Using genetic algorithm in profile-based assignment of applications to virtual machines for greener data centers

The increase in data center dependent services has made energy optimization of data centers one of the most exigent challenges in today's Information Age. The necessity of green and energy-efficient measures is very high for reducing carbon footprint and exorbitant energy costs. However, inefficient application management of data centers results in high energy consumption and low resource utilization efficiency. Unfortunately, in most cases, deploying an energy-efficient application management solution inevitably degrades the resource utilization efficiency of the data centers. To address this problem, a Penalty-based Genetic Algorithm (GA) is presented in this paper to solve a defined profile-based application assignment problem whilst maintaining a trade-off between the power consumption performance and resource utilization performance. Case studies show that the penalty-based GA is highly scalable and provides 16% to 32% better solutions than a greedy algorithm.

A penalty-based genetic algorithm for the migration cost-aware virtual machine placement problem in cloud data centers

In the past few years, the virtual machine (VM) placement problem has been studied intensively and many algorithms for the VM placement problem have been proposed. However, those proposed VM placement algorithms have not been widely used in today's cloud data centers as they do not consider the migration cost from current VM placement to the new optimal VM placement. As a result, the gain from optimizing VM placement may be less than the loss of the migration cost from current VM placement to the new VM placement. To address this issue, this paper presents a penalty-based genetic algorithm (GA) for the VM placement problem that considers the migration cost in addition to the energy-consumption of the new VM placement and the total inter-VM traffic flow in the new VM placement. The GA has been implemented and evaluated by experiments, and the experimental results show that the GA outperforms two well known algorithms for the VM placement problem.

A Strength Pareto Evolutionary Algorithm for live migration of multiple interdependent virtual machines in data centers

Although live VM migration has been intensively studied, the problem of live migration of multiple interdependent VMs has hardly been investigated. The most important problem in the live migration of multiple interdependent VMs is how to schedule VM migrations as the schedule will directly affect the total migration time and the total downtime of those VMs. Aiming at minimizing both the total migration time and the total downtime simultaneously, this paper presents a Strength Pareto Evolutionary Algorithm 2 (SPEA2) for the multi-VM migration scheduling problem. The SPEA2 has been evaluated by experiments, and the experimental results show that the SPEA2 can generate a set of VM migration schedules with a shorter total migration time and a shorter total downtime than an existing genetic algorithm, namely Random Key Genetic Algorithm (RKGA). This paper also studies the scalability of the SPEA2.

Structure matters: Adoption of structured classification Approach in the context of cognitive presence classification

Within online learning communities, receiving timely and meaningful insights into the quality of learning activities is an important part of an effective educational experience. Commonly adopted methods – such as the Community of Inquiry framework – rely on manual coding of online discussion transcripts, which is a costly and time consuming process. There are several efforts underway to enable the automated classification of online discussion messages using supervised machine learning, which would enable the real-time analysis of interactions occurring within online learning communities. This paper investigates the importance of incorporating features that utilise the structure of on-line discussions for the classification of "cognitive presence" – the central dimension of the Community of Inquiry framework focusing on the quality of students' critical thinking within online learning communities. We implemented a Conditional Random Field classification solution, which incorporates structural features that may be useful in increasing classification performance over other implementations. Our approach leads to an improvement in classification accuracy of 5.8% over current existing techniques when tested on the same dataset, with a precision and recall of 0.630 and 0.504 respectively.

Engaging elementary students in learning science: An analysis of classroom dialogue

Research over a long period of time has continued to demonstrate problems in the teaching of science in school. In addition, declining levels of participation and interest in science and related fields have been reported from many particularly western countries. Among the strategies suggested is the recruitment of professional scientists and technologists either at the graduate level or advanced career level to change career and teach. In this study, we analysed how one beginning middle primary teacher engaged with students to support their science learning by establishing rich classroom discussions. We followed his evolving teaching expertise over three years focussing on his communicative practices informed by socio-cultural theory. His practices exemplified a non-interactive dialogical communicative approach where ideas were readily discussed but were concentrated on the class acquiring acceptable scientific understandings. His focus on the language of science was a significant aspect of his practice and one that emerged from his professional background. The study affirms the theoretical frameworks proposed by Mortimer and Scott (2003) highlighting how dialogue contributes to heightened student interest in science.

Exploiting the beta distribution-based reputation model in recommender system

Reputation systems are employed to measure the quality of items on the Web. Incorporating accurate reputation scores in recommender systems is useful to provide more accurate recommendations as recommenders are agnostic to reputation. The ratings aggregation process is a vital component of a reputation system. Reputation models available do not consider statistical data in the rating aggregation process. This limitation can reduce the accuracy of generated reputation scores. In this paper, we propose a new reputation model that considers previously ignored statistical data. We compare our proposed model against state-of the-art models using top-N recommender system experiment.

Orienting to emotion in computer-mediated Cognitive Behavioural Therapy

Exploring emotions is a defining feature of psychotherapy. This study explores how therapists explore emotions when they cannot see or hear their clients. In analysing 1,279 sessions of online text-based Cognitive Behavioural Therapy (CBT) we focused on therapists’ commiserations (e.g., “I’m sorry to hear that”) and their affective inferences (e.g., “that sounds very scary for you”). Both practices routinely prefaced moves to pursue a range of therapeutic activities, many of which did not prioritise sustained focus on the emotion that had just been oriented to. By separating message composition from message transmission, the modality used for these therapy sessions enabled therapists to combine orientations to emotion with attempts to shift the focus of discussion. Our analysis finds that although physically co-present and computer-mediated psychotherapy share a common focus on emotional experience, the modality used for therapy can be relevant in the design and use of these orientations. Data are in British English.

A Multiplicative Homomorphic Sealed-Bid Auction-Based on Goldwasser-Micali Encryption

Instead of the costly encryption algorithms traditionally employed in auction schemes, efficient Goldwasser-Micali encryption is used to design a new sealed-bid auction. Multiplicative homomorphism instead of the traditional additive homomorphism is exploited to achieve security and high efficiency in the auction. The new scheme is the currently known most efficient non-interactive sealed-bid auction with bid privacy.

An Efficient and Verifiable Solution to the Millionaire Problem

A new solution to the millionaire problem is designed on the base of two new techniques: zero test and batch equation. Zero test is a technique used to test whether one or more ciphertext contains a zero without revealing other information. Batch equation is a technique used to test equality of multiple integers. Combination of these two techniques produces the only known solution to the millionaire problem that is correct, private, publicly verifiable and efficient at the same time.

Let's Dance: A Language for Service Behavior Modeling

In Service-Oriented Architectures (SOAs), software systems are decomposed into independent units, namely services, that interact with one another through message exchanges. To promote reuse and evolvability, these interactions are explicitly described right from the early phases of the development lifecycle. Up to now, emphasis has been placed on capturing structural aspects of service interactions. Gradually though, the description of behavioral dependencies between service interactions is gaining increasing attention as a means to push forward the SOA vision. This paper deals with the description of these behavioral dependencies during the analysis and design phases. The paper outlines a set of requirements that a language for modeling service interactions at this level should fulfill, and proposes a language whose design is driven by these requirements.

Security of Two-Party Identity-Based Key Agreement

Identity-based cryptography has become extremely fashionable in the last few years. As a consequence many proposals for identity-based key establishment have emerged, the majority in the two party case. We survey the currently proposed protocols of this type, examining their security and efficiency. Problems with some published protocols are noted.

Deniable Authenticated Key Establishment for Internet Protocols

We propose two public-key schemes to achieve “deniable authentication” for the Internet Key Exchange (IKE). Our protocols can be implemented using different concrete mechanisms and we discuss different options; in particular we suggest solutions based on elliptic curve pairings. The protocol designs use the modular construction method of Canetti and Krawczyk which provides the basis for a proof of security. Our schemes can, in some situations, be more efficient than existing IKE protocols as well as having stronger deniability properties.