https://beloglazov.info/papers/2011-energy-aware-fgcs.pdf

Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing

Large-scale Internet services require a computing infrastructure that can beappropriately described as a warehouse-sized computing system. The cost ofbuilding datacenter facilities capable of delivering a given power capacity tosuch a computer can rival the recurring energy consumption costs themselves.Therefore, there are strong economic incentives to operate facilities as closeas possible to maximum capacity, so that the non-recurring facility costs canbe best amortized. That is difficult to achieve in practice because ofuncertainties in equipment power ratings and because power consumption tends tovary significantly with the actual computing activity. Effective powerprovisioning strategies are needed to determine how much computing equipmentcan be safely and efficiently hosted within a given power budget.In this paper we present the aggregate power usage characteristics of largecollections of servers (up to 15 thousand) for different classes ofapplications over a period of approximately six months. Those observationsallow us to evaluate opportunities for maximizing the use of the deployed powercapacity of datacenters, and assess the risks of over-subscribing it. We findthat even in well-tuned applications there is a noticeable gap (7 - 16%)between achieved and theoretical aggregate peak power usage at the clusterlevel (thousands of servers). The gap grows to almost 40% in wholedatacenters. This headroom can be used to deploy additional compute equipmentwithin the same power budget with minimal risk of exceeding it. We use ourmodeling framework to estimate the potential of power management schemes toreduce peak power and energy usage. We find that the opportunities for powerand energy savings are significant, but greater at the cluster-level (thousandsof servers) than at the rack-level (tens). Finally we argue that systems needto be power efficient across the activity range, and not only at peakperformance levels.

/pdf/power-provisioning-for-a-warehouse-sized-computer-3iyo1da2iy.pdf

Power provisioning for a warehouse-sized computer

Most statistical and machine-learning algorithms assume that the data is a random sample drawn from a stationary distribution. Unfortunately, most of the large databases available for mining today violate this assumption. They were gathered over months or years, and the underlying processes generating them changed during this time, sometimes radically. Although a number of algorithms have been proposed for learning time-changing concepts, they generally do not scale well to very large databases. In this paper we propose an efficient algorithm for mining decision trees from continuously-changing data streams, based on the ultra-fast VFDT decision tree learner. This algorithm, called CVFDT, stays current while making the most of old data by growing an alternative subtree whenever an old one becomes questionable, and replacing the old with the new when the new becomes more accurate. CVFDT learns a model which is similar in accuracy to the one that would be learned by reapplying VFDT to a moving window of examples every time a new example arrives, but with O(1) complexity per example, as opposed to O(w), where w is the size of the window. Experiments on a set of large time-changing data streams demonstrate the utility of this approach.

/pdf/mining-time-changing-data-streams-a9kghvc13p.pdf

Mining time-changing data streams

This survey covers rollback-recovery techniques that do not require special language constructs. In the first part of the survey we classify rollback-recovery protocols into checkpoint-based and log-based.Checkpoint-based protocols rely solely on checkpointing for system state restoration. Checkpointing can be coordinated, uncoordinated, or communication-induced. Log-based protocols combine checkpointing with logging of nondeterministic events, encoded in tuples called determinants. Depending on how determinants are logged, log-based protocols can be pessimistic, optimistic, or causal. Throughout the survey, we highlight the research issues that are at the core of rollback-recovery and present the solutions that currently address them. We also compare the performance of different rollback-recovery protocols with respect to a series of desirable properties and discuss the issues that arise in the practical implementations of these protocols.

/pdf/a-survey-of-rollback-recovery-protocols-in-message-passing-bnrpate93a.pdf

A survey of rollback-recovery protocols in message-passing systems

We introduce TinySec, the first fully-implemented link layer security architecture for wireless sensor networks. In our design, we leverage recent lessons learned from design vulnerabilities in security protocols for other wireless networks such as 802.11b and GSM. Conventional security protocols tend to be conservative in their security guarantees, typically adding 16--32 bytes of overhead. With small memories, weak processors, limited energy, and 30 byte packets, sensor networks cannot afford this luxury. TinySec addresses these extreme resource constraints with careful design; we explore the tradeoffs among different cryptographic primitives and use the inherent sensor network limitations to our advantage when choosing parameters to find a sweet spot for security, packet overhead, and resource requirements. TinySec is portable to a variety of hardware and radio platforms. Our experimental results on a 36 node distributed sensor network application clearly demonstrate that software based link layer protocols are feasible and efficient, adding less than 10% energy, latency, and bandwidth overhead.

/pdf/tinysec-a-link-layer-security-architecture-for-wireless-2w93vaox2f.pdf

TinySec: a link layer security architecture for wireless sensor networks

Energy conservation without performance degradation is an important goal for battery-operated computers, such as laptops and hand-held assistants. In this paper we determine the potential benefits of application-supported device management for optimizing energy and performance. In particular, we consider application transformations that increase device idle times and inform the operating system about the length of each upcoming period of idleness. We use modeling and experimentation to assess the potential energy and performance benefits of this type of application support for a laptop disk. Our main modeling results show that these benefits are significant. Our experimental results demonstrate that unless applications are transformed, they cannot accrue any of the predicted benefits. Overall, we find that the transformations can reduce disk energy consumption by as much as 89% with only a small degradation in performance.

Energy-efficient server clusters

Consistent checkpointing provides transparent fault tolerance for long-running distributed applications. Performance measurements of an implementation of consistent checkpointing are described. The measurements show that consistent checkpointing performs remarkably well. Eight computation-intensive distributed applications were executed on a network of 16 diskless Sun-3/60 workstations, and the performance without checkpointing was compared to the performance with consistent checkpoints taken at two-minute intervals. For six of the eight applications, the running time increased by less than 1% as a result of the checkpointing. The highest overhead measured was 5.8%. Incremental checkpointing and copy-on write checkpointing were the most effective techniques in lowering the running time overhead. It is argued that these measurements show that consistent checkpointing is an efficient way to provide fault tolerance for long-running distributed applications. >

The performance of consistent checkpointing

Power management has traditionally focused on portable and handheld devices. This paper breaks with tradition and presents a case for managing power consumption in web servers. Web servers experience large periods of low utilization, presenting an opportunity for using power management to reduce energy consumption with minimal performance impact. We measured the energy consumption of a "typical" web server under a variety of workloads derived from access logs of real websites, including the 1998 Winter Olympics web site. Our measurements show that the CPU is the largest consumer of power for typical web servers today.We have also created a power simulator for web serving workloads that estimates CPU energy consumption with less than 5.7% error for our workloads. The simulator is fast, processing over 75,000 requests/second on a 866MHz uniprocessor machine. Using the simulator, we quantify the potential benefits of dynamically scaling the processor voltage and frequency, a power management technique that is traditionally found only in handheld devices. We find that dynamic voltage and frequency scaling is highly effective for saving energy with moderately intense web workloads, saving from 23% to 36% of the CPU energy while keeping server responsiveness within reasonable limits.

The case for power management in web servers

Manetho is a new transparent rollback-recovery protocol for long-running distributed computations. It uses a novel combination of antecedence graph maintenance, uncoordinated checkpointing, and sender-based message logging. Manetho simultaneously achieves the advantages of pessimistic message logging, namely limited rollback and, fast output commit, and the advantage of optimistic message logging, namely low failure-free overhead. These advantages come at the expense of a complex recovery scheme. >

/pdf/manetho-transparent-roll-back-recovery-with-low-overhead-2pwy4loi6v.pdf

Elmootazbellah Nabil Elnozahy

Papers

A survey of rollback-recovery protocols in message-passing systems

Energy-efficient server clusters

The performance of consistent checkpointing

The case for power management in web servers

Manetho: transparent roll back-recovery with low overhead, limited rollback, and fast output commit