We study the problem of optimizing data center electric utility bill under uncertainty in workloads and real-world pricing schemes. Our focus is on using control knobs that modulate the power consumption of IT equipment. To overcome the difficulty of casting/updating such control problems and the computational intractability they suffer from in general, we propose and evaluate a hierarchical optimization framework wherein an upper layer uses (i) temporal aggregation to restrict the number of decision instants during a billing cycle to computationally feasible values, and (ii) spatial (i.e., control knob) aggregation whereby it models the large and diverse set of power control knobs with two abstract knobs labeled demand dropping and demand delaying. These abstract knobs operate upon a fluid power demand. The key insight underlying our modeling is that the power modulation effects of most IT control knobs can be succinctly captured as dropping and/or delaying a portion of the power demand. These decisions are passed onto a lower layer that leverages existing research to translate them into decisions for real IT knobs. We develop a suite of algorithms for our upper layer that deal with different forms of input uncertainty. An experimental evaluation of the proposed approach offers promising results: e.g., it offers net cost savings of about 25% and 18% to a streaming media server and a MapReduce-based batch workload, respectively.

Shared resources in a chip multiprocessors (CMPs) pose unique challenges to the seamless adoption of CMPs in virtualization environments and high performance computing systems. While sharing resources like on-chip last level cache is generally beneficial due to increased resource utilization, lack of control over management of these resources can lead to loss of determinism, faded performance isolation, and an overall lack of the notion of Quality of Service (QoS) provided to individual applications. This has direct ramifications on adhering to service level agreements in environments involving consolidation of multiple heterogeneous workloads. Although providing QoS in presence of shared resources has been addressed in the literature, it has been commonly observed that reservation of resources for QoS leads to under-utilization of resources.

A wide variety of software systems employ closed loops (feedback) to achieve service level objectives and to optimize resource usage. Control theory provides a systematic approach to constructing closed loop systems, and is widely used in disciplines such as mechanical and electrical engineering. This paper describes recent advances in applying control theory to computing systems, and identifies research challenges to address so that control engineering can be widely used by software practitioners.

Temperature-aware computing is becoming more important in design of computer systems as power densities are increasing and the implications of high operating temperatures result in higher failure rates of components and increased demand for cooling capability. Computer architects and system software designers need to understand the thermal consequences of their proposals, and develop techniques to lower operating temperatures to reduce both transient and permanent component failures. Recognizing the need for thermal modeling tools to support those researches, there has been work on modeling temperatures of processors at the micro-architectural level which can be easily understood and employed by computer architects for processor designs. However, there is a dearth of such tools in the academic/research community for undertaking architectural/systems studies beyond a processor - a server box, rack or even a machine room. In this paper we presents a detailed 3-dimensional computational fluid dynamics based thermal modeling tool, called ThermoStat, for rack-mounted server systems. We conduct several experiments with this tool to show how different load conditions affect the thermal profile, and also illustrate how this tool can help design dynamic thermal management techniques. We propose reactive and proactive thermal management for rack mounted server and isothermal workload distribution for rack.

High power densities and the implications of high operating temperatures on the failure rates of components are key driving factors of temperature-aware computing. Computer architects and system software designers need to understand the thermal consequences of their proposals, and develop techniques to lower operating temperatures to reduce both transient and permanent component failures. Tools for understanding temperature ramifications of designs have been mainly restricted to industry for studying packaging and cooling mechanisms, with little access to such toolsets for academic researchers. Developing such tools is an arduous task since it usually requires cross-cutting areas of expertise spanning architecture, systems software, thermodynamics, and cooling systems. Recognizing the need for such tools, there has been work on modeling temperatures of processors at the micro-architectural level which can be easily understood and employed by computer architects for processor designs. However, there is a dearth of such tools in the academic/research community for undertaking architectural/systems studies beyond a processor - a server box, rack or even a machine room. This paper presents a detailed 3-dimensional computational fluid dynamics based thermal modeling tool, called ThermoStat, for rack-mounted server systems. Using this tool, we model a 20 (each with dual Xeon processors) node rack-mounted server system, and validate it with over 30 temperature sensor measurements at different points in the servers/rack. We conduct several experiments with this tool to show how different load conditions affect the thermal profile, and also illustrate how this tool can help design dynamic thermal management techniques

I/O consolidation is a growing trend in production environments due to increasing complexity in tuning and managing storage systems. A consequence of this trend is the need to serve multiple users and/or workloads simultaneously. It is imperative to ensure that these users are insulated from each other by virtualization in order to meet any service-level objective (SLO). Previous proposals for performance virtualization suffer from one or more of the following drawbacks: (1) They rely on a fairly detailed performance model of the underlying storage system; (2) couple rate and latency allocation in a single scheduler, making them less flexible; or (3) may not always exploit the full bandwidth offered by the storage system.This article presents a two-level scheduling framework that can be built on top of an existing storage utility. This framework uses a low-level feedback-driven request scheduler, called AVATAR, that is intended to meet the latency bounds determined by the SLO. The load imposed on AVATAR is regulated by a high-level rate controller, called SARC, to insulate the users from each other. In addition, SARC is work-conserving and tries to fairly distribute any spare bandwidth in the storage system to the different users. This framework naturally decouples rate and latency allocation. Using extensive I/O traces and a detailed storage simulator, we demonstrate that this two-level framework can simultaneously meet the latency and throughput requirements imposed by an SLO, without requiring extensive knowledge of the underlying storage system.

I/O consolidation is a growing trend in production environments due to the increasing complexity in tuning and managing storage systems. A consequence of this trend is the need to serve multiple users/workloads simultaneously. It is imperative to make sure that these users are insulated from each other by visualization in order to meet any service level objective (SLO). This paper presents a 2-level scheduling framework that can be built on top of an existing storage utility. This framework uses a low-level feedback-driven request scheduler, called AVATAR, that is intended to meet the latency bounds determined by the SLO. The load imposed on AVATAR is regulated by a high-level rate controller, called SARC, to insulate the users from each other. In addition, SARC is work-conserving and tries to fairly distribute any spare bandwidth in the storage system to the different users. This framework naturally decouples rate and latency allocation. Using extensive I/O traces and a detailed storage simulator, we demonstrate that this 2-level framework can simultaneously meet the latency and throughput requirements imposed by an SLO, without requiring extensive knowledge of the underlying storage system.

The growing cost of tuning and managing computer systems is leading to out-sourcing of commercial services to hosting centers. These centers provision thousands of dense servers within a relatively small real-estate in order to host the applications/services of different customers who may have been assured by a service-level agreement (SLA). Power consumption of these servers is becoming a serious concern in the design and operation of the hosting centers. The effects of high power consumption manifest not only in the costs spent in designing effective cooling systems to ward off the generated heat, but in the cost of electricity consumption itself. It is crucial to deploy power management strategies in these hosting centers to lower these costs towards enhancing profitability. At the same time, techniques for power management that include shutting down these servers and/or modulating their operational speed, can impact the ability of the hosting center to meet SLAs. In addition, repeated on-off cycles can increase the wear-and-tear of server components, incurring costs for their procurement and replacement. This paper presents a formalism to this problem, and proposes three new online solution strategies based on steady state queuing analysis, feedback control theory, and a hybrid mechanism borrowing ideas from these two. Using real web server traces, we show that these solutions are more adaptive to workload behavior when performing server provisioning and speed control than earlier heuristics towards minimizing operational costs while meeting the SLAs.

No abstract available.

Despite the importance of storage in enterprise computer systems, there are few adequate tools to design and configure a storage system to meet application data requirements efficiently. Storage system design involves choosing the disk arrays to use, setting the configuration options on those arrays, and determining an efficient mapping of application data onto the configured system. This is a complex process because of the multitude of disk array configuration options, and the need to take into account both capacity and potentially contending I/O performance demands when placing the data. Thus, both existing tools and administrators using rules of thumb often generate designs that are of poor quality.This article presents the Disk Array Designer (DAD), which is a tool that can be used both to guide administrators in their design decisions and to automate the design process. DAD uses a generalized best-fit bin packing heuristic with randomization and backtracking to search efficiently through the huge number of possible design choices. It makes decisions using device models that estimate storage system performance. We evaluate DAD's designs based on traces from a variety of database, filesystem, and e-mail workloads. We show that DAD can handle the difficult task of configuring midrange and high-end disk arrays, even with complex real-world workloads. We also show that DAD quickly generates near-optimal storage system designs, improving in both speed and quality over previous tools.