Ivona Brandic

• TU Wien

Edge computing provides resources for IoT workloads at the network edge. Monitoring systems are vital for efficiently managing resources and application workloads by collecting, storing, and providing relevant information about the state of the resources. However, traditional monitoring systems have a centralized architecture for both data plane an...

With the increasing capabilities of quantum systems, the efficient, practical execution of quantum programs is becoming more critical. Each execution includes compilation time, which accounts for substantial overhead of the overall program runtime. To address this challenge, proposals that leverage precompilation techniques have emerged, whereby en...

Quantum computing exhibits the unique capability to natively and efficiently encode various natural phenomena, promising theoretical speedups of several orders of magnitude. However, not all computational tasks can be efficiently executed on quantum machines, giving rise to hybrid systems, where some portions of an application run on classical mach...

Large Language Models (LLMs) are becoming integral to daily life, showcasing their vast potential across various Natural Language Processing (NLP) tasks. Beyond NLP, LLMs are increasingly used in software development tasks, such as code completion, modification, bug fixing, and code translation. Software engineers widely use tools like GitHub Copil...

Large language models (LLMs) have shown significant improvements in many natural language processing (NLP) tasks, accelerating their rapid adoption across many industries. These models are resource-intensive, requiring extensive computational resources both during training and inference, leading to increased energy consumption and negative environm...

The deployment of ML models on edge devices is challenged by limited computational resources and energy availability. While split computing enables the decomposition of large neural networks (NNs) and allows partial computation on both edge and cloud devices, identifying the most suitable split layer and hardware configurations is a non-trivial tas...

In recent years, Edge AI has become more prevalent with applications across various industries, from environmental monitoring to smart city management. Edge AI facilitates the processing of Internet of Things (IoT) data and provides privacy-enabled and latency-sensitive services to application users using Machine Learning (ML) algorithms, e.g., Tim...

With the increasing interest in Quantum Machine Learning, Quantum Neural Networks (QNNs) have emerged and gained significant attention. These models have, however, been shown to be notoriously difficult to train, which we hypothesize is partially due to the architectures, called ansatzes, that are hardly studied at this point. Therefore, in this pa...

Mobile devices offload latency-sensitive application tasks to edge servers to satisfy applications' Quality of Service (QoS) deadlines. Consequently, ensuring reliable offloading without QoS violations is challenging in distributed and unreliable edge environments. However, current edge offloading solutions are either centralized or do not adequate...

As we enter the post-Moore era, we experience the rise of various non-von-Neumann-architectures to address the increasing computational demand for modern applications, with quantum computing being among the most prominent and promising technologies. However, this development creates a gap in current computer science curricula since most quantum com...

The Internet of Things is gaining traction for sensing and monitoring outdoor environments such as water bodies, forests, or agricultural lands. Sustainable deployment of sensors for environmental sampling is a challenging task because of the spatial and temporal variation of the environmental attributes to be monitored, the lack of the infrastruct...

The advent of edge devices dedicated to machine learning tasks enabled the execution of AI-based applications that efficiently process and classify the data acquired by the resource-constrained devices populating the Internet of Things. The proliferation of such applications (e.g., critical monitoring in smart cities) demands new strategies to make...

The edge computing paradigm helps handle the Internet of Things (IoT) generated data in proximity to its source. Challenges occur in transferring, storing, and processing this rapidly growing amount of data on resource-constrained edge devices. Symbolic Representation (SR) algorithms are promising solutions to reduce the data size by converting act...

The advent of edge devices dedicated to machine learning tasks enabled the execution of AI-based applications that efficiently process and classify the data acquired by the resource-constrained devices populating the Internet of Things. The proliferation of such applications (e.g., critical monitoring in smart cities) demands new strategies to make...

To address increasing demands of computational resources, scientific applications started to support different type of hardware accelerators, e.g., GPUs, TPUs, ASICs. However, due to the limitation to scalability of hardware resources posed by the Moore Law, a conspicuous amount of research is focusing in integration of Quantum Computers in the ove...

The edge computing paradigm helps handle the Internet of Things (IoT) generated data in proximity to its source. Challenges occur in transferring, storing, and processing this rapidly growing amount of data on resource-constrained edge devices. Symbolic Representation (SR) algorithms are promising solutions to reduce the data size by converting act...

Near real-time edge analytics requires dealing with the rapidly growing amount of data, limited resources, and high failure probabilities of edge nodes. Therefore, data replication is of vital importance to meet SLOs such as service availability and failure resilience. Consequently, specific input datasets, requested by on-demand analytics (e.g., o...

This book constitutes the refereed post-conference proceedings of the 5th International Symposium on Algorithmic Aspects of Cloud Computing, ALGOCLOUD 2019, held in Munich, Germany, in September 2019. The 8 revised full papers were carefully reviewed and selected from 16 submissions. The aim of the symposium is to present research activities and re...

While the modern parallel computing systems offer high performance, utilizing these powerful computing resources to the highest possible extent demands advanced knowledge of various hardware architectures and parallel programming models. Furthermore, optimized software execution on parallel computing systems demands consideration of many parameters...

The rapid cloud computing growth has turned data center energy consumption into a global problem. At the same time, modern cloud providers operate multiple geographically-distributed data centers. Distributed data center infrastructure changes the rules of cloud control, as energy costs depend on current regional electricity prices and temperatures...

New pricing policies are emerging where cloud providers charge resource provisioning based on the allocated CPU frequencies. As a result, resources are offered to users as combinations of different performance levels and prices which can be configured at runtime. With such new pricing schemes and the increasing energy costs in data centres, balanci...

New dynamic cloud pricing options are emerging with cloud providers offering resources as a wide range of CPU frequencies and matching prices that can be switched at runtime. On the other hand, cloud providers are facing the problem of growing operational energy costs. This raises a trade-off problem between energy savings and revenue loss when per...

Optimized software execution on parallel computing systems demands consideration of many parameters at run-time. Determining the optimal set of parameters in a given execution context is a complex task, and therefore to address this issue researchers have proposed different approaches that use heuristic search or machine learning. In this paper, we...

While modern parallel computing systems offer high performance, utilizing these powerful computing resources to the highest possible extent demands advanced knowledge of various hardware architectures and parallel programming models. Furthermore, optimized software execution on parallel computing systems demands consideration of many parameters at...

An UltraScale System (USS) joins parallel and distributed computing systems that will be two to three orders of magnitude larger than today's infrastructure regarding scale, performance, the number of components and their complexity. For such systems to become a reality, however, advances must be made in HPC, large-scale distributed systems, and bi...

New pricing policies are emerging where cloud providers charge resource provisioning based on the allocated CPU frequencies. As a result, resources are offered to users as combinations of different performance levels and prices which can be configured at runtime. With such new pricing schemes and the increasing energy costs in data centres, balanci...

Cloud Computing started by renting computing infrastructures in form of virtual machines, which include hardware resources such as memory and processors. However, due to its popularity it gave birth to Everything-as-a-Service concept, where each service can comprise large variety of software/hardware elements. Although having the same concept, serv...

In recent years, cloud computing providers have been working to provide highly available and scalable cloud services to keep themselves alive in the competitive market of various cloud services. The difficulty is that to provide such high quality services, they need to enlarge data centers (DCs), and consequently, to increase operating costs. Hence...

Monitoring ultrascale systems such as Clouds requires collecting enormous amount of data by periodically reading metric values from a system. Current approaches tend to select a static frequency for sampling monitoring data. On one hand, over-sampling the data by collecting it at high frequencies results in data redundancy during steady runs of the...

Vertical elasticity is recognized as a key enabler for efficient resource utilization of cloud infrastructure through fine-grained resource provisioning, e.g., allowing CPU cycles to be leased for as short as a few seconds. However, little research has been done to support vertical elasticity where the focus is mostly on a single resource, either C...

In recent years, cloud computing providers have been working to provide highly available and scalable cloud services to keep themselves alive in a competitive market. The difficulty is that to provide such high quality services, they need to enlarge data centers (DCs), and consequently, to increase operating costs. Hence, leveraging cost-aware solu...

Energy consumption is one of the main limiting factors for designing and deploying ultrascale systems. Therefore, this paper presents challenges and trends associated with energy efficiency for ultrascale systems based on current activities of the working group on "Energy Efficiency" in the European COST Action Nesus IC1305. The analysis contains m...

The rapid cloud computing growth has turned data center energy consumption into a global problem. At the same time, modern cloud providers operate multiple geographically-distributed data centers. Distributed data center infrastructure changes the rules of cloud control, as energy costs depend on current regional electricity prices and temperatures...

Cloud computing offers the elasticity features by dynamically resizing the infrastructure in response to changes in workload demands to meet performance guarantees and minimize costs. In the last decade, a large body of work has been done in the area of horizontal elasticity, while only few research efforts addressed vertical elasticity. This paper...

New dynamic cloud pricing options are emerging with cloud providers offering resources as a wide range of CPU frequencies and matching prices that can be switched at runtime. On the other hand, cloud providers are facing the problem of growing operational energy costs. This raises a trade-off problem between energy savings and revenue loss when per...

By moving business processes into the cloud, business partners can benefit from lower costs, more flexibility and greater scalability in terms of resources offered by the cloud providers. In order to execute a process or a part of it, a business process owner selects and leases feasible resources while considering different constraints, e.g., Optim...

While the HPC community is working towards the development of the first Exaflop computer (expected around 2020), after reaching the Petaflop milestone in 2008 still only few HPC applications are able to fully exploit the capabilities of Petaflop systems. In this paper we argue that efforts for preparing HPC applications for Exascale should start be...

Keeping the quality of service defined by Service Level Agreements (SLAs) is a key factor to facilitate business operations of Cloud providers. SLA enforcement relies on resource and application monitoring—a topic that has been investigated by various Cloud-related projects. Application-level monitoring still represents an open research issue espec...

Cloud Computing is today's most emphasized Information and Communications Technology (ICT) paradigm that is directly or indirectly used by almost every online user. However, such a great significance comes with a support of a great infrastructure that includes large data centers comprising thousands of server units and other supporting equipment. T...

Almost every online user directly or indirectly uses cloud computing, which is the most promising information and communication technology (ICT) paradigm. However, cloud computing's ultrascale size requires large datacenters comprising thousands of servers and other supporting equipment. The power consumption share of such infrastructures reaches 1...

Computing Clouds offer a new way of using IT facilities including the hardware, storage, applications and networks. The huge resource pool on the Cloud forms an appropriate platform for running applications with both computing and data intensity, like the DNA sequencing workflows. This paper studies the topic of running scientific workflows on mult...

Novel energy-aware cloud management methods dynamically reallocate computation across geographically distributed data centers to leverage regional electricity price and temperature differences. As a result, a managed VM may suffer occasional downtimes. Current cloud providers only offer high availability VMs, without enough flexibility to apply suc...

Cloud Computing started by renting computing infrastructures in form of virtual machines, which include hardware resources such as memory and processors. However, due to its popularity it gave birth to Everything-as-a-Service concept, where each service can comprise large variety of software/hardware elements. Although having the same concept, serv...

Large scale applications are emerged as one of the important applications in distributed computing. Today, the economic and technical benefits offered by the Cloud computing technology encouraged many users to migrate their applications to Cloud. On the other hand, the variety of the existing Clouds requires them to make decisions about which provi...

Cloud computing is a newly emerged computing infrastructure that builds on the latest achievements of diverse research areas, such as Grid computing, Service-oriented computing, business process management and virtualization. An important characteristic of Cloud-based services is the provision of non-functional guarantees in the form of Service Lev...

Cloud computing popularity is growing rapidly and consequently the number of companies offering their services in the form of Software-as-a-Service (SaaS) or Infrastructure-as-a-Service (IaaS) is increasing. The diversity and usage benefits of IaaS offers are encouraging SaaS providers to lease resources from the Cloud instead of operating their ow...

The rapid advancements in recent years of high-throughput technologies in the life sciences are facilitating the generation and storage of huge amount of data in different databases. Despite significant developments in computing capacity and performance, an analysis of these large-scale data in a search for biomedical relevant patterns remains a ch...

Cloud computing revolutionised the industry with its elastic, on-demand approach to computational resources, but has lead to a tremendous impact on the environment. Data centers constitute 1.1-1.5% of total electricity usage in the world. Taking a more informed view of the electrical grid by analysing real-time electricity prices, we set the founda...

Cloud computing represents a novel on-demand computing technology where resources are provisioned in compliance to a set of predefined non-functional properties specified and negotiated by means of service level agreements (SLAs). Currently, cloud providers strive to achieve efficient SLA enforcement strategies to avoid costly SLA violations during...

Quality of service (QoS) can be a critical element for achieving the business goals of a service provider, for the acceptance of a service by the user, or for guaranteeing service characteristics in a composition of services, where a service is defined as either a software or a software-support (i.e., infrastructural) service which is available on...

Scientific community is one of the major driving forces for developing and utilizing IT technologies such as Supercomputers and Grid. Although, the main race has always been for bigger and faster infrastructures, an easier access to such infrastructures in recent years created a demand for more customizable and scalable environments. However, intro...

(article can be downloaded at http://ideas.repec.org/p/snv/dp2009/201296.html) Cloud computing is supposed to offer resources (i.e., data, software, and hardware services) in a manner similar to traditional utilities such as water, electricity, gas, and telephony. However, the current Cloud market is fragmented and static, preventing the successful...

(article can be downloaded at http://ideas.repec.org/p/snv/dp2009/2013100.html) Low liquidity in cloud markets can result in market instability and inefficiency, preventing the successful implementation of ubiquitous computing on demand. To circumvent this issue, it has been suggested to channel demand and supply into a limited number of standardiz...

Scientific applications have always been one of the major driving forces for the development and efficient utilization of large scale distributed systems — computational Grids represent one of the prominent examples. While these infrastructures, such as Grids or Clusters, are widely used for running most of the scientific applications, they still u...

One of the major challenges facing the Cloud paradigm is the emergence of suitable economic platforms for the trading of Cloud services. Today, many researchers investigate how specific Cloud mar-ket platforms can be conceived and in some cases implemented. How-ever, such endeavours consider only specific types of actors, business models, or Cloud...

Cloud computing is a novel computing paradigm that offers data, software, and hardware services in a manner similar to traditional utilities such as water, electricity, and telephony. Usually, in Cloud and Grid computing, contracts between traders are established using Service Level Agreements (SLAs), which include objectives of service usage. Howe...

Grid and cloud computing have changed the IT landscape in the way we access and manage IT infrastructures. Both technologies provide easy-to-use and on-demand access to large-scale infrastructures. Grid and cloud computing are major research areas with strong involvement from both academia and industry. Although significant progress has been made i...

Cloud resources and services are offered based on Service Level Agreements (SLAs) that state usage terms and penalties in case of violations. Although, there is a large body of work in the area of SLA provisioning and monitoring at infrastructure and platform layers, SLAs are usually assumed to be guaranteed at the application layer. However, appli...

Cloud providers aim at guaranteeing Service Level Agreements (SLAs) in a resource-efficient way. This, amongst others, means that resources of virtual (VMs) and physical machines (PMs) have to be autonomically allocated responding to external influences as workload or environmental changes. Thereby, workload volatility (WV) is one of the crucial fa...

Cloud computing utilizes arbitrary mega-scale computing infrastructures and is currently revolutionizing the ICT landscape by allowing remote access to computing power and data over the Internet. Besides the huge economical impact Cloud technology exhibits a high potential to be a cornerstone of a new generation of sustainable and energy-efficient...

Cloud computing is revolutionizing the ICT landscape by providing scalable and efficient computing resources on demand. The ICT industry – especially data centers, are responsible for considerable amounts of CO2 emissions and will very soon be faced with legislative restrictions, such as the Kyoto protocol, defining caps at different organizational...

Cloud computing is a promising concept for the implementation of scalable on-demand computing infrastructures, where resources are provided in a self-managing manner based on predefined customers requirements. A Service Level Agreement (SLA), which is established between a Cloud provider and a customer, specifies these requirements. It includes ter...

Due to the large variety in computing resources, Cloud markets often suffer from a low probability of finding matches between consumers' bids and providers' asks, resulting in low market liquidity. The approach of SLA templates (i.e., templates for electronic contracts) is a means to reduce this variety as it channels the demand and supply. However...

Cloud Computing infrastructures have been developed as individual islands, and mostly proprietary solutions so far. However, as more and more infrastructure providers apply the technology, users face the inevitable question of using multiple infrastructures in parallel. Federated cloud management systems offer a simplified use of these infrastructu...

(article can be downloaded at http://ideas.repec.org/p/snv/dp2009/201177.html) Due to the large variety in computing resources and, consequently, the large number of different types of service level agreements (SLAs), computing resource markets face the problem of a low market liquidity. Restricting the number of different resource types to a small...

Grid portals are web gateways aiming at concealing the underlying infrastructure through a pervasive, transparent, user-friendly, ubiquitous and seamless access to heterogeneous and geographically spread resources (i.e. storage, computational facilities, ...

To guarantee the vision of Cloud Computing QoS goals between the Cloud provider and the customer have to be dynamically met. This so-called Service Level Agreement (SLA) enactment should involve little human-based interaction in order to guarantee the scalability and efficient resource utilization of the system. To achieve this we start from Autono...

Because of the large number of different types of service level agreements (SLAs), computing resource markets face the challenge of low market liquidity. The authors therefore suggest restricting the number of different resource types to a small set of standardized computing resources to counteract this problem. Standardized computing resources are...

Currently, the Cloud landscape is a fragmented, static and shapeless market that hinders the paradigm's ability to fulfil its promise of ubiquitous computing on tap and as a commodity. In this paper, we present our vision of an autonomic self-aware Cloud market platform, and argue that autonomic market platforms for Clouds can step up to the challe...

Dynamic and self-adaptable markets are fundamental for the successful implementation of the Cloud computing paradigm where computing resources are provided on demand and dynamically to the heterogeneous user base. Usually, in Cloud markets, contracts between traders are established using Service Level Agreements (SLAs), which include objectives of...

With the rapid development in recent years of high-throughput technologies in the life sciences, huge amounts of data are being generated and stored in databases. Despite significant advances in computing capacity and performance, an analysis of these large-scale data in a search for biomedically relevant patterns remains a challenging task. Scient...

For today's Traffic Management Systems (TMS), availability is a key concern. Failures in the system limit its availability and have direct impact on the total cost of ownership (TCO) through additional maintenance costs. Therefore, availability parameters are often subject of contracts (so called Service Level Agreements (SLAs)) between system owne...

The emergence of Cloud Computing raises the question of dynamically allocating resources of physical (PM) and virtual machines (VM) in an on-demand and autonomic way. Yet, using Cloud Computing infrastructures efficiently requires fulfilling three partially contradicting goals: first, achieving low violation rates of Service Level Agreements (SLA)...

Cloud Computing represents a novel and promising approach for implementing scalable ICT systems for individual-, communities-, and business-use relying on the latest achievements of diverse research areas, such as Grid computing, Service oriented computing, business processes, and virtualization. From the technological point of view Grid computing...

Provisioning resources as a service in a scalable on-demand manner is a basic feature in Cloud computing technology. Service provisioning in Clouds is based on Service Level Agreements (SLAs) representing a contract signed between the customer and the service provider stating the terms of the agreement including non-functional requirements of the s...