Online Web Cluster Capacity Estimation and Its Application to Energy Conservation

Designers of data centers and Web servers aim to make on-demand allocation of resources to clients in order to lower the deployment cost of hosted services. Moreover, they must also minimize operating costs, such as energy consumption, by matching service-capacity demand with resource supply. However, since the term "capacity" is typically defined vaguely or inadequately, it is difficult to assess resource needs and, hence, servers, which are several times larger than needed at runtime, are usually deployed. The time-varying nature of the workload model further complicates the problem and necessitates an online capacity-estimation solution. To address this overprovisioning problem, we first define the capacity of a server cluster as the sustainable throughput subject to a request retransmission ratio constraint and then analyze different approaches to capacity estimation in a running system. Various capacity-estimation mechanisms, such as offline benchmarking and CPU-utilization evaluation, are discussed and compared with our queue-monitoring method. We employ several different data-collection methods (application instrumentation, user-space tools, simple network management protocol (SNMP), and kernel modules) to compare their effects on estimation accuracy. Of these, queue monitoring is found to provide a good and stable estimate of server capacity. To validate this finding, we propose a simple cluster- resizing mechanism and evaluate the energy-conservation performance. A good combination of data collection and online capacity estimation is found to make significantly more energy savings than traditional approaches (that is, static estimation and scheduled capacity). Our experimental results show that more than 40 percent of energy can be saved for regular daily usage patterns without any prior knowledge of the workload and that long start-up and shutdown delays affect energy savings considerably.     

On supporting high-throughput routing metrics in on-demand routing protocols for multi-hop wireless networks

Researchers have proposed routing metrics other than hop count, such as ETX (expected transmission count) and ETT (expected transmission time), to find routes with high throughput. These metrics are inherently suitable to be used in source routing protocols such as DSR, because link state information needs to be collected for the calculation of the shortest path. In this paper, we propose an efficient and generalized approach called accumulated path metric (APM) for supporting high-throughput metrics (HTMs) in on-demand routing protocols. One advantage of APM is that it is able to find the shortest path, in terms of a particular metric, without collecting topology information and without running a shortest-path algorithm. This will significantly simplify the existing design of supporting HTMs in DSR. We present a proof of the correctness of APM. Moreover, we address the problem of duplicate RREQ (route request) transmissions with existing HTM schemes and present a broadcast ordering (BO) technique to suppress unnecessary RREQ transmissions. We study the performance of APM and BO in both AODV and DSR, and the results verify the effectiveness of the proposed approaches.     

Rotary Ultrasonic Machining: A Review

Rotary ultrasonic machining (RUM) is a mechanical type of nontraditional hybrid machining process that has been utilized potentially to machine a wide range of latest and difficult-to-machine materials, including ductile, hard and brittle, ceramics, composites, etc. In RUM, the basic material removal phenomenon of ultrasonic machining (USM) and conventional diamond grinding amalgamates together and results in higher material removal rate (MRR), improved hole accuracy with superior surface finish. In the current article, several investigations carried out in the domain of RUM for enormous materials have been critically reviewed and reported. It also highlights several experimental and theoretical ensues of RUM to improve the process outcomes and it is reported that process performance can be substantially improved by making the right selection of machine, diamond tooling, material and operating parameters. In recent years, various investigators have explored umpteen ways to enhance the RUM process performance by probing the different factors that influence the quality attributes. Among the various accessible modifications in RUM as employed in industries, rotary ultrasonic drilling is more strongly established compared to other versions such as rotary ultrasonic side milling, face milling, grinding, surface texturing, etc. The micro machining applications of RUM have also been discussed briefly. The final section of this paper discusses RUM developments and outlines the aspects for future research.     

Increasing coverage to improve detection of network and host anomalies

For intrusion detection, the LERAD algorithm learns a succinct set of comprehensible rules for detecting anomalies, which could be novel attacks. LERAD validates the learned rules on a separate held-out validation set and removes rules that cause false alarms. However, removing rules with possible high coverage can lead to missed detections. We propose three techniques for increasing coverage—Weighting, Replacement and Hybrid. Weighting retains previously pruned rules and associate weights to them. Replacement, on the other hand, substitutes pruned rules with other candidate rules to ensure high coverage. We also present a Hybrid approach that selects between the two techniques based on training data coverage. Empirical results from seven data sets indicate that, for LERAD, increasing coverage by Weighting, Replacement and Hybrid detects more attacks than Pruning with minimal computational overhead.     

Scheduling UET-UCT tasks: branch-and-bound search in the priority space

The paper is concerned with the problem of scheduling partially ordered unit execution time tasks on parallel processors with unit communication delays and release times. Two criteria are considered, the maximum lateness and its particular case, the makespan. This problem plays an important role in scheduling theory and was originally inspired by the applications to multi-processor computer systems. It is well known that for both criteria the problem is NP-hard in the strong sense. The paper presents an implementation of the branch-and-bound method which does not partition the feasible region explicitly. The theoretical results are complemented by computational experiments.     

Biomedical applications of thermally activated shape memory polymers

Shape memory polymers (SMPs) are smart materials that can remember a primary shape and can return to this primary shape from a deformed secondary shape when given an appropriate stimulus. This property allows them to be delivered in a compact form via minimally invasive surgeries in humans, and deployed to achieve complex final shapes. Here we review the various biomedical applications of SMPs and the challenges they face with respect to actuation and biocompatibility. While shape memory behavior has been demonstrated with heat, light and chemical environment, here we focus our discussion on thermally stimulated SMPs.     

Effect of long-term stress and temperature exposure on the fracture toughness of Ti-62222 alloy

Fracture toughness tests were conducted on a Ti-62222 (titanium alloy) sheet being considered for use in high temperature aircraft applications in the as received condition and after exposing the pre-cracked specimens to a sustained stress intensity, K, level between 55 and 60.5 MPa for 200 h at 350°C. It was concluded that the fracture toughness does not degrade as a result of exposure to high temperature and the K levels in this material. The tensile strength in the exposed condition also remained the same as in the as received condition.     

An Efficient and Scalable Quasi-Aggregate Signature Scheme Based on LFSR Sequences

Aggregate signatures can be a crucial building block for providing scalable authentication of a large number of users in several applications like building efficient certificate chains, authenticating distributed content management systems, and securing path vector routing protocols. Aggregate signatures aim to prevent resources (signature and storage elements, and computation) from growing linearly in the number of signers participating in a network protocol. In this paper, we present an efficient and scalable quasi-aggregate signature scheme, {rm CLFSR}- {rm QA}, based on third-order linear feedback shift register (cubic LFSR) sequences that can be instantiated using both XTR and GH public key cryptosystems. In the proposed {rm CLFSR}-{rm QA} construction, signers sign messages sequentially; however, the verfier need not know the order in which messages were signed. The proposed scheme offers constant length signatures, fast signing, aggregation, and verification operations at each node, and requires the least storage elements (public keys needed to verify the signature), compared to any other aggregate signature scheme. To the best of our knowledge, {rm CLFSR}- {rm QA} is the first aggregate signature scheme to be constructed using LFSR sequences. We believe that the {rm CLFSR}- {rm QA} signature scheme can be catalytic in improving the processing latency as well as reducing space requirements in building secure, large-scale distributed network protocols. We perform extensive theoretical analysis including correctness and security of {rm CLFSR}- {rm QA} and also present a performance (computation and communication costs, storage overhead) comparison of the proposed scheme with well-known traditional constructions.     

Exploring Variability and Performance in a Sub-200-mV Processor

In this study, we explore the design of a subthreshold processor for use in ultra-low-energy sensor systems. We describe an 8-bit subthreshold processor that has been designed with energy efficiency as the primary constraint. The processor, which is functional below V_dd=200 mV, consumes only 3.5 pJ/inst at V_dd=350 mV and, under a reverse body bias, draws only 11 nW at V_dd=160 mV. Process and temperature variations in subthreshold circuits can cause dramatic fluctuations in performance and energy consumption and can lead to robustness problems. We investigate the use of body biasing to adapt to process and temperature variations. Test-chip measurements show that body biasing is particularly effective in subthreshold circuits and can eliminate performance variations with minimal energy penalties. Reduced performance is also problematic at low voltages, so we investigate global and local techniques for improving performance while maintaining energy efficiency.