Default Resource Research Articles

How socioeconomically disadvantaged people access, understand, appraise, and apply health information: A qualitative study exploring health literacy skills.

Health literacy, or a person's competence to access, understand, appraise and apply health information, can be considered a mediating factor between socioeconomic characteristics and health disparities. Socioeconomically disadvantaged people in particular present with less health literacy skills. To develop targeted interventions tailored to their real needs, it is important to understand how they function and what difficulties they encounter when dealing with health information. The purpose of this study was to explore their experiences when accessing, understanding, appraising, and applying health information in their everyday lives. Semi-structured face-to-face interviews were conducted with 12 socioeconomically disadvantaged adults living in the community in Switzerland (age range: 44-60 years old). Thematic analysis of the interviews yielded four themes, describing the health literacy processes of participants, related barriers, and compensatory strategies used: Financial insecurity triggers the need for health information; Pathway 1: Physicians as ideal (but expensive) interlocutors; Pathway 2: The internet as a suboptimal alternative; and Pathway 3: Relatives as a default resource. The progression of socioeconomically disadvantaged people in the health literacy process is like an 'obstacle course', with numerous steps taken backwards before they can develop compensatory strategies to overcome the barriers to obtaining health information. Financial deprivation seems to be the most important factor contributing to health literacy barriers. Appraising health information is the health literacy skill with which socioeconomically disadvantaged people struggle the most. Physician-based, individual skills-based, organizational, and policy-based interventions are needed to help them overcome their health literacy challenges.

Vertical Pod Autoscaling in Kubernetes for Elastic Container Collaborative燜ramework

Kubernetes is an open-source container management tool which automates container deployment, container load balancing and container(de)scaling, including Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA). HPA enables flawless operation, interactively scaling the number of resource units, or pods, without downtime. Default Resource Metrics, such as CPU and memory use of host machines and pods, are monitored by Kubernetes. Cloud Computing has emerged as a platform for individuals beside the corporate sector. It provides cost-effective infrastructure, platform and software services in a shared environment. On the other hand, the emergence of industry 4.0 brought new challenges for the adaptability and infusion of cloud computing. As the global work environment is adapting constituents of industry 4.0 in terms of robotics, artificial intelligence and IoT devices, it is becoming eminent that one emerging challenge is collaborative schematics. Provision of such autonomous mechanism that can develop, manage and operationalize digital resources like CoBots to perform tasks in a distributed and collaborative cloud environment for optimized utilization of resources, ensuring schedule completion. Collaborative schematics are also linked with Bigdata management produced by large scale industry 4.0 setups. Different use cases and simulation results showed a significant improvement in Pod CPU utilization, latency, and throughput over Kubernetes environment.

Niyama: Node scheduling for cloud workloads with resource isolation

SummaryCloud providers place tasks from multiple applications on the same resource pool to improve the resource utilization of the infrastructure. The consequent resource contention has an undesirable effect on latency‐sensitive tasks. In this article, we present Niyama—a resource isolation approach that uses a modified version of deadline scheduling to protect latency‐sensitive tasks from CPU bandwidth contention. Conventionally, deadline scheduling has been used to schedule real‐time tasks with well‐defined deadlines. Therefore, it cannot be used directly when the deadlines are unspecified. In Niyama, we estimate deadlines in intervals and secure bandwidth required for the interval, thereby ensuring optimal job response times. We compare our approach with cgroups: Linux's default resource isolation mechanism used in containers today. Our experiments show that Niyama reduces the average delay in tasks by 3–20 when compared to cgroups. Since Linux's deadline scheduling policy is work‐conserving in nature, there is a small drop in the server‐level CPU utilization when Niyama is used naively. We demonstrate how the use of core reservation and oversubscription in the inter‐node scheduler can be used to offset this drop; our experiments show a 1.3–2.24 decrease in delay in job response time over cgroups while achieving high CPU utilization.

Amnis: Optimized stream processing for edge computing

The proliferation of Internet-of-Things (IoT) devices is rapidly increasing the demands for efficient processing of low latency stream data generated close to the edge of the network. Edge computing-based stream processing techniques that carefully consider the heterogeneity of the computational and network resources available in the infrastructure provide significant benefits in optimizing the throughput and end-to-end latency of the data streams. In this paper, we propose a novel stream query processing framework called Amnis that optimizes the performance of the stream processing applications through a careful allocation of computational and network resources available at the edge. The Amnis approach differentiates itself through its consideration of data locality and resource constraints during physical plan generation and operator placement for the stream queries. Additionally, Amnis considers the coflow dependencies to optimize the network resource allocation through an application-level rate control mechanism. We implement a prototype of Amnis in Apache Storm. Our performance evaluation carried out in a real testbed shows that the proposed techniques achieve as much as 200X improvement on the end-to-end latency and 10X improvement on the overall throughput compared to the default resource aware scheduler in Storm.

Advances in parallel and distributed computing and its applications

Advances in parallel and distributed computing and its applications

An Enhanced Mathematical Model For Cloud Based Data Oriented Job Analysis

Data processing or data analytics is the common functionality that is attached to most of the real world applications. The amount of processing required for data oriented tasks or jobs are quiet high. To resolve the processing issue the most common approach deployed is by using a high performance cluster. Setting a cluster over real time infrastructure leads to a very expensive solution. A Cloud based infrastructure remains as an ideal support for setting up a cluster. Managing the cluster over a Cloud is a challenging task as allocation of infrastructure based on the task schedule is a critical parameter. The proposed mathematical model introduces a strategy to allocate the infrastructure and manage the load of the cluster based on Queuing model. The experimental setup is made on top of private cloud and Hadoop based data processing jobs are tested. The proposed data oriented resource optimizer enhances the performance of the cluster by balancing the increased load due to data processing jobs. The result shows the enhanced improvement in performance compared to default resource manager.

Enhancing disk input output performance in consolidated virtualized cloud platforms using a randomized approximation scheme

AbstractIn a virtualized computer system with shared resources, consolidated virtual services (VSs) fiercely compete with each other to obtain the required capacity of resources, and this causes significant system's performance degradation. The performance of input output (I/O)‐bound applications running inside their own VS is mainly determined by the total time required to schedule every read/write request, plus the actual time needed by the device driver to complete the request. To achieve a right performance isolation of shared resources (e.g., the last level cache, memory bandwidth, and the disk buffer), it is essential to limit the performance degradation level among collocated applications, as simultaneously several I/O operations are requested by VSs, perhaps with different priorities. This article proposes a resource allocation controller that uses a fully polynomial‐time randomized approximation scheme to enable performance isolation of concurrent I/O requests in a shared system with multiple consolidated VSs. This controller uses a Monte Carlo sampling approach to measure and estimate the unknown attributes of operational requests originating from each VS. This is formalized as an optimization problem with the aim to minimize the degree of total quality of service (QoS) violation incidents in the entire platform. We associated a reward function to every working machine that represents the fulfillment degree of quality of service metric among all running VSs. The conducted comprehensive set of experiments showed that the proposed algorithm can reduce the QoS violation incidents by 32%, compared with the result which is obtained by employing the default resource allocation policy embedded in the existing Linux container layer.

Updates on usage of the Czech national HPC center

The distributed computing of the ATLAS experiment at LHC has used computing resources of the Czech national HPC center IT4Innovations for several years. The submission system is based on ARC-CEs installed at the Czech Tier2 site (praguelcg2). Recent improvements of this system will be discussed here. First, there was a migration of the ARC-CE from version 5 to 6 which improves the reliability and scalability. A shared filesystem built on top of sshfs 3.7 no longer represents performance bottleneck. It provided an order of magnitude better transfer performance. New Singularity containers with full software stack can easily fit default resource limits on the IT4I cluster filesystem. A new submission system, allowing sequential running of payloads in one job, was set and adapted to HPC’s environment, improving usage on worker nodes with very high number of cores. Overall, the whole infrastructure provides significant contribution to resources provided by praguelcg2.

More bang for your buck: Boosting performance with capped power consumption

Achieving faster performance without increasing power and energy consumption for computing systems is an outstanding challenge. This paper develops a novel resource allocation scheme for memory-bound applications running on High-Performance Computing (HPC) clusters, aiming to improve application performance without breaching peak power constraints and total energy consumption. Our scheme estimates how the number of processor cores and CPU frequency setting affects the application performance. It then uses the estimate to provide additional compute nodes to memory-bound applications if it is profitable to do so. We implement and apply our algorithm to 12 representative benchmarks from the NAS parallel benchmark and HPC Challenge (HPCC) benchmark suites and evaluate it on a representative HPC cluster. Experimental results show that our approach can effectively mitigate memory contention to improve application performance, and it achieves this without significantly increasing the peak power and overall energy consumption. Our approach obtains on average 12.69% performance improvement over the default resource allocation strategy, but uses 7.06% less total power, which translates into 17.77% energy savings.

Horizontal Pod Autoscaling in Kubernetes for Elastic Container Orchestration.

Kubernetes, an open-source container orchestration platform, enables high availability and scalability through diverse autoscaling mechanisms such as Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler and Cluster Autoscaler. Amongst them, HPA helps provide seamless service by dynamically scaling up and down the number of resource units, called pods, without having to restart the whole system. Kubernetes monitors default Resource Metrics including CPU and memory usage of host machines and their pods. On the other hand, Custom Metrics, provided by external software such as Prometheus, are customizable to monitor a wide collection of metrics. In this paper, we investigate HPA through diverse experiments to provide critical knowledge on its operational behaviors. We also discuss the essential difference between Kubernetes Resource Metrics (KRM) and Prometheus Custom Metrics (PCM) and how they affect HPA’s performance. Lastly, we provide deeper insights and lessons on how to optimize the performance of HPA for researchers, developers, and system administrators working with Kubernetes in the future.

Hindu Kush Himalayan Ecosystem as a Common Concern of Humankind: Imperative for Better Regional Jurisprudence

Hindu Kush Himalayan region (hereafter the HKH) - with 3500 odd kilometres stretched in eight countries- is default resource generation hub for about one-fifth population of the world. The ecosystem-growing delicate these days- seems to play a critical role for the survival of flora and fauna along with the maintenance of all its life-sustaining mountain glaciers. Ten major rivers to carry forward hitherto sustainable development of these peoples fall into question now. Further, in the wake of global climate change today, the delicate HKH ecosystem becomes increasingly fragile to unfold manifold consequences and thereby take its toll on the population. And the same might turn apocalyptic in its magnanimity of irreversibledamage. Like time-bomb, thus, climate ticks to get blown off. As it is getting already too delayed for timely resort to safeguards, if still not taken care of in time, lawmakers ought to find the aftermath too late to lament for. Besides being conscious for climate discipline across the world, collective efforts on the part of all regional states together are imperative to minimize the damage. Therefore, each one has put hands together to be saved from the doomsday that appears to stand ahead to accelerate a catastrophicend, in the given speed of global climate change. As the largest Himalayan state and its central positioning at the top of the HKH, Nepal has had potential to play a criticalrole to engage regional climate change regime and thereby spearhead climate diplomacy worldwide to play regional capital of the HKH ecosystem. As regional superpower, India has had potential to usurp leadership avatar to this end. With reasoningof his own, the author pleads for better jurisprudence to attain regional environmental integrity inter se- rather than regional environmental integration alone- to defendthe vulnerable HKH ecosystem since the same constitutes common concern of humankind and much more so for themselves. Hence, to quote from Shakespeare, “To be or not to be, that is the question” is reasonable here. While states are engaged in the spree to cause mutually agreed destruction, global climate change- with deadly aftermath- poses the last and final unifier for them to turn United Nations in rhetoric sense o f the term.

Raccoon: A Novel Network I/O Allocation Framework for Workload-Aware VM Scheduling in Virtual Environments

We present a network I/O allocation framework, called Raccoon , for workload-aware VM scheduling algorithm to facilitate hybrid I/O workloads in virtual environments. Raccoon combines the strengths of paravirtual I/O and SR-IOV techniques to not only minimize the network latency, but also optimize the bandwidth utilization for workload-aware VM scheduling. In Raccoon , a limited number of VFs in SR-IOV are granted to I/O-intensive VMs while the paravirtual Network Interface Cards (vNICs) are allocated to other non-I/O-intensive VMs as the default resources. With this design, Raccoon provides latency reduction and bandwidth guarantee under the premise that I/O-intensive VMs will always be granted the VFs to facilitate their I/O operations. The types of workloads in each VM are identified at runtime by modified XenMon . By leveraging the ACPI Hotplug technique, Raccoon can adaptively plugin and plugout the SR-IOV VFs upon the changes of VM requirements so that an efficient I/O workload-aware VM scheduling algorithm can be implemented based on the bonding driver technique. The experimental results reveal that Raccoon can combine the benefits of para-virtual I/O and SR-IOV techniques to improve the overall performance of virtualized platforms with VMs that have diverse I/O workloads.

Clearinghouse Default Waterfalls: Risk-Sharing, Incentives, and Systemic Risk

Central to the ongoing debate on default resource adequacy are the incentives provided by the clearinghouse waterfall structure. We show that clearinghouse equity and member default funds play a complementary role to initial margins: they incentivize safe members to participate rather than deterring risky ones from not participating. Our results explain the empirical differences in capitalization and profitability across clearinghouses, and show that temporary low funding cost environments create the illusion that clearinghouses are adequately capitalized. The model sheds normative insights: minimum equity requirements can impose additional costs on members because the clearinghouse's equilibrium response is to increase default funds.

Scale & Cap

As the number of cores per server node increases, designing multi-threaded applications has become essential to efficiently utilize the available hardware parallelism. Many application domains have started to adopt multi-threaded programming; thus, efficient management of multi-threaded applications has become a significant research problem. Efficient execution of multi-threaded workloads on cloud environments, where applications are often consolidated by means of virtualization, relies on understanding the multi-threaded specific characteristics of the applications. Furthermore, energy cost and power delivery limitations require data center server nodes to work under power caps, which bring additional challenges to runtime management of consolidated multi-threaded applications. This article proposes a dynamic resource allocation technique for consolidated multi-threaded applications for power-constrained environments. Our technique takes into account application characteristics specific to multi-threaded applications, such as power and performance scaling, to make resource distribution decisions at runtime to improve the overall performance, while accurately tracking dynamic power caps. We implement and evaluate our technique on state-of-the-art servers and show that the proposed technique improves the application performance by up to 21% under power caps compared to a default resource manager.

Feedback-Based Resource Allocation in MapReduce-Based Systems

Containers are considered an optimized fine-grain alternative to virtual machines in cloud-based systems. Some of the approaches which have adopted the use of containers are the MapReduce frameworks. This paper makes an analysis of the use of containers in MapReduce-based systems, concluding that the resource utilization of these systems in terms of containers is suboptimal. In order to solve this, the paper describes AdaptCont, a proposal for optimizing the containers allocation in MapReduce systems. AdaptCont is based on the foundations of feedback systems. Two different selection approaches, Dynamic AdaptCont and Pool AdaptCont, are defined. Whereas Dynamic AdaptCont calculates the exact amount of resources per each container, Pool AdaptCont chooses a predefined container from a pool of available configurations. AdaptCont is evaluated for a particular case, the application master container of Hadoop YARN. As we can see in the evaluation, AdaptCont behaves much better than the default resource allocation mechanism of Hadoop YARN.

The end of the waterfall: Default resources of central counterparties

Central counterparties (CCPs) have become pillars of the new global financial architecture following the financial crisis of 2008. The key role of CCPs in mitigating counterparty risk and contagion has in turn cast them as systemically important financial institutions whose eventual failure may lead to potentially serious consequences for financial stability, and prompted discussions on CCP risk management standards and safeguards for recovery and resolutions of CCPs in case of failure. We contribute to the debate on CCP default resources by focusing on the incentives generated by the CCP loss allocation rules for the CCP and its members and discussing how the design of loss allocation rules may be used to align these incentives in favour of outcomes which benefit financial stability. After reviewing the ingredients of the CCP loss waterfall and various proposals for loss recovery provisions for CCPs, we examine the risk management incentives created by different ingredients in the loss waterfall and discuss possible approaches for validating the design of the waterfall. We emphasise the importance of CCP stress tests and argue that such stress tests need to account for the interconnectedness of CCPs through common members and cross-margin agreements. A key proposal is that capital charges on assets held against CCP default funds should depend on the quality of the risk management of the CCP, as assessed through independent stress tests.

The End of the Waterfall: Default Resources of Central Counterparties

Central counterparties (CCPs) have become pillars of the new global financial architecture following the financial crisis of 2008. The key role of CCPs in mitigating counterparty risk and contagion has in turn cast them as systemically important financial institutions whose eventual failure may lead to potentially serious consequences for financial stability, and prompted discussions on CCP risk management standards and safeguards for recovery and resolutions of CCPs in case of failure. This note contributes to the debate on CCP default resources by focusing on the incentives generated by the CCP loss allocation rules for the CCP and its members and discussing how the design of loss allocation rules may be used to align these incentives in favor of outcomes which benefit financial stability. After reviewing the ingredients of the CCP loss waterfall and various proposals for loss recovery provisions for CCPs, we examine the risk management incentives created by different ingredients in the loss waterfall and discuss possible approaches for validating the design of the waterfall. We emphasize the importance of CCP stress tests and argue that such stress tests need to account for the interconnectedness of CCPs through common members and cross-margin agreements. A key proposal is that capital charges on assets held against CCP Default Funds should depend on the quality of the risk management of the CCP, as assessed through independent stress tests.

Charting the API minefield using software telemetry data

Programs draw significant parts of their functionality through the use of Application Programming Interfaces (APIs). Apart from the way developers incorporate APIs in their software, the stability of these programs depends on the design and implementation of the APIs. In this work, we report how we used software telemetry data to analyze the causes of API failures in Android applications. Specifically, we got 4.9 gb worth of crash data that thousands of applications sent to a centralized crash report management service. We processed that data to extract approximately a million stack traces, stitching together parts of chained exceptions, and established heuristic rules to draw the border between applications and the API calls. We examined a set of more than a half million stack traces associated with risky API calls to map the space of the most common application failure reasons. Our findings show that the top ones can be attributed to memory exhaustion, race conditions or deadlocks, and missing or corrupt resources. Given the classes of the crash causes we identified, we recommend API design and implementation choices, such as specific exceptions, default resources, and non-blocking algorithms, that can eliminate common failures. In addition, we argue that development tools like memory analyzers, thread debuggers, and static analyzers can prevent crashes through early code testing and analysis. Finally, some execution platform and framework designs for process and memory management can also eliminate some application crashes.

Some Thought on JME’s Direction...

We are delighted to take the reins of JME, sharing Editor-in-Chief duties, as a new way of engaging with authors, reviewers, and the entire scholarship of teaching and learning (SOTL) community. There were many reasons to say “yes” to stepping into the Editor-in-Chief role when the OBTS Board asked. JME is close to our hearts and has occupied a special place for us for many years. JME has helped us so often in our teaching and has been our default resource when we have looked to refresh our teaching practice. And JME has been our preferred outlet for our own pedagogy research as a result of both the marvelous review process and the fearless readership with whom we could converse about teaching and learning innovations. We simply could not resist the opportunity to serve the journal in this way. While working with JME is a labor of love, we realize that challenges remain for SOTL moving forward due to a host of factors, not the least of which is the current measurement paradigm that uses citations as the dominant metric. Journal editorship gives us pause to consider opportunities for expanding the management education and development conversation and reframing how pedagogical scholarship is viewed. The issue of where and how pedagogical scholarship fits within the larger disciplinary scholarship mix still exists, and while it is not going away, external stakeholders are demanding metrics that are less esoteric than academic journal citations and more relevant to their needs—such as teaching innovations that positively affect student learning outcomes for a world that needs managers who can effectively navigate a high-velocity environment. In addition, external

Central Counterparties and Their Financial Resources – A Numerical Approach

New regulatory standards require central counterparties (CCPs) to have robust processes in place to mitigate their counterparty credit risk exposures. At the same time, the standards allow CCPs to tailor their risk management models. This paper considers how CCPs can optimally determine the relative mix of initial margin and default fund contributions in a stylised setting, by balancing the costs of default resources with the expected losses they protect against. Where members are of good credit quality and the probability of experiencing losses is low, the loss-mutualising properties of the default fund are favoured over the defaulter-pays properties of initial margin. Significant tail risks in the markets cleared by the CCP further favour the use of the default fund as a cost-effective insurance against potentially large losses. By contrast, when members are more likely to default or extreme losses are unlikely, the CCP has incentives to maximise the defaulter-pays collateral it takes, and the benefits of the loss-mutualising default fund are reduced. Our numerical results support the recognition that CCPs should have some discretion over how they set the optimal level and composition of their default resources, based on the specific risks of the markets and portfolios that they clear. Our results also show that changes in collateral costs and capital requirements can have a significant impact on a CCP’s optimal risk management choices.