We propose enabling continuous performance optimisation of distributed hybrid applications in heterogeneous cloud, Edge, and HPC environments by employing an intelligent re-deployment feedback loop.

We propose to tackle the complexity of deploying and operating modern applications onto heterogeneous HPC and cloud-based systems by providing application developers and infrastructure operators with tools to abstract their application and infrastructure requirements.

Abstract IoT-based applications need to be dynamically orchestrated on cloud-edge infrastructures for reasons such as performance, regulations, or cost. In this context, a crucial problem is facilitating the work of DevOps teams in deploying, monitoring, and managing such applications by providing necessary tools and platforms. The SODALITE@RT open-source framework aims at addressing this scenario. In this paper, we present the main features of the SODALITE@RT: modeling of cloud-edge resources and applications using open standards and infrastructural code, and automated deployment, monitoring, and management of the applications in the target infrastructures based on such models. The capabilities of the SODALITE@RT are demonstrated through a relevant case study.

We present the Disaster Recovery Layer (DRL) that enables OpenStack-managed datacenter workloads, Virtual Machines (VMs) and Volumes, to be protected and recovered in another datacenter, in case of a disaster. This work has been carried out in the context of the EU FP7 ORBIT project that develops technologies for enabling business continuity as a service. The DRL framework is based on a number of autonomous components and extensions of OpenStack modules, while its functionalities are available through OpenStack's Horizon UI and command line interface. Also, the DRL's architecture is extensible, allowing for the easy and dynamic integration of protection, restoration and orchestration plug-ins that adopt new approaches. A distributed disaster detection mechanism was also developed for identifying datacenter disasters and alerting the DRL. For the evaluation of the DRL, a two (active and backup) datacenters testbed has been setup in respective sites in Umea and Lulea, 265km apart and connected through the Swedish national research and education network. In case of a disaster, traffic is redirected between the datacenters utilizing the BGP anycast scheme. The experiments performed, show that DRL can efficiently protect VMs and Volumes, with minimum service disruption in case of failures and low overhead, even when the available bandwidth is limited.

In the KVM hypervisor, incoming packets from the network must pass through several objects in the Linux kernel before being delivered to the guest VM. Currently, both the hypervisor and the guest keep their own sets of buffers on the receive path. For large packets, the overall processing time is dominated by the copying of data from hypervisor buffers to guest buffers.

Reduction of disk drive power consumption is a challenging task, particularly since the most prevalent way of achieving it, powering down idle disks, has many undesirable side-effects. Some hard disk drives support acoustic modes, meaning they can be configured to reduce the acceleration and velocity of the disk head. This reduces instantaneous power consumption but sacrifices performance. As a result, input/output (I/O) operations run longer at reduced power. This is useful for power capping since it causes significant reduction in peak power consumption of the disks. We conducted experiments on several disk drives that support acoustic management. Most of these disk drives support only two modes - quiet and normal. We ran different I/O workloads, including SPC-1 to simulate a real-world online transaction processing workload. We found that the reduction in peak power can reach up to 23% when using quiet mode. We show that for some workloads this translates into a reduction of 12.5% in overall energy consumption. In other workloads we encountered the opposite phenomenon-an increase of more than 6% in the overall energy consumption.

Power consumption is a major issue in today's datacenters. Storage typically comprises a significant percentage of datacenter power. Thus, understanding, managing, and reducing storage power consumption is an essential aspect of any efforts that address the total power consumption of datacenters. We developed a scalable power modeling method that estimates the power consumption of storage workloads. The modeling concept is based on identifying the major workload contributors to the power consumed by the disk arrays.

The SPANNER software environment for the specification and analysis of concurrent process coordination and resource sharing coordination is described. In the SPANNER environment, one can formally produce a specification of a distributed computing problem, and then verify its validity through reachability analysis and simulation. SPANNER is based on a finite-state machine model called the selection/resolution model. The capabilities of SPANNER are illustrated by the analysis of two classical coordination problems: (1) the dining philosophers; and (2) Dijkstra's concurrent programming problem. In addition, some of the more recently implemented capabilities of the SPANNER system are discussed, such as process types and cluster variables.>

SPANNER is a software package for the specification, analysis, and evaluation of protocols. It is based on a mathematical model of coordinating processes called the selection/resolution model.