Energy-Harvesting Systems for Green Computing

Energy harvesting technologies provide a promising alternative to battery-powered systems and create an opportunity to achieve sustainable computing for the exploitation of ambient energy sources. However, energy harvesting devices and power generators encompass a number of non-classical system behaviors or characteristics, such as delivering nondeterministic power density, and these would create hindrance for effectively utilizing the harvested energy. Previously, we have investigated new design methods and tools that are used to enable power adaptive computing and, particularly, catering non-deterministic voltage, which can efficiently utilize ambient energy sources. Also, we developed a co-optimization approach to maximize the computational efficiency from the harvested ambient energy. This paper will provide a review of these methods. Emerging technologies, such as 3D-IC, which would also enable new paradigm of green and high-performance computing, will be also discussed.

Green5G: Enhancing Capacity and Coverage in Device-to-Device Communication

With the popularity of green computing and the huge usage of networks,there is an acute need for expansion of the 5G network.5G is used where energy efficiency is the highest priority,and it can play a pinnacle role in helping every industry to hit sustainability.While in the 5G network,conventional performance guides,such as network capacity and coverage are still major issues and need improvements.Device to Device communication(D2D)communication technology plays an important role to improve the capacity and coverage of 5G technology using different techniques.The issue of energy utilization in the IoT based system is a significant exploration center.Energy optimizationin D2D communication is an important point.We need to resolve this issue for increasing system performance.Green IoT speaks to the issue of lessening energy utilization of IoT gadgets which accomplishes a supportable climate for IoT systems.In this paper,we improve the capacity and coverage of 5G technology using Multiple Inputs Multiple Outputs(MU-MIMO).MUMIMO increases the capacity of 5G in D2D communication.We also present all the problems faced by 5G technology and proposed architecture to enhance system performance.

Resource pre-allocation algorithms for low-energy task scheduling of cloud computing

In order to lower the power consumption and improve the coefficient of resource utilization of current cloud computing systems, this paper proposes two resource pre-allocation algorithms based on the ＂shut down the redundant, turn on the demanded＂ strategy here. Firstly, a green cloud computing model is presented, abstracting the task scheduling problem to the virtual machine deployment issue with the virtualization technology. Secondly, the future workloads of system need to be predicted： a cubic exponential smoothing algorithm based on the conservative control（CESCC） strategy is proposed, combining with the current state and resource distribution of system, in order to calculate the demand of resources for the next period of task requests. Then, a multi-objective constrained optimization model of power consumption and a low-energy resource allocation algorithm based on probabilistic matching（RA-PM） are proposed. In order to reduce the power consumption further, the resource allocation algorithm based on the improved simulated annealing（RA-ISA） is designed with the improved simulated annealing algorithm. Experimental results show that the prediction and conservative control strategy make resource pre-allocation catch up with demands, and improve the efficiency of real-time response and the stability of the system. Both RA-PM and RA-ISA can activate fewer hosts, achieve better load balance among the set of high applicable hosts, maximize the utilization of resources, and greatly reduce the power consumption of cloud computing systems.

Triangular code:Near-optimal linear time fountain code

In this paper,we propose Triangular Code(TC),a new class of fountain code with near-zero redundancy and linear encoding and decoding computational complexities of OeLklog kT,where k is the packet batch size and L is the packet data length.Different from previous works where the optimal performance of codes has been shown under asymptotic assumption,TC enjoys near-zero redundancy even under non-asymptotic settings for smallmoderate number of packets.These features make TC suitable for practical implementation in batteryconstrained devices in IoT,D2D and M2M network paradigms to achieve scalable reliability,and minimize latency due to its low decoding delay.TC is a non-linear code,which is encoded using the simple shift and XOR addition operations,and decoded using the simple back-substitution algorithm.Although it is nonlinear code at the packet level,it remains linear code when atomized at the bit level.We use this property to show that the backsubstitution decoder of TC is equivalent to the Belief Propagation(BP)decoder of LT code.Therefore,TC can benefit from rich prolific literature published on LT code,to design efficient code for various applications.Despite the equivalency between the decoders of TC and LT code,we show that compared to state-of-the-art optimized LT code,TC reduces the redundancy of LT code by 68%-99% for k reaching 1024.

A Service Level Agreement Aware Online Algorithm for Virtual Machine Migration

The demand for cloud computing has increased manifold in the recent past.More specifically,on-demand computing has seen a rapid rise as organizations rely mostly on cloud service providers for their day-to-day computing needs.The cloud service provider fulfills different user requirements using virtualization-where a single physical machine can host multiple VirtualMachines.Each virtualmachine potentially represents a different user environment such as operating system,programming environment,and applications.However,these cloud services use a large amount of electrical energy and produce greenhouse gases.To reduce the electricity cost and greenhouse gases,energy efficient algorithms must be designed.One specific area where energy efficient algorithms are required is virtual machine consolidation.With virtualmachine consolidation,the objective is to utilize the minimumpossible number of hosts to accommodate the required virtual machines,keeping in mind the service level agreement requirements.This research work formulates the virtual machine migration as an online problem and develops optimal offline and online algorithms for the single host virtual machine migration problem under a service level agreement constraint for an over-utilized host.The online algorithm is analyzed using a competitive analysis approach.In addition,an experimental analysis of the proposed algorithm on real-world data is conducted to showcase the improved performance of the proposed algorithm against the benchmark algorithms.Our proposed online algorithm consumed 25%less energy and performed 43%fewer migrations than the benchmark algorithms.

Joint Communication and Processor Frequency Selection for Low-Energy Systems under Timing Constraints

This paper presents a method to reduce the energy consumption of multi-core systems characterized by processor cores and buses with discrete frequency levels under timing constraints.The proposed method takes the transformations of the original task graphs,which include dependent tasks located in different iterations,as inputs.The proposed method utilizes mapping selection as well as joint processor and communication frequency scaling to implement energy reduction.We conduct experiments on several random task graphs.Experimental results show that the proposed method can achieve substantial energy reduction compared with previous work under the same hard timing constraints.

Autonomous on-Chip Debugging for Sensors Based on AVR Microcontrollers

Sensors are often based on minimalistic microcontrollers for their reduced power consumption and size. Because of the specific hardware of sensors, their software development, including debugging, is also particular. Simulators and external computers are conventional approaches to sensor debugging, but they both face limitations such as the supported hardware and debugging conditions. In this paper, we propose a fully autonomous on-chip debugging solution for sensors (and other devices) based on AVR microcontrollers, with a particular focus on human-machine interaction. The proposal is then validated in practice through various experiments, notably involving real-world sensors. Formal measurement of the induced overhead is also conducted, which eventually demonstrates the applicability of the proposal.

Supply and Demand Oriented Energy Management in the Internet of Things

The Internet of Things (IoT) is emerging as an attractive paradigm involving physical perceptions, cyber interactions, social correlations and even cognitive thinking through a cyber-physical-social-thinking hyperspace. In this context, energy management with the purposes of energy saving and high efficiency is a challenging issue. In this work, a taxonomy model is established in reference to the IoT layers (i.e., sensor-actuator layer, network layer, and application layer), and IoT energy management is addressed from the perspectives of supply and demand to achieve green perception, communication, and computing. A smart home scenario is presented as a case study involving the main enabling technologies with supply-side, demand-side, and supply-demand balance considerations, and open issues in the field of IoT energy management are also discussed.

Dynamic Power Saving via Least-Square Self-Tuning Regulator in the Virtualized Computing Systems

In recent years,power saving problem has become more and more important in many fields and attracted a lot of research interests.In this paper,the authors consider the power saving problem in the virtualized computing system.Since there are multiple objectives in the system as well as many factors influencing the objectives,the problem is complex and hard.The authors will formulate the problem as an optimization problem of power consumption with a prior requirement on performance,which is taken as the response time in the paper.To solve the problem,the authors design the adaptive controller based on least-square self-tuning regulator to dynamically regulate the computing resource so as to track a given reasonable reference performance and then minimize the power consumption using the tracking result supplied by the controller at each time.Simulation is implemented based on the data collected from real machines and the time delay of turning on/off the machine is included in the process.The results show that this method based on adaptive control theory can save power consumption greatly with satisfying the performance requirement at the same time,thus it is suitable and effective to solve the problem.

Towards Automated Provisioning and Emergency Handling in Renewable Energy Powered Datacenters

Designing eco-friendly system has been at the forefront of computing research. Faced with a growing concern about the server energy expenditure and the climate change, both industry and academia start to show high interest in computing systems powered by renewable energy sources. Existing proposals on this issue mainly focus on optimizing resource utilization or workload performance. The key supporting hardware structures for cross-layer power management and emergency handling mechanisms are often left unexplored. This paper presents GreenPod, a research framework for exploring scalable and dependable renewable power management in datacenters. An important feature of GreenPod is that it enables joint management of server power supplies and virtualized server workloads. Its interactive communication portal between servers and power supplies allows dataeenter operators to perform real-time renewable energy driven load migration and power emergency handling. Based on our system prototype, we discuss an important topic： virtual machine （VM） workloads survival when facing extended utility outage and insufficient onsite renewable power budget. We show that whether a VM can survive depends on the operating frequencies and workload characteristics. The proposed framework can greatly encourage and facilitate innovative research in dependable green computing.

Coordinating workload balancing and power switching in renewable energy powered data center

There has been growing concern about energy consumption and environmental impact of datacenters. Some pioneers begin to power datacenters with renewable energy to offset carbon footprint. However, it is challenging to integrate intermittent renewable energy into datacenter power system. Grid-tied system is widely deployed in renewable energy powered datacenters. But the drawbacks （e.g. Harmonic dis- turbance and costliness） of grid tie inverter harass this design. Besides, the mixture of green load and brown load makes power management heavily depend on software measurement and monitoring, which often suffers inaccuracy. We propose DualPower, a novel power provisioning architecture that en- ables green datacenters to integrate renewable power supply without grid tie inverters. To optimize DualPower operation, we propose a specially designed power management frame- work to coordinate workload balancing with power supply switching. We evaluate three optimization schemes （LM, PS and JO） under different datacenter operation scenarios on our trace-driven simulation platform. The experimental results show that DualPower can be as efficient as grid-tied system and has good scalability. In contrast to previous works, Du- alPower integrates renewable power at lower cost and main- tains full availability of datacenter servers.