Time-bounded distributed QoS-aware service configuration in heterogeneous cooperative environments

Abstract

The scarcity and diversity of resources among the devices of heterogeneous computing environments may affect their ability to execute services within the users' requested Quality of Service levels, particularly in open real-time environments where the characteristics of the computational load cannot always be predicted in advance but, nevertheless, response to events still has to be provided within precise timing constraints in order to guarantee a desired level of performance. This paper proposes a cooperative service execution, allowing resource constrained devices to collectively execute services with their more powerful neighbours, meeting non-functional requirements that otherwise would not be met by an individual execution. Nodes dynamically group themselves into a new coalition, allocating resources to each new service and establishing an initial service configuration which maximises the satisfaction of the QoS constraints associated with the new service and minimises the impact on the global QoS caused by the new service's arrival.However, the increased complexity of open real-time environments may prevent the possibility of computing optimal local and global resource allocations within a useful and bounded time. As such, the QoS optimisation problem is here reformulated as a heuristic-based anytime optimisation problem that can be interrupted at any time and quickly respond to environmental changes. Extensive simulations demonstrate that the proposed anytime algorithms are able to quickly find a good initial service solution and effectively optimise the rate at which the quality of the current solution improves at each iteration of the algorithms, with an overhead that can be considered negligible when compared against the introduced benefits. AbstractThe scarcity and diversity of resources among the devices of heterogeneous computing environments may affect their ability to execute services within the users' requested Quality of Service levels, particularly in open real-time environments where the characteristics of the computational load cannot always be predicted in advance but, nevertheless, response to events still has to be provided within precise timing constraints in order to guarantee a desired level of performance. This paper proposes a cooperative service execution, allowing resource constrained devices to collectively execute services with their more powerful neighbours, meeting non-functional requirements that otherwise would not be met by an individual execution. Nodes dynamically group themselves into a new coalition, allocating resources to each new service and establishing an initial service configuration which maximises the satisfaction of the QoS constraints associated with the new service and minimises the impact on the global QoS caused by the new service's arrival.However, the increased complexity of open real-time environments may prevent the possibility of computing optimal local and global resource allocations within a useful and bounded time. As such, the QoS optimisat...

Flexible and Dynamic Replication Control for Interdependent Distributed Real-Time Embedded Systems

“…. , c n } as a set of software components c i being cooperatively executed by a coalition of nodes [2]. Each component c i is defined by its functionality, is able to send and receive messages, is available at a certain point of the network, and has a set of QoS parameters that can be changed in order to adapt service provisioning to a dynamically changing environment.…”

“…Nodes dynamically group themselves into a new coalition, cooperatively allocating resources to each new service and establishing an initial Service Level Agreement (SLA) that maximises the satisfaction of the user's QoS constraints associated with the new service while minimises the impact on the global system's QoS caused by the new service's arrival [2]. Within a coalition, each component c i ∈ S will then be executed at a QoS level L minimum ≤ Q i val ≤ L desired at a node n i .…”

“…However, due to the heterogeneity and dynamically varying workloads of nodes in the system, it is not guaranteed that at least one of the backups will be able to locally reserve the needed resources to output such quality level. Such feasibility is determined by the anytime local QoS optimisation algorithm of [2], which aims to minimise the impact of the activation of a new component on the currently provided QoS level of previously activated components at a particular node.…”

“…Let Q i val be the QoS level that was being outputted by the primary replica of component c i ∈ S that has failed. If no passive replica of c i is able to output the same QoS level, select the one which is able to output the QoS level Q [2]. We assume that coalition partners are willing to collaborate in order to achieve a global coalition's consistency, even if this might reduce the utility of their local optimisations.…”

“…Our previous work [2] applied concepts of cooperative QoS-aware computing to address such challenges, emerging as a promising distributed computing paradigm to face the stringent demands on resources and performance of new embedded real-time systems. Available software components can be shared among different services and can be adapted at runtime to varying operational conditions, enhancing the efficiency in the use of the available resources.…”