2019 IEEE MOBILE COMPUTING

A Secure AMR Fixed Codebook Steganographic Scheme Based on Pulse Distribution Model

Abstract: Adaptive multi-rate (AMR), a popular audio compression standard, is widely used in mobile communication and mobile Internet applications and has become a novel carrier for hiding information. To improve the statistical security, this paper presents a steganographic scheme in the AMR fixed codebook (FCB) domain based on the pulse distribution model (PDMAFS), which is obtained from the distribution characteristics of the FCB value in the cover audio. The pulse positions in stego audio are controlled by message encoding and random masking to make the statistical distribution of the FCB parameters close to that of the cover audio. The experimental results show that the statistical security of the proposed scheme is better than that of the existing schemes. Furthermore, the hiding capacity is maintained compared with the existing schemes. The average hiding capacity can reach 2.06 kbps at an audio compression rate of 12.2 kbps and the auditory concealment is good. To the best of our knowledge, this is the first secure AMR FCB steganographic scheme that improves the statistical security based on the distribution model of the cover audio. This scheme can be extended to other audio compression codecs under the principle of Algebraic Code Excited Linear Prediction (ACELP), such as G.723.1 and G.729.

A Stackel berg Game Approach Toward Socially-Aware Incentive Mechanisms for Mobile Crowd sensing

Abstract: Mobile crowd sensing has shown a great potential to address large-scale data sensing problems by allocating sensing tasks to pervasive mobile users. The mobile users will participate in a crowd sensing platform if they can receive a satisfactory reward. In this paper, to recruit effectively and efficiently sufficient number of mobile users, i.e., participants, we investigate an optimal incentive mechanism of a crowd sensing service provider. We apply a two-stage Stackel berg game to analyze the participation level of the mobile users and the optimal incentive mechanism of the crowd sensing service provider using backward induction. In order to motivate the participants, the incentive mechanism is designed by taking into account the social network effects from the underlying mobile social domain. We derive the analytical expressions for the discriminatory incentive as well as the uniform incentive mechanisms. To fit into practical scenarios, we further formulate a Bayesian Stackel berg game with incomplete information to analyze the interaction between the crowd sensing service provider and mobile users, where the social structure information, i.e., the social network effects, is uncertain. The existence and uniqueness of the Bayesian Stackel berg equilibrium is analytically validated by identifying the best response strategies of the mobile users. Numerical results corroborate the fact that the network effects significantly stimulate higher mobile participation level and greater revenue of the crowd sensing service provider. In addition, the social structure information helps the crowd sensing service provider to achieve greater revenue gain.

Backup Battery Analysis and Allocation against Power Outage for Cellular Base Stations

Abstract: Base stations have been widely deployed to satisfy the service coverage and explosive demand increase in today’s cellular networks. Their reliability and availability heavily depend on the electrical power supply. Battery groups are installed as backup power in most of the base stations in case of power outages due to severe weathers or human-driven accidents, particularly in remote areas. The limited numbers and capacities of batteries, however, can hardly sustain a long power outage without a well-designed allocation strategy. As a result, the service interruption occurs along with an increasing maintenance cost. Meanwhile, a deep discharge of a battery in such case can also accelerate the battery degradation and eventually contribute to a higher battery replacement cost. In this paper, we closely examine the base station features and backup battery features from a 1.5-year dataset of a major cellular service provider, including 4,206 base stations distributed across 8,400 square kilometers and more than 1.5 billion records on base stations and battery statuses. Through exploiting the correlations between the battery working conditions and battery statuses, we build up a deep learning based model to estimate the remaining lifetime of backup batteries. We then develop BatAlloc, a battery allocation framework to address the mismatch between the battery supporting ability and diverse power outage incidents. We present an effective solution that minimizes both the service interruption time and the overall cost. Our real world trace-driven experiments show that BatAlloc cuts down the average service interruption time from 4.7 hours to nearly zero with only 85% of the overall cost compared to the current practical allocation.

Counting Human Objects Using Backscattered Radio Frequency Signals

Abstract: In this paper, we propose a system called R# to estimate the number of human objects using passive RFID tags but without attaching anything to human objects. The idea is based on our observation that the more human objects are present, the higher the variation in the RSS values of the tag backscattered RF signals. Thus, based on the received RF signals, the reader can estimate the number of human objects. R# includes an RFID reader and some (say 20) passive tags, which are deployed in the region that we want to monitor the number of human objects, such as the region in front of a supermarket shelf. The RFID reader periodically emits RF signals to identify all tags and the tags simply respond with their IDs via EPCglobal Class 1 Generation 2 protocol. We implemented R# using commercial Impinj H47 passive RFID tags and Impinj reader model R420. We conducted experiments in a simulated picking aisle area of the supermarket environment. The experimental results show that R# can achieve high estimation accuracy (more than 90% with up to ten human objects).

Energy-Efficient Dynamic Computation Offloading and Cooperative Task Scheduling in Mobile Cloud Computing

Abstract: Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance com- putation capability and save energy for smart mobile devices (SMDs) by offloading computation-intensive tasks from resource- constrained SMDs onto resource-rich cloud. However, how to achieve energy-efficient computation offloading under hard constraint for Application completion time remains a challenge. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate the eDors problem into an energy-efficiency cost (EEC) minimization problem while satisfying task-dependency requires- ment and completion time deadline constraint. We then propose a distributed eDors algorithm consisting of three sub algorithms of computation offloading selection, clock frequency control and transmission power allocation. Next, we show that computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, we provide experimental results in a real test bed and demonstrate that the eDors algorithm can effectively reduce EEC by optimally adjusting CPU clock frequency of SMDs in local computing, and adapting the transmission power for wireless channel conditions in cloud computing.

Follow-Me Cloud When Cloud Services Follow Mobile Users

Abstract: The trend towards the codification of the 3GPP LTE mobile network architecture and the emergence of federated cloud infrastructures call for alternative service delivery strategies for improved user experience and efficient resource utilization. We propose Follow-Me Cloud (FMC), a design tailored to this environment, but with a broader applicability, which allows mobile users to always be connected via the optimal data anchor and mobility gateways, while cloud-based services follow them and are delivered via the optimal service point inside the cloud infrastructure. FMC applies a Markov-Decision-Process-based algorithm for cost-effective, performance-optimized service migration decisions, while two alternative schemes to ensure service continuity and disruption-free operation are proposed, based on either Software Defined Networking technologies or the Locator/Identifier  Separation  Protocol. Numerical results from our analytic model for FMC, as well as test bed experiments with the two alternative FMC implementations we have developed, demonstrate quantitatively and qualitatively the advantages it can bring about.

Food Net Toward an Optimized Food Delivery Network Based on Spatial Crowd sourcing

Abstract: This paper builds a Food Delivery Network (Food Net in short) using spatial crowd sourcing (SC). It investigates the participation of urban taxis to support on demand take-out food delivery. Unlike existing SC-enabled service sharing systems (e.g., ridesharing), the delivery of food in Food Net is more time-sensitive and the optimization problem is more complex regarding high-efficiency, huge-number of delivery needs. In particular, two on demand food delivery problems under different situations are studied in our work: (1) for O-OTOD, the food is opportunistically delivered by taxis when carrying passengers, and the optimization goal is to minimize the number of selected taxis to maintain a relatively high incentive to the participated drivers; (2) for D-OTOD, taxis dedicatedly deliver food without taking passengers, and the aim is to minimize the number of selected taxis (i.e., to raise the reward for each participant) and the total traveling distance to reduce the cost. A two-stage approach, including the construction algorithm and the Adaptive Large Neighborhood Search (ALNS) algorithm based on simulated annealing, is proposed to solve the problem. We have conducted extensive experiments based on the real-world datasets, including city-wide restaurant data, cell tower data, and the large-scale taxi trajectory data with 10,000 taxis in the city of Chengdu, China. Experimental results demonstrate that our proposed algorithms are more effective and efficient than baselines, fulfilling the food delivery service using a smaller number of taxis within the given time.

Signal Processing of Multimodal Mobile Life logging Data Towards Detecting Stress in Real-World Driving

Abstract: Stress is a negative emotion that is part of everyday life. However, frequent episodes or prolonged periods of stress can be detrimental to long-term health. Nevertheless, developing self-awareness is an important aspect of fostering effective ways to self-regulate these experiences. Mobile life logging systems provide an ideal platform to support self-regulation of stress by raising awareness of negative emotional states via continuous recording of psychophy siological and behavioral data. However, obtaining meaningful information from large volumes of raw data represents a significant challenge because these data must be accurately quantified and processed before stress can be detected. This work describes a set of algorithms designed to process multiple streams of life logging data for stress detection in the context of real world driving. Two data collection exercises have been performed where multimodal data, including raw cardiovascular activity and driving information, were collected from twenty-one people during daily commuter journeys. Our approach enabled us to 1) pre-process raw physiological data to calculate valid measures of heart rate variability, a significant marker of stress, 2) identify/correct artefacts in the raw physiological data and 3) provide a comparison between several classifiers for detecting stress. Results were positive and ensemble classification models provided a maximum accuracy of 86.9% for binary detection of stress in the real-world.

2019 IEEE DATA MINING

Comments Mining With TF-IDF the Inherent Bias and Its Removal

Abstract: Text mining has gained great momentum in recent years, with user-generated content becoming widely available. One key use is comment mining, with much attention being given to sentiment analysis and opinion mining. An essential step in the process of comment mining is text pre-processing; a step in which each linguistic term is assigned with a weight that commonly increases with its appearance in the studied text, yet is offset by the frequency of the term in the domain of interest. A common practice is to use the well-known tf-idf formula to compute these weights. This paper reveals the bias introduced by between-participants’ discourse to the study of comments in social media, and proposes an adjustment. We find that content extracted from discourse is often highly correlated, resulting in dependency structures between observations in the study, thus introducing a statistical bias. Ignoring this bias can manifest in a non-robust analysis at best and can lead to an entirely wrong conclusion at worst. We propose an adjustment to tf-idf that accounts for this bias. We illustrate the effects of both the bias and correction with with seven Face book fan pages data, covering different domains, including news, finance, politics, sport, shopping, and entertainment.

Collaborative Optimization of Service Composition for Data-Intensive Applications in a Hybrid Cloud

Abstract: The multi-valued evaluations of quality of service (QoS), the complicated constraints between cloud services (CSs) and the collaborative resource assignments add many difficulties to the problem of CS composition for data-intensive applications (DiA) in a hybrid cloud (CSCD-HC). Solving the CSCD-HC problem has become a challenging task due to the uncertain QoS, the diverse hardware configurations and the flexible pricing about CSs. This paper proposes a collaborative optimization approach for CSCD-HC. This approach models a DiA as a role-based collaboration (RBC) system and employs the environments – classes, agents, roles, groups, and objects (E-CARGO) model to formalize the CSCD-HC problem with complicated constraints. To deal with the multi-valued QoS evaluations, this paper exploits the cloud model theory to analyze the performance of CSs, and presents a new method utilizing the Mahalanobis distance to improve the similarity calculation of QoS cloud models. Based on it, the qualification of candidate CSs can be precisely measured for supporting CS composition. A solution via the IBM ILOG CPLEX optimization package is put forward to solve the CSCD-HC problem. The experimental results demonstrate that the proposed approach is effective and feasible for optimizing CSCD-HC.

Cause Infer Automated End-to-End Performance Diagnosis with Hierarchical Causality Graph in Cloud Environment

Abstract: Modern computing systems especially cloud-based and cloud-centric systems always consist of a mass of components running in large distributed environments with complicated interactions. They are vulnerable to performance problems due to the highly dynamic runtime environment changes (e.g., overload and resource contention) or software bugs (e.g., memory leak). Unfortunately, it is notoriously difficult to diagnose the root causes of these performance problems in a fine granularity due to complicated interactions and a large cardinality of potential cause set. In this paper, we build an automated, black-box and end-to-end cause inference system named Cause Infer to pinpoint the root causes or at least provide some hints. Cause Infer can automatically map a distributed system to a two-layer hierarchical causality graph and infer the root causes along the causal paths in the causality graph. Cause Infer models the fault propagation paths in an explicit way and works without instrumentation to the running production system, which makes Cause Infer more effective and practical than previous approaches. The experimental evaluations in two benchmark systems show that Cause Infer can identify the root causes in a high accuracy. Compared to several state-of-the-art approaches, Cause Infer can achieve over 10% improvement. Moreover, Cause Infer is lightweight and flexible enough to readily scale out in large distributed systems. With Cause Infer, the mean time to recovery (MTTR) of the cloud systems can be significantly reduced.

Chameleon A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field

Abstract: Auto-scalars for clouds promise stable service quality at low costs when facing changing workload intensity. The major public cloud providers provide trigger-based auto-scalars based on thresholds. However, trigger-based auto-scaling has reaction times in the order of minutes. Novel auto-scalars from literature try to overcome the limitations of reactive mechanisms by employing proactive prediction methods. However, the adoption of proactive auto-scalars in production is still very low due to the high risk of relying on a single proactive method. This paper tackles the challenge of reducing this risk by proposing a new hybrid auto-scaling mechanism, called Chameleon, combining multiple different proactive methods coupled with a reactive fallback mechanism. Chameleon employs on-demand, automated time series-based forecasting methods to predict the arriving load intensity in combination with run-time service demand estimation to calculate the required resource consumption per work unit without the need for application instrumentation. We benchmark Chameleon against five different state-of-the-art proactive and reactive auto-scalars one in three different private and public cloud environments. We generate five different representative workloads each taken from different real-world system traces. Overall, Chameleon achieves the best scaling behavior based on user and elasticity performance metrics, analyzing the results from 400 hours aggregated experiment time.

An Efficient Semi-Supervised Multi-label Classifier Capable of Handling Missing Labels

Abstract: Multi-label classification has received considerable interest in recent years. Multi-label classifiers usually need to address many issues including: handling large-scale datasets with many instances and a large set of labels, compensating missing label assignments in the training set, considering correlations between labels, as well as exploiting unlabeled data to improve prediction performance. To tackle datasets with a large set of labels, embedding-based methods represent the label assignments in a low dimensional space. Many state-of-the-art embedding-based methods use a linear dimensionality reduction to map the label assignments to a low-dimensional space. However, by doing so, these methods actually neglect the tail labels - labels that are infrequently assigned to instances. In this paper, we propose an embedding-based method that non-linearly embeds the label vectors using a stochastic approach, thereby predicting the tail labels more accurately. Moreover, the proposed method has excellent mechanisms for handling missing labels, dealing with large-scale datasets, as well as exploiting unlabeled data. Experiments on real-world datasets show that our method outperforms state-of-the-art multi-label classifiers by a large margin, in terms of prediction performance, as well as training time. Our implementation of the proposed method is available online.

Automated Discovery of Process Models from Event Logs Review and Benchmark

Abstract:  Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes un reproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering twelve publicly-available real-life event logs, twelve proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.

Budget and Deadline Aware e-Science Workflow Scheduling in Clouds

Abstract: Basic science is becoming ever more computationally intensive, increasing the need for large-scale compute and storage resources, be they within a High Performance Computer cluster, or more recently within the cloud. In most cases, large scale scientific computation is represented as a workflow for scheduling and runtime provisioning. Such scheduling become an even more challenging problem on cloud systems due to the dynamic nature of the cloud, in particular, the elasticity, the pricing models (both static and dynamic), the non-homogeneous resource types, the vast array of services, and Virtualization. This mapping of workflow tasks on to a set of provisioned instances is an example of the general scheduling problem and is NP-complete. In addition, we also need to ensure that certain runtime constraints are met - the most typical being the cost of the computation and the time which that computation requires to complete. In this article, we introduce a new heuristic scheduling algorithm, Budget Deadline Aware Scheduling (BDAS) that addresses eScience workflow scheduling under budget and deadline constraints in Infrastructure as a Service (IaaS) clouds. The novelty of our work is satisfying both budget and deadline constraints while introducing a tunable cost-time trade off over heterogeneous instances. In addition, we study the stability and robustness of our algorithm by performing sensitivity analysis. The results demonstrate that overall BDAS finds a viable schedule for more than 40000 test cases accomplishing both defined constraints. Budget and deadline. Moreover, our algorithm achieves a 17:0 􀀀 23:8% higher success rate when compared to state of the art algorithms.

Adversarial Deep Learning Models with Multiple Adversaries

Abstract: We develop an adversarial learning algorithm for supervised classification in general and Convolutional Neural Networks (CNN) in particular. The algorithm’s objective is to produce small changes to the data distribution defined over positive and negative class labels so that the resulting data distribution is misclassified by the CNN. The theoretical goal is to determine a manipulating change on the input data that finds learner decision boundaries where many positive labels become negative labels. Then we propose a CNN which is secure against such unforeseen changes in data. The algorithm generates adversarial manipulations by formulating a multiplayer stochastic game targeting the classification performance of the CNN. The multiplayer stochastic game is expressed in terms of multiple two-player sequential games. Each game consists of interactions between two players – an intelligent adversary and the learner CNN – such that a player’s payoff function increases with interactions. Following the convergence of a sequential non co operative Stackel berg game, each two-player game is solved for the Nash equilibrium. The Nash equilibrium finds a pair of strategies (learner weights and evolutionary operations) from which there is no incentive for either learner or adversary to deviate. We then retrain the learner over all the adversarial manipulations generated by multiple players to propose a secure CNN which is robust to subsequent adversarial data manipulations. The adversarial data and corresponding CNN performance is evaluated on MNIST handwritten digits data. The results suggest that game theory and evolutionary algorithms are very effective in securing deep learning models against performance vulnerabilities simulated as attack scenarios from multiple adversaries.

Adaptive Distributed RDF Graph Fragmentation and Allocation based on Query Workload

Abstract: As massive volumes of Resource Description Framework (RDF) data are growing, designing a distributed RDF database system to manage them is necessary. In designing this system, it is very common to partition the RDF data into some parts, called fragments, which are then distributed. Thus, the distribution design comprises two steps: fragmentation and allocation. In this study, we explore the workload for fragmentation and allocation, which aims to reduce the communication cost during SPARQL query processing. Specifically, we adaptively maintain some frequent access patterns (FAPs) to reflect the characteristics of the workload while ensuring the data integrity and approximation ratio. Based on these frequent access patterns, we propose three fragmentation strategies, namely vertical, horizontal and mixed fragmentation, to divide RDF graphs while meeting different types of query processing objectives. After fragmentation, we discuss how to allocate these fragments to various sites while balancing the fragments. Finally, we discuss how to process queries based on the results of fragmentation and allocation. Experiments over large RDF datasets confirm the superior performance of our proposed solutions.

A Comparative Study of Ontology Matching Systems via Inferential Statistics

Abstract: Comparing ontology matching systems are typically performed by comparing their average performances over multiple datasets. However, this paper examines the alignment systems using statistical inference since averaging is statistically unsafe and inappropriate. The statistical tests for comparison of two or multiple alignment systems are theoretically and empirically reviewed. For comparison of two systems, the Wilcox on signed-rank and McNamara’s mid-p and asymptotic tests are recommended due to their robustness and statistical safety in different circumstances. The Friedman and Quade tests with their corresponding post-hoc procedures are studied for comparison of multiple systems, and their [dis]advantages are discussed. The statistical methods are then applied to benchmark and multiform tracks from the ontology matching evaluation initiative (OAEI) 2015 and their results are reported and visualized by critical difference diagrams.

A General Theory of IR Evaluation Measures

Abstract:  Interval scales are assumed by several basic descriptive statistics, such as mean and variance, and by many statistical significance tests which are daily used in IR to compare systems. Unfortunately, so far, there has not been any systematic and formal study to discover the actual scale properties of IR measures. Therefore, in this paper, we develop a theory of IR evaluation measures, based on the representational theory of measurements, to determine whether and when IR measures are interval scales. We found that common set-based retrieval measures – namely Precision, Recall, and F-measure – always are interval scales in the case of binary relevance while this happens also in the case of multi-graded relevance only when the relevance degrees themselves are on a ratio scale and we define a specific partial order among systems. In the case of rank-based retrieval measures – namely AP, gRBP, DCG, and ERR – only gRPB is an interval scale when we choose a specific value of the parameter p and define a specific total order among systems while all the other IR measures are not interval scales. Besides the formal framework itself and the proof of the scale properties of several commonly used IR measures, the paper also defines some brand new set-based and rank-based IR evaluation measures which ensure to be interval scales.

A New Formulation of Linear Discriminant Analysis for Robust Dimensionality Reduction

Abstract: Dimensionality reduction is a critical technology in the domain of pattern recognition, and linear discriminator analysis (LDA) is one of the most popular supervised dimensionality reduction methods. However, whenever its distance criterion of objective function uses L2-norm, it is sensitive to outliers. In this paper, we propose a new formulation of linear discriminant analysis via joint L2;1-norm minimization on objective function to induce robustness, so as to efficiently alleviate the influence of outliers and improve the robustness of proposed method. An efficient iterative algorithm is proposed to solve the optimization problem and proved to be convergent. Extensive experiments are performed on an artificial data set, on UCI data sets and on four face data sets, which sufficiently demonstrates the efficiency of comparing to other methods and robustness to outliers of our approach.

A Note on the Behavior of Majority Voting in Multi-Class Domains with Biased Annotators

Abstract: Majority voting is a popular and robust strategy to aggregate different opinions in learning from crowds, where each worker labels examples according to their own criteria. Although it has been extensively studied in the binary case, its behavior with multiple classes is not completely clear, specifically when annotations are biased. This paper attempts to fill that gap. The behavior of the majority voting strategy is studied in-depth in multi-class domains, emphasizing the effect of annotation bias. By means of a complete experimental setting, we show the limitations of the standard majority voting strategy. The use of three simple techniques that infer global information from the annotations and annotators allows us to put the performance of the majority voting strategy in context.

A Rapid Hybrid Clustering Algorithm for Large Volumes of High Dimensional Data

Abstract: Clustering large volumes of high-dimensional data is a challenging task. Many clustering algorithms have been developed to address either handling datasets with a very large sample size or with a very high number of dimensions, but they are often impractical when the data is large in both aspects. To simultaneously overcome both the ‘curse of dimensionality’ problem due to high dimensions and scalability problems due to large sample size, we propose a new fast clustering algorithm called FensiVAT. FensiVAT is a hybrid, ensemble-based clustering algorithm which uses fast data-space reduction and an intelligent sampling strategy. In addition to clustering, FensiVAT also provides visual evidence that is used to estimate the number of clusters (cluster tendency assessment) in the data. In our experiments, we compare FensiVAT with nine state-of-the-art approaches which are popular for large sample size or high-dimensional data clustering. Experimental results suggest that FensiVAT, which can cluster large volumes of high-dimensional datasets in a few seconds, is the fastest and most accurate method of the ones tested.

2019 IEEE CLOUD COMPUTING

A Bi-layered Parallel Training Architecture for Large-Scale Convolutional Neural Networks

Abstract: Benefitting from large-scale training datasets and the complex training network, Convolutional Neural Networks (CNNs) are widely applied in various fields with high accuracy. However, the training process of CNNs is very time-consuming, where large amounts of training samples and iterative operations are required to obtain high-quality weight parameters. In this paper, we focus on the time-consuming training process of large-scale CNNs and propose a Bi-layered Parallel Training (BPT-CNN) architecture in distributed computing environments. BPT-CNN consists of two main components: (a) an outer-layer parallel training for multiple CNN sub networks on separate data subsets, and (b) an inner-layer parallel training for each sub network. In the outer-layer parallelism, we address critical issues of distributed and parallel computing, including data communication, synchronization, and workload balance. A heterogeneous aware Incremental Data Partitioning and Allocation (IDPA) strategy is proposed, where large-scale training datasets are partitioned and allocated to the computing nodes in batches according to their computing power. To minimize the synchronization waiting during the global weight update process, an Asynchronous Global Weight Update (AGWU) strategy is proposed. In the inner-layer parallelism, we further accelerate the training process for each CNN subnetwork on each computer, where computation steps of convolutional layer and the local weight training are parallelized based on task-parallelism. We introduce task decomposition and scheduling strategies with the objectives of thread-level load balancing and minimum waiting time for critical paths. Extensive experimental results indicate that the proposed BPT-CNN effectively improves the training performance of CNNs while maintaining the accuracy.

 A  Cloud Brokerage Architecture for Efficient Cloud Service Selection

Abstract: The expanding cloud computing services offer great opportunities for consumers to find the best service and best pricing. Meanwhile, it also raises new challenges for consumers who need to select the best service out of such a huge pool since it will be time-consuming for consumers to collect the necessary information and analyze all service providers to make the decision. Therefore, in this paper, we propose a novel brokerage-based architecture in the cloud, where the cloud brokers is responsible for the service selection. We also design an efficient indexing structure, called B cloud-tree, for managing the information of a large number of cloud service providers. We then develop the service selection algorithm that recommends most suitable cloud services to the cloud consumers. We carry out extensive experimental studies on real and synthetic cloud data, and demonstrate a significant performance improvement over previous approaches.

A Cost-Efficient Router Architecture for HPC Inter-Connection Networks Design and Implementation

Abstract: High-radix routers with lower latency and higher bandwidth play an increasingly important role in constructing large-scale interconnection networks such as those used in super-computers and datacenters. The tile-based crossbar approach partitions a single large crossbar into many small tiles and can considerably reduce the complexity of arbitration while providing higher throughput than the conventional switch implementation. However, it is not scalable due to power consumption, placement, and routing problems. Inspired by non-saturated throughput theory, this paper proposes a scalable router microarchitecture, termed Multiport Binding Tile-based Router (MBTR). By aggregating multiple physical ports into a single tile a high-radix router can be flexibly organized into different tile arrays, thus the number of tiles and hardware overhead can be considerably reduced. For a radix-64 router MBTR achieves up to 50 ⇠ 75% reduction in memory consumption as well as wire area compared with a hierarchical switch. We theoretically deduce the sufficient and necessary conditions for the asymmetrical crossbar to achieve un-saturated relative 100% throughput. Based on this observation we analyze the MBTR throughput and derive the condition that should be satisfied by the MBTR design parameters to yield 100% throughput. We further discuss how to make a trade-off between MBTR parameters based on the constraints of performance, power and area. The simulation results demonstrate MBTR is indistinguishable from the YARC router in terms of throughput and delay, and can even outperform it by reducing potential contention for output ports. We have fabricated a 36-port MBTR chip at 28nm, providing 100 Gb/s bidirectional bandwidth per port, with a fall-through latency of just 30ns. Internally it runs at 9.6Tb/s, thus offering a speedup of 1.34⇥.

A Distributed Discovery of Communicating Resource Systems Models

Abstract: |In communicating resource systems (CRS), a set of independent, hierarchically composed resources communicate to realize  a business process. Examples of CRSs include REST ful Web services, cloud computing platforms and hierarchical distributed systems. Unfortunately, their complexity, distributed nature, and usually long lifespan can lead to problems like live locks, deadlock occurrences and invocation loops. This makes CRSs hard to maintain and manage; it also leads to a loss of QoS or system failures. Moreover, it is important to take into account, in system management, the exact way how the system gets to some desired or undesired state. Therefore, it is crucial to analyze processes, executed in a system to _nd and _x the above problems. Unfortunately, CRS process models are hardly ever available. Therefore, we present a new distributed algorithm, dRMA, discovering process models from CRS event logs. In order to reduce the representational bias between the discovered process model and the real process, we have de_ned a Communication Net, which can express features of CRSs: directed communication channels, hierarchy, and a resource perspective. Finally, we have evaluated dRMA on a cluster, and have showed that distributing jobs can signi_cantly reduce the time of process model discovery.

A Distributed Secure Outsourcing Scheme for Solving Linear Algebraic Equations in Ad Hoc Clouds

Abstract: The emerging ad hoc clouds form a new cloud computing paradigm by leveraging untapped local computation and storage resources. An important application of ad hoc clouds is to outsource computational intensive problems to nearby cloud agents. Specifically, for the problem of solving a linear algebraic equation (LAE), an outsourcing client assigns each cloud agent a sub problem, and then all involved agents apply a consensus-based algorithm to obtain the correct solution of the LAE in an iterative and distributed manner. However, such a distributed collaboration paradigm suffers from cyber security threats that undermine the confidentiality of the outsourced problem and the integrity of the returned results. In this paper, we identify a number of such security threats in this process, and propose a secure outsourcing scheme which not only preserves the privacy of the LAE parameters and the final solution from the participating agents, but also guarantees the correctness of the final solution. We prove that the proposed scheme has low computation complexity at each agent, and is robust against the identified security attacks. Numerical and simulation results are presented to demonstrate the effectiveness of the proposed method.

A Distributed Trust Evaluation Protocol with Privacy Protection for Intercloud

Abstract: Intercloud seeks to facilitate resource sharing among clouds. To support Intercloud, a trust evaluation framework among clouds and users is required. For trust evaluation, conventional protocols are typically based on a centralized  Architecture  focusing  on a one-way relationship. For Intercloud, the environment is highly dynamic and distributed, and relationships can be one-way or two-way (i.e., clouds provide services to each other). This paper presents a distributed trust evaluation protocol with privacy protection for Intercloud. The new contributions and innovative features are summarized below. First, feedback is protected by homomorphic encryption with verifiable secret sharing. Second, to cater to the dynamic nature of Intercloud, trust evaluation can be conducted in a distributed manner and is functional even when some of the parties are offline. Third, to facilitate customized trust evaluation, an innovative mechanism is used to store feedback, such that it can be processed flexibly while protecting feedback privacy. The protocol has been proved based on a formal security model. Simulations have been performed to demonstrate the effectiveness of the protocol. The results show that even when half of the clouds are malicious or offline, by choosing suitable operational parameters the protocol can still support effective trust evaluation with privacy protection.

A General Analysis Framework for Soft Real-Time Tasks

Abstract: Much recent work has been conducted on supporting soft real-time tasks on multiprocessors due to the multicore revolution. While most earlier works focus on the traditional sporadic task model with deterministic worst-case specification, several recent works investigate the stochastic nature of many workloads seen in practice, specifying task execution times using average-case provisioning instead of the worst case. Unfortunately, all the existing work on supporting soft real-time workloads ignores a simple practical fact that the job inter-arrival time (or task period) is also stochastic for many real-world applications. Adopting a fixed worst-case period to model all the arriving pattern is rather pessimistic and may result in significant capacity loss in practice. Based on these observations, we present a general soft real-time multiprocessor schedulability analysis framework in this paper for practical sporadic task systems specified by stochastic period and execution demand, following probability distributions. Our analysis can be generally applied to global tunable priority-based schedulers, which allow any job’s priority to be changed dynamically at runtime within a priority window of constant length. We have extensively evaluated the analysis framework using a MPEG video decoding case study and simulation-based experiments. Experimental results demonstrate significant advantages of our analysis, which yields over 200% and 50% improvements compared to existing analysis assuming worst-case task periods in terms of schedulability and magnitude of the derived tardiness bound, respectively.

A Hybrid Approach for Optimizing Carbon Footprint in InterCloud Environment

Abstract: This paper focuses on the problem of workload placement in an Inter Cloud with the view of minimizing the carbon footprint of such a computing environment. In order to reduce the ecological impact of the data center Greenhouse Gas (GhG) missions, this paper addresses the problem as a whole, by proposing a global mathematical formulation, based on the joint optimization of the Virtual Machine (VM) placement and their related traffics, along with a workload consolidation method and a cooling maximization technique that considers the dynamic behavior of the cooling fans. As the Virtual Machine Placement Problem (VMPP) is classified as an NP-hard problem, with the addition of the traffic embedding, the problem becomes more complex and stays NP-hard. Therefore, we propose a hybrid approach, for solving such problem and find good feasible solutions in a polynomial time. The results obtained from comparing with the exact method and other reference approaches help in assessing the efficiency of the proposed algorithm, as the carbon footprint costs are relatively close to the lower bound, with an average gap of about 3%, and found within a reasonable amount of time.

A Hybrid Approach for Optimizing Parallel Clustering Throughput using the GPU

Abstract: We introduce HYBRID-DBSCAN, that uses the GPU and CPUs for optimizing clustering throughput. The main idea is to exploit the memory bandwidth on the GPU for fast index searches, and optimize data transfers between host and GPU, to alleviate the potential negative performance impact of the PCIe interconnect. We propose and compare two GPU kernels that exploit grid-based indexing schemes to improve neighborhood search performance. We employ a batching scheme for host-GPU data transfers to obviate limited GPU memory, and exploit concurrent operations on the host and GPU. This scheme is robust with respect to both sparse and dense data distributions and avoids buffer overflows that would otherwise degrade performance. We evaluate our approaches on ionospheric total electron content datasets as well as intermediate-redshift galaxies from the Sloan Digital Sky Survey. HYBRID-DBSCAN outperforms the reference implementation across a range of application scenarios, including small workloads, which typically is the domain of CPU-only algorithms. We advance an empirical response time performance model of HYBRID-DBSCAN by utilizing the underlying properties of the datasets. With only a single execution of HYBRID-DBSCAN on a dataset, we are able to accurately predict the response time for a range of _ search distances.

A Modified Hierarchical Attribute-Based Encryption Access Control Method for Mobile Cloud Computing

Abstract: Cloud computing is an Internet-based computing pattern through which shared resources are provided to devices on demand. Its an emerging but promising paradigm to integrating mobile devices into cloud computing, and the integration performs in the cloud based hierarchical multi-user data-shared environment. With integrating into cloud computing, security issues such as data confidentiality and user authority may arise in the mobile cloud computing system, and it is concerned as the main constraints to the developments of mobile cloud computing. In order to provide safe and secure operation, a hierarchical access control method using modified hierarchical attribute-based encryption (M-HABE) and a modified three-layer structure is proposed in this paper. In a specific mobile cloud computing model, enormous data which may be from all kinds of mobile devices, such as smart phones, functioned phones and PDAs and so on can be controlled and monitored by the system, and the data can be sensitive to unauthorized third party and constraint to legal users as well. The novel scheme mainly focuses on the data processing, storing and accessing, which is designed to ensure the users with legal authorities to get corresponding classified data and to restrict illegal users and unauthorized legal users get access to the data, which makes it extremely suitable for the mobile cloud computing paradigms.

A Novel Task-Duplication Based Clustering Algorithm for Heterogeneous Computing Environments

Abstract: As a crucial task in heterogenous distributed systems, DAG-scheduling models a scheduling application with a set of distributed tasks by a Direct Acyclic Graph (DAG). The goal is to assign tasks to different processors so that the whole application can finish as soon as possible. Task Duplication-Based (TDB) scheme is an important technique addressing this problem. The main idea is to duplicate tasks on multiple machines so that the results of the duplicated tasks are available on multiple machines to trade computation time for communication time. Existing TDB algorithms enumerate and test all possible duplication candidates, and only keep the candidates that can improve the overall scheduling. We observe that while a duplication candidate is ineffective at the moment, after other duplications have been applied, this ineffective duplication candidate can become effective, which in turn can cause other ineffective duplications to become effective. We call this phenomenon the chain reaction of task duplication. We propose a novel Task Duplication based Clustering Algorithm (TDCA) to improve the schedule performance by utilizing duplication task more thoroughly. TDCA improves parameter calculation, task duplication, and task merging. The analysis and experiments are based on randomly generated graphs with various characteristics, including DAG depth and width, communication-computing cost ration, and variant computation power of processors. Our results demonstrate that the TDCA algorithm is very competitive. It improves the schedule makes pan of task duplication-based algorithms for heterogeneous systems for various communication computing costratios.