Dynamic Resource Allocation

Computer systems are subject to continuously increasing performance demands. However, energy consumption has become a critical issue, both for high-end large-scale parallel systems [12], as well as for portable devices [34]. In other words, more work needs to be done in less time, preferably with the same or smaller energy budget. Future performance and efficiency goals of computer systems can only be reached with large-scale, heterogeneous architectures [6]. Due to their distributed nature, control software is required to coordinate the parallel execution of applications on such platforms. Abstraction, arbitration and multi-objective optimization are only a subset of the tasks this software has to fulfill [6, 31]. The essential problem in all this is the allocation of platform resources to satisfy the needs of an application.\ud \ud This work considers the dynamic resource allocation problem, also known as the run-time mapping problem. This problem consists of task assignment to (processing) elements and communication routing through the interconnect between the elements. In mathematical terms, the combined problem is defined as the multi-resource quadratic assignment and routing problem (MRQARP). An integer linear programming formulation is provided, as well as complexity proofs on the N P-hardness of the problem.\ud \ud This work builds upon state-of-the-art work of Yagiura et al. [39, 40, 42] on metaheuristics for various generalizations of the generalized assignment problem. Specifically, we focus on the guided local search (GLS) approach for the multi-resource quadratic assignment problem (MRQAP). The quadratic assignment problem defines a cost relation between tasks and between elements. We generalize the multi-resource quadratic assignment problem with the addition of a capacitated interconnect and a communication topology between tasks. Numerical experiments show that the performance of the approach is comparable with commercial solvers. The footprint, the time versus quality trade-off and available metadata make guided local search a suitable candidate for run-time mapping

Run-time Spatial Mapping of Streaming Applications to Heterogeneous Multi-Processor Systems

In this paper, we define the problem of spatial mapping. We present reasons why performing spatial mappings at run-time is both necessary and desirable. We propose what is—to our knowledge—the first attempt at a formal description of spatial mappings for the embedded real-time streaming application domain. Thereby, we introduce criteria for a qualitative comparison of these spatial mappings. As an illustration of how our formalization relates to practice, we relate our own spatial mapping algorithm to the formal model

Using guided local search for adaptive resource reservation in large-scale embedded systems

To maintain a predictable execution environment, an embedded system must ensure that applications are, in advance, provided with sufficient resources to process tasks, exchange information and to control peripherals. The problem of assigning tasks to processing elements with limited resources, and routing communication channels through a capacitated interconnect is combined into an integer linear programming formulation. We describe a guided local search algorithm to solve this problem at run-time. This algorithm allows for a hybrid strategy where configurations computed at design-time may be used as references to lower the computational overhead at run-time. Computational experiments on a dataset with 100 tasks and 20 processing elements show the effectiveness of this algorithm compared to state-of-the-art solvers CPLEX and Gurobi. The guided local search algorithm finds an initial solution within 100 milliseconds, is competitive for small platforms, scales better with the size of the platform, and has lower memory usage (2-19%)

Adaptive resource allocation for streaming applications

Streaming applications often have latency and throughput requirements due to timing critical signal processing, or the time critical interaction with their environment. Mapping such applications to a multi-core architecture is commonly done at design-time to be able to analyze the complex design-space. However, such design-flows cannot deal with a dynamic platform or a dynamic set of applications. Hardware faults and resources claimed by other applications may render the assumed available resources inaccessible. To avoid the assumptions posed on the state of the platform by a fixed resource allocation, applica- tions should be designed with location transparency in mind. Applications must be analyzed at design-time to determine the required resource budget, independent of which specific resources will be allocated. Sufficient performance can be guaranteed when such applications are mapped onto an architecture in which each resource is arbitrated using a budget scheduler. Within the Cutting edge Reconfigurable ICs for Stream Processing (CRISP) project, a many-core platform is developed that adheres to these requirements. Using the configuration features of the platform, the system is able to control at run-time what resources are being used by the applications. This paper shows that run-time resource allocation can effectively adapt to the available set of resources, providing partial distribution transparency to the user. As an example, a GNSS receiver is mapped to the platform containing faulty hardware components. A few resources remain critical, but in most cases the faulty components can be circumvented, such that adequate resources can be allocated to the application at run-time

Designing Reliable Cyber-Physical Systems

Cyber-physical systems, that consist of a cyber part a computing System and a physical part the system in the physical environment as well as the respective interfaces between those parts, are omnipresent in our daily lives. The application in the physical environment drives the overall requirements that must be respected when designing the computing system. Here, reliability is a core aspect where some of the most pressing design challenges are: *monitoring failures throughout the computing system, *determining the impact of failures on the application constraints, and *ensuring correctness of the computing system with respect to application-driven requirements rooted in the physical environment. *This chapter gives an overview of the state-of-the-art techniques developed within the Horizon 2020 project IMMORTAL that tackle these challenges throughout the stack of layers of the computing system while tightly coupling the design methodology to the physical requirements. (The chapter is based on the contributions of the special session Designing Reliable Cyber-Physical Systems of the Forum on Specification and Design Languages (FDL) 2016.