The Edge-Cloud Continuum is a large-scale, loosely coupled system consisting of multiple stakeholders, regions, dynamic infrastructures, and conflicting objectives. With surging growth and demand, the Continuum's energy and carbon footprint have massively increased, resulting in great operational expense, environmental impact, and strain on power grids. Methods to mitigate this face significant challenges: Quality of Service (QoS) guarantees must be balanced against not only carbon emissions, but the loadings, capacities, and QoS of the (smart) grids that power the underlying infrastructure. Integrated models to enable reasoning across both a Continuum and its associated Smart Grids are therefore required. This work presents a formal model to reason across the integration of Smart Grids and the Edge-Cloud Continuum. Firstly, we identify the components, interactions, and properties crucial to mitigating cross-Continuum energy and carbon footprint while maintaining user, provider, and power grid QoS. We then present associated mathematical models to enable a model-based simulation to be developed based on our work. We present this simulation (all code is available for download) and use a simple scheduling algorithm to demonstrate the feasibility of utilizing knowledge from both the Smart Grid and Edge-Cloud Continuum for carbon and energy management, showing that significant savings are possible.

As microservice-based systems scale across the cloud-edge continuum, traditional centralized scheduling mechanisms increasingly struggle with latency, coordination overhead, and fault tolerance. This paper presents a new architectural direction: leveraging service mesh sidecar proxies as decentralized, in-situ schedulers to enable scalable, low-latency coordination in large-scale, cloud-native environments. We propose embedding lightweight, autonomous scheduling logic into each sidecar, allowing scheduling decisions to be made locally without centralized control. This approach leverages the growing maturity of service mesh infrastructures, which support programmable distributed traffic management. We describe the design of such an architecture and present initial results demonstrating its scalability potential in terms of response time and latency under varying request rates. Rather than delivering a finalized scheduling algorithm, this paper presents a system-level architectural direction and preliminary evidence to support its scalability potential.

Collection and analysis of distributed (cloud) computing workloads allows for a deeper understanding of user and system behavior and is necessary for efficient operation of infrastructures and applications. The availability of such workload data is however often limited as most cloud infrastructures are commercially operated and monitoring data is considered proprietary or falls under GPDR regulations. This work investigates the generation of synthetic workloads using Generative Adversarial Networks and addresses a current need for more data and better tools for workload generation. Resource utilization measurements such as the utilization rates of Content Delivery Network (CDN) caches are generated and a comparative evaluation pipeline using descriptive statistics and time-series analysis is developed to assess the statistical similarity of generated and measured workloads. We use CDN data open sourced by us in a data generation pipeline as well as back-end ISP workload data to demonstrate the multivariate synthesis capability of our approach. The work contributes a generation method for multivariate time series workload generation that can provide arbitrary amounts of statistically similar data sets based on small subsets of real data. The presented technique shows promising results, in particular for heterogeneous workloads not too irregular in temporal behavior.

Power budgeting is a commonly employed solution to reduce the negative consequences of high power consumption of large scale data centers. While various power budgeting techniques and algorithms have been proposed at different levels of data center infrastructures to optimize the power allocation to servers and hosted applications, testing them has been challenging with no available simulation platform that enables such testing for different scenarios and configurations. To facilitate evaluation and comparison of such techniques and algorithms, we introduce a simulation model for Quality-of-Service aware power budgeting and its implementation in CloudSim. We validate the proposed simulation model against a deployment on a real testbed, showcase simulator capabilities, and evaluate its scalability.

This paper addresses the problem of workload generation for distributed applications in fog/edge computing. Unlike most existing work that tends to generate workload data for individual network nodes using historical data from the targeted node, this work aims to extrapolate supplementary workloads for entire application / infrastructure graphs through diffusion of measurements from limited subsets of nodes. A framework for workload generation is proposed, which defines five diffusion algorithms that use different techniques for data extrapolation and generation. Each algorithm takes into account different constraints and assumptions when executing its diffusion task, and individual algorithms are applicable for modeling different types of applications and infrastructure networks. Experiments are performed to demonstrate the approach and evaluate the performance of the algorithms under realistic workload settings, and results are validated using statistical techniques.

Constantly growing power consumption of data centers is a major concern from environmental and economical reasons. Current approaches to reduce negative consequences of high power consumption focus on limiting the peak power consumption. During high workload periods, power consumption of highly utilized servers is throttled to stay within the power budget. However, the peak power reduction affects performance of hosted applications and thus leads to Quality of Service violations. In this paper, we introduce Power Shepherd, a hierarchical system for application performance aware power shifting. Power Shepherd reduces the data center operational costs by redistributing the available power among applications hosted in the cluster. This is achieved by, assigning server power budgets by the cluster controller, enforcing these power budgets using Running Average Power Limit (RAPL), and prioritizing applications within each server by adjusting the CPU scheduling configuration. We implement a prototype of the proposed solution and evaluate it in a real testbed equipped with power meters and using representative cloud applications. Our experiments show that Power Shepherd has potential to manage a cluster consisting of thousands of servers and limit the increase of operational costs by a significant amount when the cluster power budget is limited and the system is overutilized. Finally, we identify some outstanding challenges regarding model sensitivity and the fact that this approach in its current from is not beneficial to be used in all situations, e.g., when the system is underutilized.

Content Delivery Networks (CDNs) are handling a large part of the traffic over the Internet and are of growing importance for management and operation of coming generations of data intensive applications. This paper addresses modeling and scaling of content-oriented applications, and presents workload, application, and infrastructure models developed in collaboration with a large-scale CDN operating infrastructure provider aimed to improve the performance of content delivery subsystems de- ployed in wide area networks. It has been shown that leveraging edge resources for the deployment of caches of content greatly benefits CDNs. Therefore, the models are described from an edge computing perspective and intended to be integrated in network topology aware application orchestration and resource management systems.

Scientific workflows are increasingly common in the workloads of current High Performance Computing (HPC) systems. However, HPC schedulers do not incorporate workflow-specific mechanisms beyond the capacity to declare dependencies between their jobs. Thus, workflows are run as sets of batch jobs with dependencies, which induces long intermediate wait times and, consequently, long workflow turnaround times. Alternatively, to reduce their turnaround time, workflows may be submitted as single pilot jobs that are allocated their maximum required resources for their entire runtime. Pilot jobs achieve shorter turnaround times but reduce the HPC system's utilization because resources may idle during the workflow's execution. We present a workflow-aware scheduling (WoAS) system that enables existing scheduling algorithms to exploit fine-grained information on a workflow's resource requirements and structure without modification. The current implementation of WoAS is integrated into Slurm, a widely used HPC batch scheduler. We evaluate the system using a simulator using real and synthetic workflows and a synthetic baseline workload that captures job patterns observed over three years of workload data from Edison, a large supercomputer hosted at the National Energy Research Scientific Computing Center. Our results show that WoAS reduces workflow turnaround times and improves system utilization without significantly slowing down conventional jobs.

The high performance computing (HPC) scheduling landscape is changing. Increasingly, there are large scientific computations that include high-throughput, data-intensive, and stream-processing compute models. These jobs increase the workload heterogeneity, which presents challenges for classical tightly coupled MPI job oriented HPC schedulers. Thus, it is important to define new analyses methods to understand the heterogeneity of the workload, and its possible effect on the performance of current systems. In this paper, we present a methodology to assess the job heterogeneity in workloads and scheduling queues. We apply the method on the workloads of three current National Energy Research Scientific Computing Center (NERSC) systems in 2014. Finally, we present the results of such analysis, with an observation that heterogeneity might reduce predictability in the jobs' wait time.

In this paper, we propose a telco cloud meta-model that can be used to simulate different infrastructure configurations and explore their consequences for system performance and costs. To achieve this, we analyse current telecommunication and data centre infrastructure paradigms, describe the architecture of the telco cloud, and detail the benefits of merging both infrastructures in a unified system. Next, we detail the dynamics of the telco cloud and identify the components that are the most relevant from the perspective of modelling performance and cost. As a number of well established simulation technologies exist for most of the telco cloud components, we survey existing models in an attempt to construct a suitable composite meta-model. Finally, we present a showcase scenario to demonstrate the scope of our telco cloud simulator.

High performance computing centers have traditionally served monolithic MPI applications. However, in recent years, many of the large scientific computations have included high throughput and data-intensive jobs. HPC systems have mostly used batch queue schedulers to schedule these workloads on appropriate resources. There is a need to understand future scheduling scenarios that can support the diverse scientific workloads in HPC centers. In this paper, we analyze the workloads on two systems (Hopper, Carver) at the National Energy Research Scientific Computing (NERSC) Center. Specifically, we present a trend analysis towards understanding the evolution of the workload over the lifetime of the two systems.

In this work we address service-oriented software development in distributed computing environments, and investigate an approach to software development and integration based on code generation. The approach is illustrated in a toolkit for multi-language software generation built on three building blocks; a service description language, a serialization and transport protocol, and a set of code generation techniques. The approach is intended for use in the eScience domain and aims to reduce the complexity of development of distributed software systems through a low-knowledge-requirements model for construction of network-accessible services. The toolkit is presented along with a discussion of use cases and a performance evaluation quantifying the performance of the toolkit against alternative techniques for code generation and service communication. In tests of communication overhead and response time, toolkit performance is found to be comparable to or improve upon the performance of state-of-the-art tools.

Recent advances in hardware development coupled with the rapid adoption and broad applicability of cloud computing have introduced widespread heterogeneity in data centers, significantly complicating the management of cloud applications and data center resources. This paper presents the CACTOS approach to cloud infrastructure automation and optimization, which addresses heterogeneity through a combination of in-depth analysis of application behavior with insights from commercial cloud providers. The aim of the approach is threefold: to model applications and data center resources, to simulate applications and resources for planning and operation, and to optimize application deployment and resource use in an autonomic manner. The approach is based on case studies from the areas of business analytics, enterprise applications, and scientific computing.

Fairshare scheduling is an established technique to provide user-level differentiation in management of capacity consumption in high-performance and grid computing scheduler systems. In this paper we extend on a state-of-the-art approach to decentralized grid fairs hare and propose a generalized model for construction of decentralized prioritization-based management systems. The approach is based on (re)formulation of control problems as prioritization problems, and a proposed framework for computationally efficient decentralized priority calculation. The model is presented along with a discussion of application of decentralized management systems in distributed computing environments that outlines selected use cases and illustrates key trade-off behaviors of the proposed model.

In this paper we address reduction of complexity in management of scientific computations in distributed computing environments. We explore an approach based on separation of computation design (application development) and distributed execution of computations, and investigate best practices for construction of virtual infrastructures for computational science - software systems that abstract and virtualize the processes of managing scientific computations on heterogeneous distributed resource systems. As a result we present StratUm, a toolkit for management of eScience computations. To illustrate use of the toolkit, we present it in the context of a case study where we extend the capabilities of an existing kinetic Monte Carlo software framework to utilize distributed computational resources. The case study illustrates a viable design pattern for construction of virtual infrastructures for distributed scientific computing. The resulting infrastructure is evaluated using a computational experiment from molecular systems biology.