Meikang Qiu

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.

Phase-Change Memory Optimization for Green Cloud with Genetic Algorithm

Green cloud is an emerging new technology in the computing world in which memory is a critical component. Phase-change memory (PCM) is one of the most promising alternative techniques to the dynamic random access memory (DRAM) that faces the scalability wall. Recent research has been focusing on the multi-level cell (MLC) of PCM. By precisely arranging multiple levels of resistance inside a PCM cell, more than one bit of data can be stored in one single PCM cell. However, the MLC PCM suffers from the degradation of performance compared to the single-level cell(SLC) PCM, due to the longer memory access time. In this paper, we present a genetic-based optimization algorithm for chip multiprocessor (CMP) equipped with PCM memory in green clouds. The proposed genetic-based algorithm not only schedules and assigns tasks to cores in the CMP system, but also provides a PCM MLC configuration that balances the PCM memory performance as well as the efficiency. The experimental results show that our genetic-based algorithm can significantly reduce the maximum memory usage by 76.8 percent comparing with the uniform SLC configuration, and improve the efficiency of memory usage by 127 percent comparing with the uniform 4 bits/cell MLC configuration. Moreover, the performance of the system is also improved by 24.5 percent comparing with the uniform 4 bits/cell MLC configuration in terms of total execution time.

Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing

Employing mobile cloud computing (MCC) to enable mobile users to acquire benefits of cloud computing by an environmental friendly method is an efficient strategy for meeting current industrial demands. However, the restrictions of wireless bandwidth and device capacity have brought various obstacles, such as extra energy waste and latency delay, when deploying MCC. Addressing this issue, we propose a dynamic energy-aware cloudlet-based mobile cloud computing model (DECM) focusing on solving the additional energy consumptions during the wireless communications by leveraging dynamic cloudlets (DCL)-based model. In this paper, we examine our model by a simulation of practical scenario and provide solid results for the evaluations. The main contributions of this paper are twofold. First, this paper is the first exploration in solving energy waste problems within the dynamic networking environment. Second, the proposed model provides future research with a guideline and theoretical supports. HighlightsThe first attempt for extending the functionality of cloudlets.Achieve energy-aware performances in the dynamic networking environment.Provide future research in the field with the theoretical support and exploring directions.The model may be migrated and applied in multiple industries.

Cost minimization while satisfying hard/soft timing constraints for heterogeneous embedded systems

In high-level synthesis for real-time embedded systems using heterogeneous functional units (FUs), it is critical to select the best FU type for each task. However, some tasks may not have fixed execution times. This article models each varied execution time as a probabilistic random variable and solves heterogeneous assignment with probability (HAP) problem. The solution of the HAP problem assigns a proper FU type to each task such that the total cost is minimized while the timing constraint is satisfied with a guaranteed confidence probability. The solutions to the HAP problem are useful for both hard real-time and soft real-time systems. Optimal algorithms are proposed to find the optimal solutions for the HAP problem when the input is a tree or a simple path. Two other algorithms, one is optimal and the other is near-optimal heuristic, are proposed to solve the general problem. The experiments show that our algorithms can effectively reduce the total cost while satisfying timing constraints with guaranteed confidence probabilities. For example, our algorithms achieve an average reduction of 33.0% on total cost with 0.90 confidence probability satisfying timing constraints compared with the previous work using worst-case scenario.

Health-CPS: Healthcare Cyber-Physical System Assisted by Cloud and Big Data

The advances in information technology have witnessed great progress on healthcare technologies in various domains nowadays. However, these new technologies have also made healthcare data not only much bigger but also much more difficult to handle and process. Moreover, because the data are created from a variety of devices within a short time span, the characteristics of these data are that they are stored in different formats and created quickly, which can, to a large extent, be regarded as a big data problem. To provide a more convenient service and environment of healthcare, this paper proposes a cyber-physical system for patient-centric healthcare applications and services, called Health-CPS, built on cloud and big data analytics technologies. This system consists of a data collection layer with a unified standard, a data management layer for distributed storage and parallel computing, and a data-oriented service layer. The results of this study show that the technologies of cloud and big data can be used to enhance the performance of the healthcare system so that humans can then enjoy various smart healthcare applications and services.

Reducing write activities on non-volatile memories in embedded CMPs via data migration and recomputation

Recent advances in circuit and process technologies have pushed non-volatile memory technologies into a new era. These technologies exhibit appealing properties such as low power consumption, non-volatility, shock-resistivity, and high density. However, there are challenges to which we need answers in the road of applying non-volatile memories as main memory in computer systems. First, non-volatile memories have limited number of write/erase cycles compared with DRAM memory. Second, write activities on non-volatile memory are more expensive than DRAM memory in terms of energy consumption and access latency. Both challenges will benefit from reduction of the write activities on the nonvolatile memory. In this paper, we target embedded Chip Multiprocessors (CMPs) with Scratch Pad Memory (SPM) and non-volatile main memory. We introduce data migration and recompu-tation techniques to reduce the number of write activities on non-volatile memories. Experimental results show that the proposed methods can reduce the number of writes by 59.41% on average, which means that the non-volatile memory can last 2.8 times as long as before. Meanwhile, the finish time of programs is reduced by 31.81% on average.

Energy Efficient Security Algorithm for Power Grid Wide Area Monitoring System

Modern power grid is the most complex human-made system, which is monitored by wide-area monitoring system (WAMS). Providing time-synchronized data of power system operating states, WAMS will play a crucial role in next generation smart grid protection and control. WAMS helps secure efficient energy transmission as well as reliable and optimal grid management. As the key enabler of a smart grid, numerous sensors such as PMU and current sensors transmit real-time dynamic data, which is usually protected by encryption algorithm from malicious attacks, over wide-area-network (WAN) to power system control centers so that monitoring and control of the whole system is possible. Security algorithms for power grid need to consider both performance and energy efficiency through code optimization techniques on encryption and decryption. In this paper, we take power nodes (sites) as platforms to experimentally study ways of energy consumptions in different security algorithms. First, we measure energy consumptions of various security algorithms on CrossBow and Ember sensor nodes. Second, we propose an array of novel code optimization methods to increase energy consumption efficiency of different security algorithms. Finally, based on careful analysis of measurement results, we propose a set of principles on using security algorithms in WAMS nodes, such as cryptography selections, parameter configuration, and the like. Such principles can be used widely in other computing systems with energy constraints.

QoS-Aware Fault-Tolerant Scheduling for Real-Time Tasks on Heterogeneous Clusters

Fault-tolerant scheduling plays a significant role in improving system reliability of clusters. Although extensive fault-tolerant scheduling algorithms have been proposed for real-time tasks in parallel and distributed systems, quality of service (QoS) requirements of tasks have not been taken into account. This paper presents a fault-tolerant scheduling algorithm called QAFT that can tolerate one nodes permanent failures at one time instant for real-time tasks with QoS needs on heterogeneous clusters. In order to improve system flexibility, reliability, schedulability, and resource utilization, QAFT strives to either advance the start time of primary copies and delay the start time of backup copies in order to help backup copies adopt the passive execution scheme, or to decrease the simultaneous execution time of the primary and backup copies of a task as much as possible to improve resource utilization. QAFT is capable of adaptively adjusting the QoS levels of tasks and the execution schemes of backup copies to attain high system flexibility. Furthermore, we employ the overlapping technology of backup copies. The latest start time of backup copies and their constraints are analyzed and discussed. We conduct extensive experiments to compare our QAFT with two existing schemes-NOQAFT and DYFARS. Experimental results show that QAFT significantly improves the scheduling quality of NOQAFT and DYFARS.

Adaptive resource allocation for preemptable jobs in cloud systems

In 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 tasks execution order. Furthermore, a resource allocation mechanism with preemptable task execution can increase the utilization of clouds. In this paper, we propose an adaptive resource allocation algorithm for the cloud system with preemptable tasks. Our algorithms adjust the resource allocation adaptively based on the updated of the actual task executions. And the experimental results show that our algorithms works significantly in the situation where resource contention is fierce.

Intrusion detection techniques for mobile cloud computing in heterogeneous 5G

Mobile cloud computing is applied in multiple industries to obtain cloud-based services by leveraging mobile technologies. With the development of the wireless networks, defending threats from wireless communications have been playing a remarkable role in the Web security domain. Intrusion detection system IDS is an efficient approach for protecting wireless communications in the Fifth Generation 5G context. In this paper, we identify and summarize the main techniques being implemented in IDSs and mobile cloud computing with an analysis of the challenges for each technique. Addressing the security issue, we propose a higher level framework of implementing secure mobile cloud computing by adopting IDS techniques for applying mobile cloud-based solutions in 5G networks. On the basis of the reviews and synthesis, we conclude that the implementation of mobile cloud computing can be secured by the proposed framework because it will provide well-protected Web services and adaptable IDSs in the complicated heterogeneous 5G environment. Copyright