Gang Quan

Online optimization for scheduling preemptable tasks on IaaS cloud systems

In Infrastructure-as-a-Service (IaaS) cloud computing, computational resources are provided to remote users in the form of leases. For a cloud user, he/she can request multiple cloud services simultaneously. In this case, parallel processing in the cloud system can improve the performance. When applying parallel processing in cloud computing, it is necessary to implement a mechanism to allocate resource and schedule the execution order of tasks. Furthermore, a resource optimization mechanism with preemptable task execution can increase the utilization of clouds. In this paper, we propose two online dynamic resource allocation algorithms for the IaaS cloud system with preemptable tasks. Our algorithms adjust the resource allocation dynamically based on the updated information of the actual task executions. And the experimental results show that our algorithms can significantly improve the performance in the situation where resource contention is fierce.

Feasibility Analysis for Temperature-Constraint Hard Real-Time Periodic Tasks

While the dynamic thermal management problem is closely related to the dynamic power management problem, it has its own distinct features. In this paper, we study the feasibility checking problem for real-time periodic task sets under the peak temperature constraint. We show that the traditional scheduling approach, i.e. to repeat the schedule that is feasible through the range of one hyperperiod, does not apply any more. We then present new necessary and sufficient conditions to check the feasibility of real-time schedules. We further incorporate the close relationship of leakage, temperature, and supply voltage into our feasibility analysis, and develop more elaborated feasibility conditions. Our experiments, based on technical parameters derived from a processor using the 65 nm IC technology, demonstrate the effectiveness of our feasibility conditions and, at the same time, highlight the fact that a power/thermal-aware computing technique becomes ineffective at the submicron scale if the inter dependency of leakage, temperature, and supply voltage is not properly addressed.

Leakage aware energy minimization for real-time systems under the maximum temperature constraint

In this paper, we study the problem on how to reduce the overall energy consumption while at the same time ensuring the timing and maximum temperature constraints for a real-time system. We incorporate the interdependence of leakage, temperature and supply voltage into analysis and develop a novel method to quickly estimate the overall energy consumption. Based on this method, we then propose a scheduling technique to minimize the overall energy consumption under the maximum temperature constraint. Our experimental results show that the proposed energy estimation method can achieve up to four-order-of-magnitude speedup compared with existing approaches while keeping the maximum estimation error within 4.8%. In addition, simulation results also demonstrate that our proposed energy minimization method consistently outperforms previous related approaches significantly.

Informer homed routing fault tolerance mechanism for wireless sensor networks

Abstract Sensors in a wireless sensor network (WSN) are prone to failure, due to the energy depletion, hardware failures, etc. Fault tolerance is one of the critical issues in WSNs. The existing fault tolerance mechanisms either consume significant extra energy to detect and recover from the failures or need to use additional hardware and software resource. In this paper, we propose a novel energy-aware fault tolerance mechanism for WSN, called Informer Homed Routing (IHR). In our IHR, non cluster head (NCH) nodes select a limited number of targets in the data transmission. Therefore it consumes less energy. Our experimental results show that our proposed protocol can significantly reduce energy consumption, compared to two existing protocols: Low-Energy Adaptive Clustering Hierarchy (LEACH) and Dual Homed Routing (DHR).

Data Allocation for Hybrid Memory With Genetic Algorithm

The gradually widening speed disparity between CPU and memory has become an overwhelming bottleneck for the development of chip multiprocessor systems. In addition, increasing penalties caused by frequent on-chip memory accesses have raised critical challenges in delivering high memory access performance with tight power and latency budgets. To overcome the daunting memory wall and energy wall issues, this paper focuses on proposing a new heterogeneous scratchpad memory architecture, which is configured from SRAM, MRAM, and Z-RAM. Based on this architecture, we propose a genetic algorithm to perform data allocation to different memory units, therefore, reducing memory access cost in terms of power consumption and latency. Extensive and experiments are performed to show the merits of the heterogeneous scratchpad architecture over the traditional pure memory system and the effectiveness of the proposed algorithms.

Throughput maximization for periodic real-time systems under the maximal temperature constraint

We study the problem on how to maximize the throughput for a periodic real-time system under the given peak temperature constraint. We assume that different tasks in our system may have different power and thermal characteristics. Two algorithms are presented in this paper. The first one is built upon processors that can be either in active or sleep mode. By judiciously selecting tasks with different thermal characteristics as well as alternating the processor active/sleep mode, our approach can improve the throughput upon the existing techniques by 21% in average. We further extend this approach for processors with dynamic voltage/ frequency scaling (DVFS) capability. Our experiments show that an improvement of 24% can be achieved when compared with the existing methods.

Leakage temperature dependency modeling in system level analysis

As the semiconductor technology continues its marching toward the deep sub-micron domain, the strong relation between leakage current and temperature becomes critical in power-aware and thermal-aware design for electronic systems. Previous circuit-level research results can capture the leakage/temperature dependency accurately, but can be too complex and thus ineffective in high level system design. In this paper, we study a large spectrum of leakage power models that are able to account for the leakage/temperature dependency, and in the meantime, are simple enough and suitable for system level design. We analyze and compare the tradeoff between the complexity and accuracy of these models empirically. Our experimental results strengthen the important role that the leakage power consumption plays in the electronic system design as the transistor size continues to shrink. More importantly, our results highlight the fact that it is vital to take the leakage/temperature and leakage/supply voltage dependency into considerations for high level power and thermal aware system level design.

Neighbor-aware dynamic thermal management for multi-core platform

With the high integration density and complexity of the modern multi-core platform, thermal problems become more and more significant for both the manufacture and system designer. Dynamic thermal management technique is one effective and efficient way to mitigate and avoid thermal emergences. In this paper, we propose a novel predictive dynamic thermal management algorithm to maximize the multi-core system throughput while satisfying the peak temperature constraints. Different from the conventional approaches, we found that it is not necessarily always a good choice to migrate a hot task to the core with the lowest temperature. Instead, in our algorithm, we develop a new temperature prediction technique and migration scheme that take the local temperature of a core as well as the impacts from neighboring cores into considerations. According to our experiment results on a practical Intel desktop platform, the proposed algorithm can significantly improve the throughput compared with the conventional approach.

Harmonic semi-partitioned scheduling for fixed-priority real-time tasks on multi-core platform

This paper presents a new semi-partitioned approach to schedule sporadic tasks on multi-core platform based on the Rate Monotonic Scheduling (RMS) policy. Our approach exploits the well known fact that harmonic tasks have better schedulablility than non-harmonic ones on a single processor. The challenge for our approach, however, is how to take advantage of this fact to assign and split appropriate tasks on different processors in the semi-partitioned approach. We formally prove that our scheduling approach can successfully schedule any task sets with system utilizations bounded by the Liu&Laylands bound. Our extensive experiment results demonstrate that the proposed algorithm can significantly improve the scheduling performance compared with the previous work.

Profit and Penalty Aware Scheduling for Real-Time Online Services

As computer and Internet technology continue to advance, real-time online services are emerging. Different from traditional real-time applications for which the scheduling objective is to meet task deadlines, the optimization goal for online service systems is to maximize profit obtained through providing timely services. For this class of applications, there are two distinctive characteristics. First, tasks are associated with a pair of time dependent functions representing accrued profit when completed before their deadlines and accrued penalty otherwise, respectively. Second, the service requests or tasks arrive aperiodically with execution time varying in a wide range. This paper presents a novel scheduling method and related analysis for such applications. Two scheduling algorithms, i.e., the nonpreemptive and preemptive Profit and Penalty aware (PP-aware) scheduling algorithms, are proposed with an objective to maximize systems total accrued profit. Our simulation results clearly demonstrate the advantages of the proposed algorithms, with respect to the system total accrued profit, over other commonly used scheduling algorithms, such as Earliest Deadline First (EDF) and Utility Accrual (UA) algorithms.