With the advent of Internet services, big data and cloud computing, high-throughput computing has generated much research interest, especially on high-throughput cloud servers. However, three basic questions are still not satisfactorily answered: (1) What are the basic metrics (what throughput and high-throughput of what)? (2) What are the main factors most beneficial to increasing throughput? (3) Are there any fundamental constraints and how high can the throughput go? This article addresses these issues by utilizing the fifty-year progress in Little’s law, to reveal three fundamental relations among the seven basic quantities of throughput (λ), number of active threads (L), waiting time (W), system power (P), thread energy (E), Watts per thread ω, threads per Joule θ. In addition to Little’s law L = λW, we obtain P = λE and λ = Lωθ, under reasonable assumptions. These equations help give a first order estimation of performance and power consumption targets for billion-thread cloud servers.

With the explosive growth of information, more and more organizations are deploying private cloud systems or renting public cloud systems to process big data. However, there is no existing benchmark suite for evaluating cloud performance on the whole system level. To the best of our knowledge, this paper proposes the first benchmark suite CloudRank-D to benchmark and rank cloud computing systems that are shared for running big data applications.We analyze the limitations of previous metrics, e.g., floating point operations, for evaluating a cloud computing system, and propose two simple metrics: data processed per second and data processed per Joule as two complementary metrics for evaluating cloud computing systems. We detail the design of CloudRank-D that considers representative applications, diversity of data characteristics, and dynamic behaviors of both applications and system software platforms. Through experiments, we demonstrate the advantages of our proposed metrics. In several case studies, we evaluate two small-scale deployments of cloud computing systems using CloudRank-D.

Static cache partitioning can reduce interapplication cache interference and improve the composite performance of a cache-polluted application and a cachesensitive application when they run on cores that share the last level cache in the same multi-core processor. In a virtualized system, since different applications might run on different virtual machines (VMs) in different time, it is inapplicable to partition the cache statically in advance. This paper proposes a dynamic cache partitioning scheme that makes use of hot page detection and page migration to improve the composite performance of co-hosted virtual machines dynamically according to prior knowledge of cache-sensitive applications. Experimental results show that the overhead of our page migration scheme is low, while in most cases, the composite performance is an improvement over free composition.

Both performance and energy cost are important concerns for current data center operators. Traditionally, however, IT and mechanical engineers have separately optimized the cyber and physical aspects of data center operations. This paper considers both of these aspects with the eventual goal of developing performance and power management techniques that operate holistically to control the entire cyber-physical complex of data center installations. Toward this end, we propose a balance of payments model for holistic power and performance management. As an example of coordinated cyber-physical system management, the energyaware cyber-physical system (EaCPS) uses an application controller on the cyber side to guarantee application performance, and on the physical side, it utilizes electric currentaware capacity management (CACM) to smartly place executables to reduce the energy consumption of each chassis present in a data center rack. A web application, representative of a multi-tier web site, is used to evaluate the performance of the controller on the cyber side, the CACM control on the physical side, and the holistic EaCPS methods in a mid-size instrumented data center. Results indicate that coordinated EaCPS outperforms separate cyber and physical control modules.

System management is becoming increasingly complex and brings high costs, especially with the advent of cloud computing. Cloud computing involves numerous platforms often of virtual machines (VMs) and middleware has to be managed to make the whole system work costeffectively after an application is deployed. In order to reduce management costs, in particular for the manual activities, many computer programs have been developed remove or reduce the complexity and difficulty of system mamnagement. These programs are usually hard-coded in languages like Java and C++, which bring enough capability and flexibility but also cause high programming effort and cost. This paper proposes an architecture for developing management programs in a simple but powerful way. First of all, the manageability of a given platform (via APIs, configuration files, and scripts) is abstracted as a runtime model of the platform’s software architecture, which can automatically and immediately propagate any observable runtime changes of the target platforms to the corresponding architecture models, and vice versa. The management programs are developed using modeling languages, instead of those relatively low-level programming languages. Architecture-level management programs bring many advantages related to performance, interoperability, reusability, and simplicity. An experiment on a real-world cloud deployment and comparisonwith traditional programming language approaches demonstrate the feasibility, effectiveness, and benefits of the new model based approach for management program development.

Recently, the issue of privacy preserving location queries has attracted much research. However, there are few works focusing on the tradeoff between location privacy preservation and location query information collection. To tackle this kind of tradeoff, we propose the privacy persevering location query (PLQ), an efficient privacy preserving location query processing framework. This framework can enable the location-based query without revealing user location information. The framework can also facilitate location-based service providers to collect some information about the location based query, which is useful in practice. PLQ consists of three key components, namely, the location anonymizer at the client side, the privacy query processor at the server side, and an additional trusted third party connecting the client and server. The location anonymizer blurs the user location into a cloaked area based on a map-hierarchy. The map-hierarchy contains accurate regions that are partitioned according to real landforms. The privacy query processor deals with the requested nearest-neighbor (NN) location based query. A new convex hull of polygon (CHP) algorithm is proposed for nearest-neighbor queries using a polygon cloaked area. The experimental results show that our algorithms can efficiently process location based queries.

Proxy signature schemes enable an entity to delegate its signing rights to any other party, called proxy signer. As a variant of proxy signature primitive, proxymultisignature allows a group of original signers to delegate their signing capabilities to a single proxy signer in such a way that the proxy signer can sign a message on behalf of the group of original signers. We propose a concrete ID-based proxy multi-signature scheme from bilinear pairings. The proposed scheme is existential unforgeable against adaptively chosen message and given ID-attack in random oracle model under the computational Diffie-Hellman (CDH) assumption. The fascinating property of new scheme is that the size of a proxy multi-signature is independent of the number of original signers. Furthermore the proposed scheme is simple and computationally more efficient than other ID-based proxy multisignature schemes.

Network coding is vulnerable to pollution attacks, which prevent receivers from recovering the source message correctly. Most existing schemes against pollution attacks either bring significant redundancy to the original message or require a high computational complexity to verify received blocks. In this paper, we propose an efficient scheme against pollution attacks based on probabilistic key pre-distribution and homomorphic message authentication codes (MACs). In our scheme, each block is attached with a small number of MACs and each node can use these MACs to verify the integrity of the corresponding block with a high probability. Compared to previous schemes, our scheme still leverages a small number of keys to generate MACs for each block, but more than doubles the detection probability.Meanwhile, our scheme is able to efficiently restrict pollution propagation within a small number of hops. Experimental results show that our scheme is more efficient in verification than existing ones based on public-key cryptography.

Multi-lateral multi-issue negotiations are the most complex realistic negotiation problems. Automated approaches have proven particularly promising for complex negotiations and previous research indicates evolutionary computation could be useful for such complex systems. To improve the efficiency of realistic multi-lateral multi-issue negotiations and avoid the requirement of complete information about negotiators, a novel negotiation model based on an improved evolutionary algorithm p-ADE is proposed. The new model includes a new multi-agent negotiation protocol and strategy which utilize p-ADE to improve the negotiation efficiency by generating more acceptable solutions with stronger suitability for all the participants. Where p-ADE is improved based on the well-known differential evolution (DE), in which a new classification-based mutation strategy DE/rand-to-best/pbest as well as a dynamic self-adaptive parameter setting strategy are proposed. Experimental results confirm the superiority of p-ADE over several state-of-the-art evolutionary optimizers. In addition, the p-ADE based multiagent negotiation model shows good performance in solving realistic multi-lateral multi-issue negotiations.

Holding strategies are among the most commonly used operation control strategies. This paper presents an improved holding strategy. In the strategy, a mathematical model aiming to minimize the total waiting times of passengers at the current stop and at the following stops is constructed and a new heuristic algorithm, shuffled complex evolution method developed at the University of Arizona (SCEUA), is adopted to optimize the holding times of early buses. Results show that the improved holding strategy can provide better performance compared with a traditional schedulebased holding strategy and no-control strategy. The computational results are also evidence of the feasibility of using SCE-UA in optimizing the holding times of early buses at a stop.

This paper describes qualitative and quantitative analysis of color emotion dimension expression using a standard device-independent colorimetric system. To collect color emotion data, 20 subjects are required to report their emotion responses, using a valence-arousal emotion model, to 52 color samples that are chosen from CIELAB Lch color spaces. Qualitative analysis, including analysis of variance (ANOVA), Pearson’s correlation and Spearman’s rank correlation, shows that significant differences exist between responses to achromatic and chromatic stimuli, but there are no significant differences between chromatic samples. There is a positive linear relationship between lightness/chroma and valence-arousal dimensions. Subsequently, several classic predictors are used to quantitatively predict emotion induced by color attributes. Furthermore, several explicit color emotion models are developed by using multiple linear regression with stepwise and pace regression. Experimental results show that chroma and lightness have stronger effects on emotions than hue, which is consistent with our qualitative results and other psychological studies. Arousal has greater predictive value than valence.

Membrane computing is an emergent branch of natural computing, which is inspired by the structure and the functioning of living cells, as well as the organization of cells in tissues, organs, and other higher order structures. Tissue P systems are a class of the most investigated computing models in the framework of membrane computing, especially in the aspect of efficiency. To generate an exponential resource in a polynomial time, cell separation is incorporated into such systems, thus obtaining so called tissue P systems with cell separation. In this work, we exploit the computational efficiency of this model and construct a uniform family of such tissue P systems for solving the independent set problem, a well-known NP-complete problem, by which an efficient solution can be obtained in polynomial time.