The computational and cooling power demands of enterprise servers are increasing at an unsustainable rate. Understanding the relationship between computational power, temperature, leakage, and cooling power is crucial to enable energy-efficient operation at the server and data center levels. This paper develops empirical models to estimate the contributions of static and dynamic power consumption in enterprise servers for a wide range of workloads, and analyzes the interactions between temperature, leakage, and cooling power for various workload allocation policies. We propose a cooling management policy that minimizes the server energy consumption by setting the optimum fan speed during runtime. Our experimental results on a presently shipping enterprise server demonstrate that including leakage awareness in workload and cooling management provides additional energy savings without any impact on performance.

Time lag and quantization in temperature sensors in enterprise servers lead to stability concerns on existing variable fan speed control schemes. Stability challenges become further aggravated when multiple local controllers are running together with the fan control scheme. In this paper, we present a global control scheme which tackles the concerns on the stability of enterprise servers while reducing the performance degradation caused by the variable fan speed control scheme. We first present a stable fan speed control scheme based on the ProportionalIntegral-Derivative (PID) controller by adaptively adjusting the PID parameters according to the operating fan speed and eliminating the fan speed oscillation caused by temperature quantization. Then, we present a global control scheme which coordinates control actions among multiple local controllers. In addition, it guarantees the server stability while minimizing the overall performance degradation. We validated the proposed control scheme using a presently shipping commercial enterprise server. Our experimental results show that the proposed fan control scheme is stable under the non-ideal temperature measurement system (10 sec in time lag and 1°C in quantization figures). Furthermore, the global control scheme enables to run multiple local controllers in a stable manner while reducing the performance degradation up to 19.2% compared to conventional coordination schemes with 19.1% savings in power consumption.

Power over-subscription challenges and emerging cost management strategies motivate designing efficient data center power capping techniques. During capping, provisioned power must be budgeted among the computational and cooling units. This work presents a data center power budgeting policy that simultaneously improves the quality-of-service (QoS) and power efficiency by considering the workload- and cooling-induced asymmetries among the servers. Proposed policy finds the most efficient data center temperature and the power distribution among servers while guaranteeing reliable temperature levels for the server internal components. Experiments based on real servers demonstrate 21% increase in throughput compared to existing techniques.

Reducing the energy consumption for computation and cooling in servers is a major challenge considering the data center energy costs today. To ensure energy-efficient operation of servers in data centers, the relationship among computational power, temperature, leakage, and cooling power needs to be analyzed. By means of an innovative setup that enables monitoring and controlling the computing and cooling power consumption separately on a commercial enterprise server, this paper studies temperature-leakage-energy tradeoffs, obtaining an empirical model for the leakage component. Using this model, we design a controller that continuously seeks and settles at the optimal fan speed to minimize the energy consumption for a given workload. We run a customized dynamic load-synthesis tool to stress the system. Our proposed cooling controller achieves up to 9% energy savings and 30W reduction in peak power in comparison to the default cooling control scheme.

As server processor power densities increase, the cost of air cooling also grows resulting from higher fan speeds. Our measurements show that vibrations induced by fans in high-end servers and its rack neighbors cause a dramatic drop in hard disk bandwidth, resulting in a corresponding decrease in application performance. In this paper we quantify the performance and energy cost effects of the fan vibrations and propose a disk performance aware thermal, energy and cooling technique. Results show that we can not only meet thermal constraints, but also improve performance by 1.35x as compared to the conventional methods.

Conventional thermal management techniques are reactive, as they take action after temperature reaches a threshold. Such approaches do not always minimize and balance the temperature, and they control temperature at a noticeable performance cost. This paper investigates how to use predictors for forecasting temperature and workload dynamics, and proposes proactive thermal management techniques for multiprocessor system-on-chips. The predictors we study include autoregressive moving average modeling and lookup tables. We evaluate several reactive and predictive techniques on an UltraSPARC T1 processor and an architecture-level simulator. Proactive methods achieve significantly better thermal profiles and performance in comparison to reactive policies.

Thermal hot spots and temperature gradients on the die need to be minimized to manufacture reliable systems while meeting energy and performance constraints. In this work, we solve the task scheduling problem for multiprocessor system-on-chips (MPSoCs) using Integer Linear Programming (ILP). The goal of our optimization is minimizing the hot spots and balancing the temperature distribution on the die for a known set of tasks. Under the given assumptions about task characteristics, the solution is optimal. We compare our technique against optimal scheduling methods for energy minimization, energy balancing, and hot spot minimization, and show that our technique achieves significantly better thermal profiles. We also extend our technique to handle workload variations at runtime.

In deep submicron circuits, thermal hot spots and high temperature gradients increase the cooling costs, and degrade reliability and performance. In this paper, we propose a low-cost temperature management strategy for multicore systems to reduce the adverse effects of hot spots and temperature variations. Our technique utilizes online learning to select the best policy for the current workload characteristics among a given set of expert policies. We achieve 20% and 60% average decrease in the frequency of hot spots and thermal cycles respectively in comparison to the best performing expert, and reduce the spatial gradients to below 5%.

Designing thermal management strategies that reduce the impact of hot spots and on-die temperature variations at low performance cost is a very significant challenge for multiprocessor system-on-chips (MPSoCs). In this work, we present a proactive MPSoC thermal management approach, which predicts the future temperature and adjusts the job allocation on the MPSoC to minimize the impact of thermal hot spots and temperature variations without degrading performance. In addition, we implement and compare several reactive and proactive management strategies, and demonstrate that our proactive temperature-aware MPSoC job allocation technique is able to dramatically reduce the adverse effects of temperature at very low performance cost. We show experimental results using a simulator as well as an implementation on an UltraSPARC T1 system.

Preventing thermal hot spots and large temperature variations on the die is critical for addressing the challenges in system reliability, performance, cooling cost and leakage power. Reactive thermal management methods, which take action after temperature reaches a given threshold, maintain the temperature below a critical level at the cost of performance, and do not address the temperature variations. In this work, we propose a proactive thermal management approach, which estimates the future temperature using regression, and allocates workload on a multicore system to reduce and balance the temperature to avoid temperature induced problems. Our technique reduces the hot spots and temperature variations significantly in comparison to reactive strategies.

Thermal hot spots and high temperature gradients degrade reliability and performance, and increase cooling costs and leakage power. In this paper, we explore the benefits of temperature-aware task scheduling for multiprocessor system-on-a-chip (MPSoC). We evaluate our techniques using workload characteristics collected from a real system by Sun's Continuous System Telemetry. We first solve the task scheduling problem statically using integer linear programming (ILP). The ILP solution is guaranteed to be optimal for the given assumptions for tasks. We formulate ILPs for minimizing energy, balancing energy, and reducing hot spots, and provide an extensive comparison of their thermal behavior against our technique. Our static solution can reduce the frequency of hot spots by 35%, spatial gradients by 85%, and thermal cycles by 61% in comparison to the ILP for minimizing energy. We then design dynamic scheduling policies at the OS-level with negligible performance overhead. Our adaptive dynamic policy reduces the frequency of high-magnitude thermal cycles and spatial gradients by around 50% and 90%, respectively, in comparison to state-of-the-art schedulers. Reactive thermal management strategies, such as thread migration, can be combined with our scheduling policy to further reduce hot spots, temperature variations, and the associated performance cost.

Memory leaks are known to be a major cause of reliability and performance issues in software. This paper describes a run-time scheme that detects and removes memory leaks with minimal performance overhead and with no modifications to application source code. The scheme consists of a first stage where a pattern recognition technique proactively detects subtle memory leaks, followed by a more resource-intensive second stage that scans the memory space of an application and removes detected memory leaks. The pattern recognition technique in the first stage is based on the multivariate state estimation technique (MSET) which provides accurate detection of subtle memory leaks with very little overhead. The second stage is only activated when problems are identified by the first stage. For our prototype, this second stage is based on debugging and analysis tools provided by Solaris 10. Due to the low-overhead impact of the first stage, the system can be monitored for memory leaks without incurring noticeable performance degradation. We present and discuss some results from our unique proactive detection and debugging methodology

Software aging phenomena have been recently studied; one particularly complex type is shared memory pool latch contention in large OLTP servers. Latch contention onset leads to severe performance degradation until a manual rejuvenation of the DBMS shared memory pool is triggered. Conventional approaches to automated rejuvenation have failed for latch contention because no single resource metric has been identified that can be monitored to alert the onset of this complex mechanism. The current investigation explores the feasibility of applying an advanced pattern recognition method that is embodied in a commercially available equipment condition monitoring system (SmartSignal eCM/spl trade/) for proactive annunciation of software-aging faults. One hundred data signals are monitored from a large OLTP server, collected at 20-60 sec. intervals over a 5-month period. Results show 13 variables consistently deviate from normal operation prior to a latch event, providing up to 2 hours early warning.