Novel online data allocation for hybrid memories on tele-health systems

[EN] The developments of wearable devices such as Body Sensor Networks (BSNs) have greatly improved the capability of tele-health industry. Large amount of data will be collected from every local BSN in real-time. These data is processed by embedded systems including smart phones and tablets. After that, the data will be transferred to distributed storage systems for further processing. Traditional on-chip SRAMs cause critical power leakage issues and occupy relatively large chip areas. Therefore, hybrid memories, which combine volatile memories with non-volatile memories, are widely adopted in reducing the latency and energy cost on multi-core systems. However, most of the current works are about static data allocation for hybrid memories. Those mechanisms cannot achieve better data placement in real-time. Hence, we propose online data allocation for hybrid memories on embedded tele-health systems. In this paper, we present dynamic programming and heuristic approaches. Considering the difference between profiled data access and actual data access, the proposed algorithms use a feedback mechanism to improve the accuracy of data allocation during runtime. Experimental results demonstrate that, compared to greedy approaches, the proposed algorithms achieve 20%-40% performance improvement based on different benchmarks. (C) 2016 Elsevier B.V. All rights reserved.This work is supported by NSF CNS-1457506 and NSF CNS-1359557.Chen, L.; Qiu, M.; Dai, W.; Hassan Mohamed, H. (2017). Novel online data allocation for hybrid memories on tele-health systems. Microprocessors and Microsystems. 52:391-400. https://doi.org/10.1016/j.micpro.2016.08.003S3914005

ENERGY-AWARE OPTIMIZATION FOR EMBEDDED SYSTEMS WITH CHIP MULTIPROCESSOR AND PHASE-CHANGE MEMORY

Over the last two decades, functions of the embedded systems have evolved from simple real-time control and monitoring to more complicated services. Embedded systems equipped with powerful chips can provide the performance that computationally demanding information processing applications need. However, due to the power issue, the easy way to gain increasing performance by scaling up chip frequencies is no longer feasible. Recently, low-power architecture designs have been the main trend in embedded system designs. In this dissertation, we present our approaches to attack the energy-related issues in embedded system designs, such as thermal issues in the 3D chip multiprocessor (CMP), the endurance issue in the phase-change memory(PCM), the battery issue in the embedded system designs, the impact of inaccurate information in embedded system, and the cloud computing to move the workload to remote cloud computing facilities. We propose a real-time constrained task scheduling method to reduce peak temperature on a 3D CMP, including an online 3D CMP temperature prediction model and a set of algorithm for scheduling tasks to different cores in order to minimize the peak temperature on chip. To address the challenging issues in applying PCM in embedded systems, we propose a PCM main memory optimization mechanism through the utilization of the scratch pad memory (SPM). Furthermore, we propose an MLC/SLC configuration optimization algorithm to enhance the efficiency of the hybrid DRAM + PCM memory. We also propose an energy-aware task scheduling algorithm for parallel computing in mobile systems powered by batteries. When scheduling tasks in embedded systems, we make the scheduling decisions based on information, such as estimated execution time of tasks. Therefore, we design an evaluation method for impacts of inaccurate information on the resource allocation in embedded systems. Finally, in order to move workload from embedded systems to remote cloud computing facility, we present a resource optimization mechanism in heterogeneous federated multi-cloud systems. And we also propose two online dynamic algorithms for resource allocation and task scheduling. We consider the resource contention in the task scheduling

An efficient cloud storage system for tele-health services

[EN] Healthcare service is a critical aspect of our daily lives. Enabled by technologies such as wearable devices and wireless sensor networks, tele-health has becoming a promising new field in IT industry. Wearable devices, which detect real-time human body conditions, form body sensor networks (BSNs) for patients. In a cloud-enabled tele-health ecosystem, health data are collected by the BSN and sent to mobile devices such as smart phones and tablets. These embedded devices process the data and forward them to remote data centers. Due to the energy and time constraints of embedded systems, the effectiveness of storage systems become a critical issue. For years, memory technologies such as SRAMs and DRAMs have been widely used in computer systems. SRAMs are fast while DRAMs have high density. However, SRAMs have the disadvantage of power leakage and low density. DRAMs are slower in read and write operations. New memory technology for embedded tele-health is needed. In the paper, we propose a hybrid memory system for embedded tele-health. We combine phase-change memory PCM with flash memory to meet energy and latency requirement while reducing capital expenditure. Moreover, the data allocation and storage on server side is also a challenging problem in tele-health. Effective storage system designs are desired to efficiently store and manage health care data from users. Therefore, in the paper, we design a ecosystem for tele-health including the memory storage for embedded devices and data storage for tele-health data centers. To fully utilize the proposed ecosystem, we design several resource allocation algorithms with dynamic programming and heuristics. The experiments show that our approaches can achieve up to 30% performance enhancement compared to greedy approaches.This work has been partially supported by the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China ICT1600236 (Prof. Meikang Qiu)Chen, L.; Qiu, M.; Dai, W.; Hassan Mohamed, H. (2017). An efficient cloud storage system for tele-health services. The Journal of Supercomputing. 73(7):2949-2965. https://doi.org/10.1007/s11227-017-1977-yS29492965737Guthaus MR (2001) MiBench: a free, commercially representative embedded benchmark suite. In: IEEE WWC, pp 3–14Hu J (2012) Optimizing data allocation and memory configuration for non-volatile memory based hybrid SPM on embedded CMPs. In: IPDPSW. Shanghai, China, pp 982–989IHS (2012) Medical Devices & Healthcare IT. https://technology.ihs.com/researchareas/450450Lai S (2003) Current status of the phase change memory and its future. In: IEEE International on Electron Devices Meeting, 2003. IEDM’03 Technical DigestLi J, Qiu M (2011) Resource allocation robustness in multi-core embedded systems with inaccurate information. J Syst Archit 57(9):840–849Meza J (2012) Enabling efficient and scalable hybrid memories using fine-granularity DRAM cache management. IEEE Comput Archit Lett 11(2):61–64Okhonin S (2008) Ultra-scaled Z-RAM cell. In: Proceedings of the IEEE International SOI Conference, pp 157–158Qiu M, Chen Z (2014) Energy-aware data allocation with hybrid memory for mobile cloud systems. Syst J IEEE PP(99):1–10Qiu M, Ming Z (2015) Phase-change memory optimization for green cloud with genetic algorithm. IEEE Trans Comput 64(12):3528–3540Ramos LE (2011) Page placement in hybrid memory systems. In: Proceedings of the International Conference on Supercomputing, pp 85–95Shanavas A (2012) Zero capacitor RAM. http://www.edutalks.org/downloads/zram.pdfTian W (2013) Task allocation on nonvolatile-memory-based hybrid main memory. IEEE Trans Very Large Scale Integr (VLSI) Syst 21(7):1271–1284Wilton SJE, Jouppi NP (1996) CACTI: an enhanced cache access and cycle time model. IEEE J Solid-State Circuits 31(5):677–688Wong H (2010) Phase change memory. Proc IEEE 98(12):2201–2227Zhang L, Qiu M (2010) Variable partitioning and scheduling for MPSoC with virtually shared scratch pad memory. J Signal Process Syst 58(2):247–26