TANGO: Transparent heterogeneous hardware Architecture deployment for eNergy Gain in Operation

The paper is concerned with the issue of how software systems actually use Heterogeneous Parallel Architectures (HPAs), with the goal of optimizing power consumption on these resources. It argues the need for novel methods and tools to support software developers aiming to optimise power consumption resulting from designing, developing, deploying and running software on HPAs, while maintaining other quality aspects of software to adequate and agreed levels. To do so, a reference architecture to support energy efficiency at application construction, deployment, and operation is discussed, as well as its implementation and evaluation plans.Comment: Part of the Program Transformation for Programmability in Heterogeneous Architectures (PROHA) workshop, Barcelona, Spain, 12th March 2016, 7 pages, LaTeX, 3 PNG figure

On the integration of application level and resource level QoS control for real-time applications

We consider a dynamic set of soft real-time applications using a set of shared resources. Each application can execute in different modes, each one associated with a level of Quality of Service (QoS). Resources, in their turn, have different modes, each one with a speed and a power consumption, and are managed by a Reservation Based scheduler enabling a dynamic allocation of the fraction of resources (bandwidth) assigned to each application. To cope with dynamic changes of the application, we advocate an adaptive resource allocation policy organised in two nested feedback loops. The internal loop operates on the scheduling parameter to obtain a resource allocation that meets the temporal constraints of the applications. The external loop operates on the QoS level of the applications and on the power level of the resources to strike a good trade-off between the global QoS and the energy consumption. This loop comes into play whenever the workload of the application exceeds the bounds that permit the internal loop to operate correctly, or whenever it decreases below a level that permit more aggressive choices for the QoS or substantial energy saving

Cloud engineering is search based software engineering too

Many of the problems posed by the migration of computation to cloud platforms can be formulated and solved using techniques associated with Search Based Software Engineering (SBSE). Much of cloud software engineering involves problems of optimisation: performance, allocation, assignment and the dynamic balancing of resources to achieve pragmatic trade-offs between many competing technical and business objectives. SBSE is concerned with the application of computational search and optimisation to solve precisely these kinds of software engineering challenges. Interest in both cloud computing and SBSE has grown rapidly in the past five years, yet there has been little work on SBSE as a means of addressing cloud computing challenges. Like many computationally demanding activities, SBSE has the potential to benefit from the cloud; ‘SBSE in the cloud’. However, this paper focuses, instead, of the ways in which SBSE can benefit cloud computing. It thus develops the theme of ‘SBSE for the cloud’, formulating cloud computing challenges in ways that can be addressed using SBSE

How migrating 0.0001% of address space saves 12% of energy in hybrid storage

We present a simple, operating-\ud system independent method to reduce the num-\ud ber of seek operations and consequently reduce\ud the energy consumption of a hybrid storage\ud device consisting of a hard disk and a flash\ud memory. Trace-driven simulations show that\ud migrating a tiny amount of the address space\ud (0.0001%) from disk to flash already results\ud in a significant storage energy reduction (12%)\ud at virtually no extra cost. We show that the\ud amount of energy saving depends on which part\ud of the address space is migrated, and we present\ud two indicators for this, namely sequentiality and\ud request frequency. Our simulations show that\ud both are suitable as criterion for energy-saving\ud file placement methods in hybrid storage. We\ud address potential wear problems in the flash\ud subsystem by presenting a simple way to pro-\ud long its expected lifetime.\u

A hierarchical run-time adaptive resource allocation framework for large-scale MPSoC systems

In the embedded computer system domain, MPSoC systems have become increasingly popular due to the ever-increasing performance demands of modern embedded applications. The number of processing elements in these MPSoCs also steadily increases. Whereas current MPSoCs still contain a limited number of processing elements, future MPSoCs will feature tens up to hundreds of (heterogeneous) processing elements that are all integrated on a single chip. On these future large-scale MPSoC systems, the mapping of applications onto the hardware resources plays an important role to fully explore the parallelism of applications. In this article, a hierarchical run-time adaptive resource allocation framework which uses an intelligent task remapping approach is proposed to improve the system performance for large-scale MPSoCs