Traditional datacenters employ costly diesel generators (DG) and uninterrupted power supplies (UPS) to back up power. However, some or even all racks of a green datacenter can still be powered by renewable energy during grid power outages. This makes the utilization of the DGs and UPSs in green datacenters significantly lower than in traditional datacenters. In this paper, we propose a highly cost-effective power back-up (COPA) approach for green datacenters by leveraging the availability characteristics of renewable energy as well as grid power outages. COPA contributes three new techniques. The first technique, called least UPS capacity planning, determines the least rated power capability and runtime of the UPSs to guarantee the normal operations of a green datacenter during grid power outages. The second technique, named cooperative UPS/renewable power supply, employs UPS and renewable energy at the same time to supply power to each rack when grid power fails. The last one, dubbed renewable-energy-aware dynamic power management, controls the power consumption dynamically based on the available capacity of renewable energy and UPS. We build an experimental cluster consisting of 10 servers, and use four representative benchmarks as well as verified data about the availability characteristics of solar and wind energy to evaluate COPA. The results show that COPA reduces 47 percent and 70 percent of the power back-up cost for a solar energy powered datacenter and a wind energy powered datacenter, respectively. Moreover, COPA guarantees the application's Service Level Agreement (SLA) for at least 20 minutes (over 79 percent outages) and 56 minutes on average while enabling the back-up power to last for at least 2 hours and for 3 hours on average, which cannot be achieved by other under-provisioning power back-up approaches.

Performance and energy efficiency are major concerns in cloud computing data centers. More often, they carry conflicting requirements making optimization a challenge. Further complications arise when heterogeneous hardware and data center management technologies are combined. For example, heterogeneous hardware such as General Purpose Graphics Processing Units (GPGPUs) improve performance at the cost of greater power consumption while virtualization technologies improve resource management and utilization at the cost of degraded performance. In this paper, we focus on exploiting heterogeneity introduced by GPUs to reduce power budget requirements for servers while maintaining performance. To maintain or improve overall server performance at reduced power budget, we propose two enhancements: (a) We borrow power from co-located multithreaded virtual machines (VMs) and reallocate it to GPU VMs. (b) To compensate multi-threaded VMs and re-boost their performance, we propose to borrow virtual computing resources from GPU VMs and reallocate them to CPU VMs. Combining the two techniques minimizes server power budget while maintaining overall server performance. Our results show that server power budget can be reduced by almost 18% at the average cost of 13% performance degradation per virtual machine. In addition, reallocating virtual resources improves the performance of multi-threaded applications by 30% without affecting GPU applications. Combining both techniques reduces server energy consumption by 47 % with minimum performance degradation.

With silicon optical technology moving toward maturity, the use of photonic networks-on-chip (NoCs) for global chip communication is emerging as a promising solution to the communication requirements of future many core processors. It is expected that photonic NoCs will play an important role in alleviating current power, latency, and bandwidth constraints. However, photonic NoCs are sensitive to ambient temperature variations because their basic constituents, ring resonators, are themselves sensitive to those variations. Since ring resonators are basic building blocks for photonic modulators, switches, multiplexers, and demultiplexers, variations of on-chip temperature pose serious challenges to the proper operation of photonic NoCs. Proposed methods that mitigate the effects of temperature at the device level are either difficult to use in CMOS processes or not suitable for large scale implementation. In this paper, we propose Aurora, a thermally resilient photonic NoC architecture design that supports reliable and low bit error rate (BER) on-chip communications in the presence of large temperature variations. Our proposed architecture leverages cross-layer solutions at the device, architecture, and operating system (OS) layers that individually provide considerable improvements and synergistically provide even more significant improvements. To compensate for small temperature variations, our design varies the bias current through ring resonators. For larger temperature variations, we propose architecture-level techniques to reroute messages away from hot regions, and through cooler regions, to their destinations. We also propose a thermal/congestion-aware coscheduling algorithm at the OS level to further lower BER by reorganizing the thermal profile of the chip. Our simulation results show that Aurora provides a robust architectural solution to handle temperature variation effects on future photonic NoCs. For instance, average BER and message error rate are reduced by 96% and 85%, respectively, when the combined thermal optimization scheme [shortest path first+ OS] is applied. From the perspective of power efficiency, Aurora is also superior to conventional photonic NoC architectures by as much as 37%.

The relocation of high performance computing systems (HPC) to the cloud poses new challenges for data center architects and IT managers. These challenges are due to heterogeneity injected into data centers by cutting-edge virtualization technologies and hardware accelerators used to support emerging cloud applications and services. Although hardware accelerators like General Purpose Graphics Processing Units (GPGPUs) and virtualization technologies have been well studied and evaluated individually, a detailed analysis of their combined architectures and collective behavior from the data center point of view is lacking. Using real platforms and high performance computing workloads, we study the power performance tradeoffs due to various granularities of heterogeneity across hardware and software layers and expose hidden opportunities for optimizing overall data center efficiency. Our approach is to evaluate server power and performance from a data center point of view as opposed to evaluating hardware accelerators and virtualization technologies themselves. Our results show that performance on cloud is affected by virtualization overhead and fraction of serial code. Moreover, GPU workloads achieve 25% and 30% savings in power and energy consumption when executed on low power platforms, and only 50% of our GPU workloads are more energy efficient than their corresponding CPU implementations. The results also show that it is much more power efficient to collocate GPU virtual machines with non-GPU virtual machines.

With silicon optical technology moving towards maturity, the use of photonic network-on-chip (NoCs) for global chip communication is emerging as a promising solution to communication requirements of future many core processors. It is expected that photonic NoCs will play an important role in alleviating current power, latency, and bandwidth constraints. However, photonic NoCs are sensitive to ambient temperature variations because their basic constituents, ring resonators, are themselves sensitive to those variations. Since ring resonators are basic building blocks for photonic modulators, switches, multiplexers, and demultiplexers, variations of on-chip temperature pose serious challenges to the proper operation of photonic NoCs. Proposed methods that mitigate the effects of temperature at device level are either difficult to use in CMOS processes or not suitable for large scale implementation. In this paper, we propose Aurora, a thermally resilient photonic NoC architecture design that supports reliable and low bit error rate (BER) on-chip communications in the presence of large temperature variations. Our proposed architecture leverages solutions at both device and architecture layers that synergistically provide significant improvements. To compensate for small temperature variations, our design varies the bias current through ring resonators. For larger temperature variations, we propose architecture-level techniques to re-route messages away from hot regions, and through cooler regions, to their destinations, thereby lowering BER. Our simulation results show that Aurora provides a robust architectural solution to handle temperature variation effects on future photonic NoCs. For instance, average BER and message error rate (MER) are reduced by 78% and 30% respectively when the combined device and architectural technique (SPF) is applied. From the perspective of power efficiency, Aurora is also superior to conventional photonic NoC architectures by as much as 33%.

Large-scale computing systems such as data centers are facing increasing pressure to cap their carbon footprint. Integrating emerging clean energy solutions into computer system design therefore gains great significance in the green computing era. While some pioneering work on tracking variable power budget show promising energy efficiency, they are not suitable for data centers due to lack of performance guarantee when renewable generation is low and fluctuant. In addition, our characterization of wind power behavior reveals that data centers designed to track the intermittent renewable power incur up to 4X performance loss due to inefficient and redundant load matching activities. As a result, mitigating operational overhead while still maintaining desired energy utilization becomes the most significant challenge in managing server clusters on intermittent renewable energy generation. In this paper we take a first step in digging into the operational overhead of renewable energy powered data center. We propose iSwitch, a lightweight server power management that follows renewable power variation characteristics, leverages existing system infrastructures, and applies supply/load cooperative scheme to mitigate the performance overhead. Comparing with state-of-the-art renewable energy driven system design, iSwitch could mitigate average network traffic by 75%, peak network traffic by 95%, and reduce 80% job waiting time while still maintaining 96% renewable energy utilization. We expect that our work can help computer architects make informed decisions on sustainable and high-performance system design.

An increasing number of data centers today start to incorporate renewable energy solutions to cap their carbon footprint. However, the impact of renewable energy on large-scale data center design is still not well understood. In this paper, we model and evaluate data centers driven by intermittent renewable energy. Using real-world data center and renewable energy source traces, we show that renewable power utilization and load tuning frequency are two critical metrics for designing sustainable high-performance data centers. Our characterization reveals that load power fluctuation together with the intermittent renewable power supply introduce unnecessary tuning activities, which can increase the management overhead and degrade the performance of renewable energy driven data centers.