Single system image: A survey

Single system image is a computing paradigm where a number of distributed computing resources are aggregated and presented via an interface that maintains the illusion of interaction with a single system. This approach encompasses decades of research using a broad variety of techniques at varying levels of abstraction, from custom hardware and distributed hypervisors to specialized operating system kernels and user-level tools. Existing classification schemes for SSI technologies are reviewed, and an updated classification scheme is proposed. A survey of implementation techniques is provided along with relevant examples. Notable deployments are examined and insights gained from hands-on experience are summarized. Issues affecting the adoption of kernel-level SSI are identified and discussed in the context of technology adoption literature

A Multi-Agent Systems Approach for Peer-to-Peer Energy Trading in Dairy Farming

To achieve desired carbon emission reductions, integrating renewable generation and accelerating the adoption of peer-to-peer energy trading is crucial. This is especially important for energy-intensive farming, like dairy farming. However, integrating renewables and peer-to-peer trading presents challenges. To address this, we propose the Multi-Agent Peer-to-Peer Dairy Farm Energy Simulator (MAPDES), enabling dairy farms to participate in peer-to-peer markets. Our strategy reduces electricity costs and peak demand by approximately 30% and 24% respectively, while increasing energy sales by 37% compared to the baseline scenario without P2P trading. This demonstrates the effectiveness of our approach.Comment: Proc. of the Artificial Intelligence for Sustainability, ECAI 2023, Eunika et al. (eds.), Sep 30- Oct 1, 2023, https://sites.google.com/view/ai4s. 202

Design of an open-source laboratory demonstrator for peer-to-peer trading in local energy markets

There is significant research interest in new energy trading mechanisms based on peer-to-peer or community-based markets, which are more consumer-centric and direct compared to traditional electricity retail markets. Such trading mechanisms require an electricity trading platform that can manage and settle energy transactions from vast numbers of small distributed energy resources, and therefore scalability and interoperability are major challenges. The hardware, communications and software required for implementing local energy trading platforms needs to be designed, tested and demonstrated in a real-time environment. Accordingly, this paper presents a design for an open-source laboratory demonstrator, which allows testing of the hardware and software required for peer-to-peer local energy trading using distributed ledger technology

Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche.

Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality. Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation, but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear. Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (P < 5 × 10(-8)) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with three loci (DLK1-WDR25, MKRN3-MAGEL2 and KCNK9) demonstrating parent-of-origin-specific associations concordant with known parental expression patterns. Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and γ-aminobutyric acid-B2 receptor signalling, among novel mechanisms that regulate pubertal timing in humans. Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Applying reinforcement learning towards automating resource allocation and application scalability in the cloud

Journal articlePublic Infrastructure as a Service (IaaS) clouds such as Amazon, GoGrid and Rackspace deliver computational resources by means of virtualisation technologies. These technologies allow multiple independent virtual machines to reside in apparent isolation on the same physical host. Dynamically scaling applications running on IaaS clouds can lead to varied and unpredictable results because of the performance interference effects associated with co-located virtual machines. Determining appropriate scaling policies in a dynamic non-stationary environment is non-trivial. One principle advantage exhibited by IaaS clouds over their traditional hosting counterparts is the ability to scale resources on-demand. However, a problem arises concerning resource allocation as to which resources should be added and removed when the underlying performance of the resource is in a constant state of flux. Decision theoretic frameworks such as Markov Decision Processes are particularly suited to decision making under uncertainty. By applying a temporal difference, reinforcement learning algorithm known as Q-learning, optimal scaling policies can be determined. Additionally, reinforcement learning techniques typically suffer from curse of dimensionality problems, where the state space grows exponentially with each additional state variable. To address this challenge, we also present a novel parallel Q-learning approach aimed at reducing the time taken to determine optimal policies whilst learning online.Science Foundation Irelan

A learning architecture for scheduling workflow applications in the cloud

Conference paperThe scheduling of workflow applications involves the mapping of individual workflow tasks to computational resources, based on a range of functional and non-functional quality of service requirements. Workflow applications such as scientific workflows often require extensive computational processing and generate significant amounts of experimental data. The emergence of cloud computing has introduced a utility-type market model, where computational resources of varying capacities can be procured on demand, in a pay-per-use fashion. In workflow based applications dependencies exist amongst tasks which requires the generation of schedules in accordance with defined precedence constraints. These constraints pose a difficult planning problem, where tasks must be scheduled for execution only once all their parent tasks have completed. In general the two most important objectives of workflow schedulers are the minimisation of both cost and make span. The cost of workflow execution consists of both computational costs incurred from processing individual tasks, and data transmission costs. With scientific workflows potentially large amounts of data must be transferred between compute and storage sites. This paper proposes a novel cloud workflow scheduling approach which employs a Markov Decision Process to optimally guide the workflow execution process depending on environmental state. In addition the system employs a genetic algorithm to evolve workflow schedules. The overall architecture is presented, and initial results indicate the potential of this approach for developing viable workflow schedules on the Cloud.Science Foundation Irelan

A Learning Architecture to Support Autonomous Resource Scheduling and Allocation in the Cloud

The advent of on-demand computing facilitated by computational clouds, provides an almost unlimited resource supply to support the execution of applications and processes. Through a process known as virtualisation large server machines are divided up into smaller units known as virtual machines. These virtual machines can then be procured on demand to support application deployments, workflow executions and service delivery via the cloud. However, optimally allocating these virtual resources to support a given application deployment or workflow execution on a cloud platform presents a number of significant research challenges. Virtualisation is enabled through a domain level hypervisor which controls access to the shared hardware amongst the competing virtual machines. Switching between domains and attempting to distribute access to these shared mediums is non-trivial and causes performance interference effects amongst the virtual machines. This presents a challenge when attempting to plan a resource allocation to support a given application or workflow running in these environments. Removing these interference effects entirely, is a very difficult problem and is one of the principle challenges facing virtualisation research in the coming years. However from a resource planning perspective it is possible to reason over these variabilities to achieve a near optimal resource allocation which satisfies the defined objective criteria. Markov Decision Processes provide a decision theoretic framework which facilitates planning and scheduling under uncertainty. By modeling the allocation of resources under this framework and solving using techniques such as reinforcement learning and dynamic programming this thesis provides a learning architecture to allocate/schedule resources adhering to defined optimisation criteria. Using data from real cloud deployments we empirically evaluate our proposed solutions with respect to two different application types. The first is a workflow application deployment where the requirement is to schedule tasks to resources to ensure that both cost and makespan constraints are achieved. The second is an application scaling problem where the goal is to optimise application response time at a minimum cost for varying numbers of user requests. For both of these problems the underlying resource is variable and changes accordingly. We present a number of novel advancements from both a learning and optimisation perspective

Autonomous hvac control, a reinforcement learning approach

Abstract—Recent high profile developments of autonomous learning thermostats by companies such as Nest Labs and Honeywell have brought to the fore the possibility of ever greater numbers of intelligent devices permeating our homes and working environments into the future. However, the specific learning approaches and methodologies utilised by these devices have never been made public. In fact little information is known as to the specifics of how these devices operate and learn about their environments or the users who use them. This paper proposes a suitable learning architecture for such an intelligent thermostat in the hope that it will benefit further investigation by the research community. Our architecture comprises a number of different learning methods each of which contributes to create a complete autonomous thermostat capable of controlling a HVAC system. A novel state action space formalism is proposed to enable a Reinforcement Learning agent to successfully control the HVAC system by optimising both occupant comfort and energy costs. Our results show that the learning thermostat can achieve cost savings of 10% over a programmable thermostat, whilst maintaining high occupant comfort standards