Feedback and time are essential for the optimal control of computing systems

The performance, reliability, cost, size and energy usage of computing systems can be improved by one or more orders of magnitude by the systematic use of modern control and optimization methods. Computing systems rely on the use of feedback algorithms to schedule tasks, data and resources, but the models that are used to design these algorithms are validated using open-loop metrics. By using closed-loop metrics instead, such as the gap metric developed in the control community, it should be possible to develop improved scheduling algorithms and computing systems that have not been over-engineered. Furthermore, scheduling problems are most naturally formulated as constraint satisfaction or mathematical optimization problems, but these are seldom implemented using state of the art numerical methods, nor do they explicitly take into account the fact that the scheduling problem itself takes time to solve. This paper makes the case that recent results in real-time model predictive control, where optimization problems are solved in order to control a process that evolves in time, are likely to form the basis of scheduling algorithms of the future. We therefore outline some of the research problems and opportunities that could arise by explicitly considering feedback and time when designing optimal scheduling algorithms for computing systems

Real-time scheduling of transactions in multicore systems

International audienceTransactional memory has attracted much interest for multicore systems as it eases programming and avoids the problems of lock-based methods. However, introducing real-time scheduling of transactions in multicore systems is an open problem. Existing solutions for real-time scheduling consider either tasks in multiprocessor systems or transactions in database systems. In this paper, we show that these solutions are not suitable for multicore systems. And we discuss the main challenges to introduce real-time scheduling within transactional memory in multicore systems

Resource-Constrained Embedded Control Systems: Possibilities and Research Issues

A survey that points out research issues and open problems in the area of integrated control and real-time scheduling. Issues that are discussed include temporal robustness, schedulability margin, optimal and direct feedback scheduling, quality-of-control, and tools

Power-Aware Real-Time Scheduling: Models, Open Problems, and Practical Considerations

Power-related issues have received considerable research attention from the real-time community in the past decade. In our talk, we introduce a recent model and set of assumptions made in the recent real-time literature on energy and thermal issues; suggest two high-level open problems for power-aware real-time scheduling: {em peak-temperature minimization} and {em energy-minimization with temperature as a constraint}; and discuss practical considerations that should be considered in proposed solutions

Partitioning the Network-on-Chip to Enable Virtualization on Many-Core Processors

6th International Real-Time Scheduling Open Problems Seminar (RTSOPS 2015), Lund, Sweden.In this paper, we highlight some key problems in NoC based architectures that must be addressed before the deployment of real-time applications onto these platforms becomes possible. A paradigm shift from function centric to data and communication centric approaches is required. Combining hardware and software based flow-regulation seems to be the only way to ensure that NoCs go beyond the best-effort service and address the requirements of diverse applications

Utility-Aware Scheduling of Stochastic Real-Time Systems

Time utility functions offer a reasonably general way to describe the complex timing constraints of real-time and cyber-physical systems. However, utility-aware scheduling policy design is an open research problem. In particular, scheduling policies that optimize expected utility accrual are needed for real-time and cyber-physical domains. This dissertation addresses the problem of utility-aware scheduling for systems with periodic real-time task sets and stochastic non-preemptive execution intervals. We model these systems as Markov Decision Processes. This model provides an evaluation framework by which different scheduling policies can be compared. By solving the Markov Decision Process we can derive value-optimal scheduling policies for moderate sized problems. However, the time and memory complexity of computing and storing value-optimal scheduling policies also necessitates the exploration of other more scalable solutions. We consider heuristic schedulers, including a generalization we have developed for the existing Utility Accrual Packet Scheduling Algorithm. We compare several heuristics under soft and hard real-time conditions, different load conditions, and different classes of time utility functions. Based on these evaluations we present guidelines for which heuristics are best suited to particular scheduling criteria. Finally, we address the memory complexity of value-optimal scheduling, and examine trade-offs between optimality and memory complexity. We show that it is possible to derive good low complexity scheduling decision functions based on a synthesis of heuristics and reduced-memory approximations of the value-optimal scheduling policy

Flow-time Optimization for Concurrent Open-Shop and Precedence Constrained Scheduling Models

Scheduling a set of jobs over a collection of machines is a fundamental problem that needs to be solved millions of times a day in various computing platforms: in operating systems, in large data clusters, and in data centers. Along with makespan, flow-time, which measures the length of time a job spends in a system before it completes, is arguably the most important metric to measure the performance of a scheduling algorithm. In recent years, there has been a remarkable progress in understanding flow-time based objective functions in diverse settings such as unrelated machines scheduling, broadcast scheduling, multi-dimensional scheduling, to name a few. Yet, our understanding of the flow-time objective is limited mostly to the scenarios where jobs have no dependencies. On the other hand, in almost all real world applications, think of MapReduce settings for example, jobs have dependencies that need to be respected while making scheduling decisions. In this paper, we take first steps towards understanding this complex problem. In particular, we consider two classical scheduling problems that capture dependencies across jobs: 1) concurrent open-shop scheduling (COSSP) and 2) precedence constrained scheduling. Our main motivation to study these problems specifically comes from their relevance to two scheduling problems that have gained importance in the context of data centers: co-flow scheduling and DAG scheduling. We design almost optimal approximation algorithms for COSSP and PCSP, and show hardness results

Scalable Scheduling Policy Design for Open Soft Real-Time Systems

Open soft real-time systems, such as mobile robots, must respond adaptively to varying operating conditions, while balancing the need to perform multiple mission specific tasks against the requirement that those tasks complete in a timely manner. Setting and enforcing a utilization target for shared resources is a key mechanism for achieving this behavior. However, because of the uncertainty and non-preemptability of some tasks, key assumptions of classical scheduling approaches do not hold. In previous work we presented foundational methods for generating task scheduling policies to enforce proportional resource utilization for open soft real-time systems with these properties. However, these methods scale exponentially in the number of tasks, limiting their practical applicability. In this paper, we present a novel parameterized scheduling policy that scales our technique to a much wider range of systems. These policies can represent geometric features of the scheduling policies produced by our earlier methods, but only require a number of parameters that is quadratic in the number of tasks. We provide empirical evidence that the best of these policies are competitive with exact solution methods in small problems, and significantly outperform heuristic methods in larger ones

Real-time scheduling of transactions in multicore systems

International audienceTransactional memory has attracted much interest for multicore systems as it eases programming and avoids the problems of lock-based methods. However, introducing real-time scheduling of transactions in multicore systems is an open problem. Existing solutions for real-time scheduling consider either tasks in multiprocessor systems or transactions in database systems. In this paper, we show that these solutions are not suitable for multicore systems. And we discuss the main challenges to introduce real-time scheduling within transactional memory in multicore systems

Analysis of self-interference within DAG tasks

6th Real-Time Scheduling Open Problems Seminar (RTSOPS 2015), Lund, Sweden.No abstract (2-pages paper) Few years ago, the frontier separating the real-time embedded domain from the high-performance computing domain was neat and clearly defined. Nowadays, many contemporary applications no longer find their place in either category as they manifest both strict timing constraints and work-intensive computational demands. The only way forward to cope with such orthogonal requirements is to embrace the parallel execution programming paradigm on the emergent scalable and energy-efficient multicore/many-core architecture