods to solve the scheduling issues associated with cloud in a multi-resource perspective. However, these approaches have not considered the equalization of dominant resources on each specific resource type. This substantial gap leads to unfair allocation, SLA degradation and resource contention. To deal with this problem, in this paper we propose a novel task scheduling mechanism called MRFS. MRFS employs Lagrangian multipliers to locate tasks in suitable servers with respect to the number of dominant resources and maximum resource availability. To evaluate MRFS, we conduct time-series experiments in the cloudsim driven by randomly generated workloads. The results show that MRFS maximizes per-user utility function by %15-20 in FFMRA compared to FFMRA in absence of MRFS. Furthermore, the mathematical proofs confirm that the sharingincentive, and Pareto-efficiency properties are improved under MRF

Gland segmentation in gastric histology images: detection of intestinal metaplasia

Gastric cancer is one of the most frequent causes of cancer-related deaths worldwide. Gastric intestinal metaplasia (IM) of the mucosa of the stomach has been found to increase the risk of gastric cancer and is considered as one of the precancerous lesions. Therefore, early detection of IM may have a valuable role in histopathological risk assessment regarding the possibility of progression to cancer. Accurate segmentation and analysis of gastric glands from the histological images plays an important role in the diagnostic confirmation of IM. Thus, in this paper, we propose a framework for segmentation of gastric glands and detection of IM. More specifically, we propose the GAGL-Net for the segmentation of glands. Then, based on two features of the extracted glands we classify the tissues into normal and IM cases. The results showed that the proposed gland segmentation approach achieves an F1 score equal to 0.914. Furthermore, the proposed methodology shows great potential for the IM detection achieving an accuracy score equal to 96.6%. To evaluate the efficiency of the proposed methodology we used a publicly available dataset and we created the GAGL dataset consisting of 59 Whole Slide Images (WSI) including both IM and normal cases

A New Approach to Calculate Resource Limits with Fairness in Kubernetes

Containerization has become a new approach that facilitates application deployment and delivers scalability, productivity, security, and portability. As a first promising platform, Docker was proposed in 2013 to automate the deployment of applications. There are many advantages of Docker for delivering cloud native services. However, its widespread use has revealed problems such as performance overhead. In order to deal with those problems, Kubernetes was introduced in 2015 as a container orchestration platform to simplify the management of containers. Kubernetes simplifies managing a large scale number of docker containers, however, the fairness is a missing point in the Kubernetes that has been applied in other platforms such as Apache Hadoop, YARN and Mesos. Assigning resource limits fairly among the pods in kubernetes becomes a challenging issue as some applications may require intensive resources such as CPU and memory that should be maximized to satisfy them. In order to do that, in this paper, we practice a novel way to assign resource limits fairly among the pods in the Kubernetes environment

Disappeared by Climate Change. The Shepherd Cultures of Qulban Ceni Murra (2nd Half of the 5th Millennium bc) and their Aftermath

Le phénomène funéraire attesté dans le sud-est de la Jordanie témoigne d’une phase d’occupation méconnue de la région au cours du milieu de l’Holocène, en lien avec un mode de vie pastoral basé sur l’exploitation des ressources en eau des puits (« Early Mid-Holocene pastoral well cultures », 4500-4000 bc). À titre d’hypothèse, cette phase précoce d’occupation a pu aboutir au développement des premières « cultures des oasis » de la péninsule Arabique (« Oasis cultures », 4000-35000/3000 bc). Cette deuxième phase constitue, après la Néolithisation, un des derniers grands épisodes de sédentarisation des sociétés du Proche-Orient, reflet d’une importante capacité d’innovation et d’adaptation socio-économique permettant la conquête de nouveaux territoires arides pour une occupation sédentaire. L’occupation pastorale du Sud-Est jordanien constitue le prolongement oriental du phénomène de peuplement de la péninsule Arabique, caractérisé au cours de la période (acéramique) du Chalcolithique récent/Bronze ancien par la récurrence des structures funéraires mégalithiques de pierres dressées et des cairns. Le site de Qulban Beni Murra est l’un de ces complexes funéraires importants (>1 km2), et témoigne d’une grande diversité dans les types de tombes et de structures en présence. Il atteste aussi une série d’aménagements hydrauliques (dépressions creusées) alimentés par des puits (datés de 4400 av. J.-C. environ) qui sont le reflet d’une occupation pastorale qui a probablement abouti au développement dans cette région, après un assèchement du climat à partir de 4000, d’une économie basée sur l’exploitation des oasis, là où les ressources en eau étaient encore disponibles.Sepulchral landscapes in southeastern Jordan give evidence of hitherto unknown early Mid-Holocene pastoral well cultures (4500-4000 bc), possibly followed by the region’s transition to an oasis-type of life-mode, or its contact to Arabia’s earliest oases cultures (4000-3500/3000 bc). The latter represents the latest major episode of sedentarisation in the Middle East and has to be considered as the most innovative and adaptive socioeconomic paradigm after the Neolithisation, allowing for sedentary use of arid lands from then on. The (aceramic) Late Chalcolithic/Early Bronze Age of Jordan’s southeast appears to be part of the western fringe of the pastoral well cultures that once occupied all of the Arabian Peninsula, characterised by their extensive megalithic standing stone graves and cairn fields. Qulban Beni Murra was not only such a large sepulchral centre (>1 km2) with several structural types of burials and other built features; its series of watering complexes (troughs), fed by wells (dating around 4400 bc), gives testimony to a lake/well-based pastoralism that probably became the progenitor of well-based oases economies at hydrologically favoured spots after the climate got drier and colder from 4000 bc on.تشير مخلفات القبور في منطقة جنوب شرق الأردن الى ثقافات بدوية غير معروفة حتى اليوم ارتبطت بأبار المياه خلال الفترة المتوسطة من عصر الهولوسين, والتي ربما تبعها تحول في المنطقة الى نمط حياة الواحات او التواصل مع ثقافات الواحات المبكرة في الجزيرة العربية (4000-3500/3000 ق.م). ويمثل هذا الأخير أحدث حلقة من التوطين في الشرق الأوسط والتي يجب أن تعتبر النموذج الأكثر ابتكاراً وتكيفا اجتماعياً واقتصادياً بعد العصر الحجري الحديث، مما سمح بالإستقرار في الأراضي القاحلة واستخدامها منذ ذلك الحين. ويبدو أن جنوب شرق الأردن خلال الفترة المتأخرة من العصر الحجري النحاسي وبداية العصر البرونزي المبكر كانت جزءاً من الطرف الغربي لثقافة البدواة المعتمدة على ابار المياه والتي سكنت مناطق الجزيرة العربية وتميزت بمخلفات القبور والرجوم التي تحتوي على الحجارة المنتصبة. ومنطقة قلبان بني مرة لا تحتوي فقط على المقابر الحجرية والتي تغطي واحد كيلومتر مربع ومعالم مبنية اخرى بل تحتوي كذلك على سلسلة من المناطق المروية التي تغذيها الأبار وتؤرخ الى حوالي 4400 ق.م. وهذا يشير الى وجود البداوة التي اعتمدت على البئر او البحيرة كنظام سابق لإقتصاد الواحات والتي فضلت مناطق معينة خلال الفترات الجافة والرطبة بعد 4000 ق.م

H-FfMRA: A multi resource fully fair resources allocation algorithm in heterogeneous cloud computing

The allocation of multiple types of resources fairly and efficiently has become a substantial concern in state-of-the-art computing systems. Accordingly, the rapid growth of cloud computing has highlighted the importance of resource management as a complicated and NP-hard problem. Unlike traditional frameworks, in modern data centers, incoming jobs pose demand profiles, including diverse sets of resources such as CPU, memory, and bandwidth across multiple servers. Accordingly, the fair distribution of resources, respecting such heterogeneity appears to be a challenging issue. Furthermore, the efficient use of resources as well as fairness, establish trade-off that renders a higher degree of satisfaction for both users and providers. Dominant Resource Fairness (DRF) has been introduced as an initial attempt to address fair resource allocation in multi-resource cloud computing infrastructures. Dozens of approaches have been proposed to overcome existing shortcomings associated with DRF. Although all those developments have satisfied several desirable fairness features, there are still substantial gaps. Firstly, it is not clear how to measure the fair allocation of resources among users. Secondly, no particular trade-off considers non-dominant resources in allocation decisions. Thirdly, those allocations are not intuitively fair as some users are not able to maximize their allocations. In particular, the recent approaches have not considered the aggregate resource demands concerning dominant and non-dominant resources across multiple servers. These issues lead to an uneven allocation of resources over numerous servers which is an obstacle against utility maximization for some users with dominant resources. Correspondingly, in this paper, a resource allocation algorithm called H-FFMRA is proposed to distribute resources with fairness across servers and users, considering dominant and non-dominant resources. The experiments show that H-FFMRA achieves approximately %20 improvements on fairness as well as full utilization of resources compared to DRF in multi-server settings

MLF-DRS: A Multi-level Fair Resource Allocation Algorithm in Heterogeneous Cloud Computing Systems

Cloud computing is a novel paradigm which provides on demand, scalable and pay-as-you-use computing resources in a virtualized form. With cloud computing, users are able to access large pools of resources anywhere without any limitation. In order to use the provided facilities by the cloud in an efficient way, the management of resources is an undeniable fact that should be considered in different aspects. Among all those aspects, resource allocation has received much attentions. Given the fact that the cloud is heterogeneous, the allocation of resources has to become more sophisticated. As a first promising work to deal with that problem, Dominant Resource Fairness (DRF) has been proposed which takes into account dominant shares of users. Although DRF has a sort of desirable fairness properties, it has some limitations that have already been identified in the literature. Unfortunately, DRF and its recent developments are not intuitively fair with respect to various resource demands. In this paper, we propose a Multi-level Fair Dominant Resource Scheduling (MLF-DRS) algorithm as a new allocation model inspired by Max-Min fairness and proportionality. Unlike other works that they equalize dominant shares of different resource types which leads to starvation in the maximization of allocation for some users, our algorithm guarantees that each user receives the resources they desire for based on dominant shares. As can be deducted from the mathematical proofs, MLF-DRS provides a full utilization of resources and meets some of the desirable fair allocation properties and it is applicable to be used in a naïve extension form in the presence of multiple servers as wel

FFMRA: A Fully Fair Multi-Resource Allocation Algorithm in Cloud Environments

The need for effective and fair resource allocation in cloud computing has been identified in the literature and in industrial contexts for some time now. Cloud computing, as a promising technology, offers usage-based payment, ondemand computing resources. However, in the recent decade, the growing complexity of the IT world resulted in making Quality of Service (QoS) in the cloud a challenging subject and an NP-hard problem. Specifically, fair allocation of resources in the cloud is one of the most important aspects of QoS that becomes more interesting especially when many users submit their tasks and requests include multiple resources. Research in this area has been considered since 2012 by introducing Dominant Resource Fairness (DRF) algorithm as an initial attempt to solve the resource fair allocation problem in the cloud. Although DRF has some good features in terms of fairness, it has been proven inefficient in some conditions. Remarkably, DRF and other works in its extension are not proven intuitively fair after all. These implementations have been unable to utilize all the resources in the system and more specifically, they leave the system in an imbalanced situation with respect to each specific resource. To tackle those problems, in this paper we propose a novel algorithm namely FFMRA inspired by DRF which allocate resources in a fully fair way considering both dominant and non-dominant shares. The results from the experiments show that our proposed method provides approximately 100% utilization of resources and distributes them fairly among the users and meets good fairness properties

Teaching lean construction: perspectives on theory and practice

This paper builds on the IGLC paper, “Different Perspectives on Teaching Lean Construction,” presented last year by Tsao et al. that documented teaching approaches from three different Lean Construction (LC) university course offerings. It aggregated the approaches taken by the course offerings, the Lean Construction Institute (LCI), and the Associated General Contractors of America to develop recommendations for learning modules, outcomes, and strategies for an introductory LC university course. This paper provides four additional distinct perspectives to continue the conversation about teaching LC in a university setting. It illustrates the authors’ differences in teaching approaches, experiences, and lessons learned from course offerings in the United States and Lebanon. The paper offers additional ideas for providing “proof of concept” to students and further illustrates how teaching LC effectively requires a combination of readings, lectures, discussions, simulation exercises, field trips, and guest speakers to mix theory with action. The paper then aggregates seven teaching perspectives in a single table to provide an overview of different approaches for teaching an introductory university-level course on LC