We study scheduling problems in battery-operated computing devices, aiming at schedules with low total energy consumption. While most of the previous work has focused on finding feasible schedules in deadline-based settings, in this paper we are interested in schedules that guarantee good response times. More specifically, our goal is to schedule a sequence of jobs on a variable speed processor so as to minimize the total cost consisting of the power consumption and the total flow time of all the jobs. We first show that when the amount of work, for any job, may take an arbitrary value, then no online algorithm can achieve a constant competitive ratio. Therefore, most of the paper is concerned with unit-size jobs. We devise a deterministic constant competitive online algorithm and show that the offline problem can be solved in polynomial time.

Energy-efficient algorithms for flow time minimization

An energy-efficient, QoS-aware and cost-effective scheduling approach for real-time workflow applications in cloud computing systems utilizing DVFS and approximate computations

Energy efficient multi-objective scheduling of tasks with interval type-2 fuzzy timing constraints in an Industry 4.0 ecosystem

With the advent of heterogeneous multiprocessor architectures, efficient scheduling for high performance has been of significant importance. However, joint considerations of reliability, temperature, and stochastic characteristics of precedence-constrained tasks for performance optimization make task scheduling particularly challenging. In this paper, we tackle this challenge by using an affinity (i.e., probability)-driven task allocation and scheduling approach that decouples schedule lengths and thermal profiles of processors. Specifically, we separately model the affinity of a task for processors with respect to schedule lengths and the affinity of a task for processors with regard to chip thermal profiles considering task reliability and stochastic characteristics of task execution time and intertask communication time. Subsequently, we combine the two types of affinities, and design a scheduling heuristic that assigns a task to the processor with the highest joint affinity. Extensive simulations based on randomly generated stochastic and real-world applications are performed to validate the effectiveness of the proposed approach. Experiment results show that the proposed scheme can reduce the system makespan by up to 30.1% without violating the temperature and reliability constraints compared to benchmarking methods.

Affinity-Driven Modeling and Scheduling for Makespan Optimization in Heterogeneous Multiprocessor Systems

Fog computing is an emerging architectural paradigm for the implementation of the Internet of Things, where computation moves from cloud servers to network edges. Fog computing systems are with three characteristics: 1) low latency; 2) strong presence of real-time applications; and 3) reusability of end devices. Most existing designs of fog computing systems concentrate on reducing application processing latency, but neglect real-time requirements of applications and reusability of end devices, which may drastically degrade both functionality and quality-of-service (QoS) of applications. In this article, we investigate QoS optimization of real-time applications in fog computing systems equipped with reusable end devices and powered by hybrid energy of renewable generations and grid electricity. We propose a renewable-adaptive computation offloading approach. At the end device layer, local energy allocation schemes are designed at the application-level and component-level, where techniques of the cooperative game and mixed-integer linear programming (MILP) are leveraged, respectively. At the fog layer, the local energy allocation method is augmented to a local-remote scheduling solution by judiciously judging whether or not the computation offloading of an application needs to be triggered. The experimental results demonstrate that compared to benchmarking algorithms, our approach improves the overall and individual application QoS by up to 101.93% and 59.30%, respectively.

Exploring Renewable-Adaptive Computation Offloading for Hierarchical QoS Optimization in Fog Computing

Energy-efficient task scheduling for multi-core platforms with per-core DVFS

Cross-ISA system-mode emulation has many important applications. For example, Cross-ISA system-mode emulation helps computer architects and OS developers trace and debug kernel execution-flow efficiently by emulating a slower platform (such as ARM) on a more powerful plat-form (such as an x86 machine). Cross-ISA system-mode emulation also enables workload consolidation in data centers with platforms of different instruction-set architectures (ISAs). However, system-mode emulation is much slower. One major overhead in system-mode emulation is the multi-level memory address translation that maps guest virtual address to host physical address. Shadow page tables (SPT) have been used to reduce such overheads, but primarily for same-ISA virtualization. In this paper we propose a novel approach called embedded shadow page tables (ESPT). EPST embeds a shadow page table into the address space of a cross-ISA dynamic binary translation (DBT) and uses hardware memory management unit in the CPU to translate memory addresses, instead of software translation in a current DBT emulator like QEMU. We also use the larger address space on modern 64-bit CPUs to accommodate our DBT emulator so that it will not interfere with the guest operating system. We incorporate our new scheme into QEMU, a popular, retargetable cross-ISA system emulator. SPEC CINT2006 benchmark results indicate that our technique achieves an average speedup of 1.51 times in system mode when emulating ARM on x86, and a 1.59 times speedup for emulating IA32 on x86_64.

Efficient memory virtualization for Cross-ISA system mode emulation

Energy-efficient task scheduling is a fundamental issue in many application domains, such as energy conservation for mobile devices and the operation of green computing data centers. Modern processors support dynamic voltage and frequency scaling (DVFS) on a per-core basis, i.e., the CPU can adjust the voltage or frequency of each core. As a result, the core in a processor may have different computing power and energy consumption. To conserve energy in multi-core platforms, we propose task scheduling algorithms that leverage per-core DVFS and achieve a balance between performance and energy consumption. We consider two task execution modes: the batch mode, which runs jobs in batches, and the online mode in which jobs with different time constraints, arrival times, and computation workloads co-exist in the system. For tasks executed in the batch mode, we propose an algorithm that finds the optimal scheduling policy, and for the online mode, we present a heuristic algorithm that determines the execution order and processing speed of tasks in an online fashion. The heuristic ensures that the total cost is minimal for every time interval during a task's execution.

An Energy-Efficient Task Scheduler for Multi-core Platforms with Per-core DVFS Based on Task Characteristics

Virtualization has recently become popular in the embedded-systems area. In order to apply virtualization techniques on embedded-systems, we need a hypervisor that provides a software virtualization environment. A hypervisor scheduler assigns virtual cores in a virtual machine to physical cores. Since traditional load-balanced scheduling strategy is not aware of the asymmetry of cores, its deployment to an asymmetric platform may cause performance degradation or energy waste. In this paper, we propose an energy-efficient, asymmetry-aware scheduler for hypervisor on asymmetric multi-core platform. The goal is to generate an energy-efficient scheduling plan with performance guarantee.

Chao-Jui Chang

Papers

Energy-efficient task scheduling for multi-core platforms with per-core DVFS

Efficient memory virtualization for Cross-ISA system mode emulation

An Energy-Efficient Task Scheduler for Multi-core Platforms with Per-core DVFS Based on Task Characteristics

An energy-efficient hypervisor scheduler for asymmetric multi-core