Abstract

Big-data application processing is increasingly geo-distributed, a paradigm shift from the traditional cluster-based processing frameworks. As the communication time for data movement across geo-distributed data centers is not a design criterion for traditional cluster-based processing approaches, there are research gaps in the algorithms used for staging and scheduling big-data applications for geo-distributed clusters. We address these gaps by proposing Harmony, an approach consisting of both staging and scheduling strategies to minimize an application's total execution time. The staging strategy of Harmony exploits the intra-stage parallelism by having concurrent operators within a stage in contrast to the traditional Apache spark which uses fine-grained stages, thus reducing the computation time within each stage. Secondly, the scheduling strategy of Harmony reduces data transfers between geo-distributed data centers by exploiting data locality and thus reducing communication time and total execution time. The proposed approach Harmony achieves a speedup of two times with respect to geo-distributed Apache Spark. In addition, Harmony achieves a speedup of 1.6 times and 2.1 times when compared with the state-of-the-art framework Iridium for geo-distributed analytics over five locations with uniform and non-uniform network link bandwidths respectively.

Abstract

The focus of the ongoing Digital and Industry 4.0 revolution is on re-engineering business operations to take advantage of various technologies, including robotics. Conceptualizing, say, a robotics solution to automate aspects of warehouse operations involves multiple activities: understanding the problem space in sufficient detail to identify the right automation opportunities; working through the space of possible solution options and developing the solution design; and building a prototype solution with sufficient functional detail to enable customer experts to assess its suitability with respect to multiple concerns, and its impact on the business processes and environment. Only after that can a project be initiated to engineer and deploy the production solution, while making the necessary changes to the business system.

Abstract

Domain Specific Modeling Languages (DSML) significantly improve productivity in designing Computer Based System (CBS), by enabling them to be modeled at higher levels of abstraction. It is common for large and complex systems with distributed teams, to use DSMLs, to express and communicate designs of such systems uniformly, using a common language. DSMLs enable domain experts, with no or minimal software development background, to model solutions, using the language and terminologies used in their respective domains. Although, there are already a number of DSMLs available for modeling CBSs, their need is felt strongly across multiple domains, which still are not well supported with DSMLs. Developing a new DSML, however, is non trivial, as it requires (a) significant knowledge about the domain for which the DSML needs to be developed, as well as (b) skills to create new languages. In the …

Abstract

With the technology scaling down to sub-50 nm regime, the necessity of process variation aware estimation of Leakage Power is emphasized for robust digital circuit design. Variations in Leakage power results in a large increase in the variation of total power dissipation. This paper presents a Regression based estimation of leakage powers and total power dissipation in nanoscale standard cell-based designs that show an impressive speed-up advantage with respect to analog SPICE-level simulation. We propose a statistical variation aware estimation model through a Multivariate Linear Regression (MLR) and Multivariate Polynomial Regression (MPR) techniques. Exhaustive tests report shows MPR technique outperforms MLR technique in estimating the leakage and total power for the targeted 16 nm CMOS technology with negligible error (<1%). The proposed methodology works as black box i.e. equally valid for 16 nm, 22 nm and 45 nm technology nodes.

Abstract

Student internships are necessary for every student to learn and apply the learning to deliver tangible and relevant outcomes. In-person internship opportunities are less in number and have major challenges in scaling such as lesser number of available projects in relevant technologies, inadequate mentorship during the internship, varying college calendars and others. This paper presents SRIP (Student Remote Internship Program) approach which focuses on the domain of programming in open source technologies and projects for the internships that are relevant to 2nd and 3rd year engineering college students. It aims to overcome the stated challenges and simultaneously make a contribution to the open source community. Virtual Labs is the open source repository that we leveraged to implement SRIP pilot study for interns to contribute and develop programming skills.

Abstract

Due to the advent of deep sub-micron technologies, statistical (in addition to temperature and supply voltage) variations aware estimation of leakages power has become prominent. Also, estimation of leakage currents at SPICE level guarantees the most accurate results, however not feasible means in high complexity ICs. This performs adversely for Monte-Carlo iterations for statistical analysis. In this paper we introduced an accurate machine learning technique to model statistical and operating variation aware estimation from Artificial Neural Network and regression based Multivariate Polynomial Regression which exhibits innately faster computation and attained error less than 1% for the targeted 16nm FinFET technology node although model is black box for any technology. The accuracy of the proposed technique has been tested over several basic cells and estimation of the complex circuits have been carried out utilizing the pre-modelled basic cells.

Abstract

The formidable increase in the number of smaller and smarter embedded devices has compelled programmers to develop more and more specialized application programs for these systems. These resource intensive programs that have to be executed on limited memory systems make a strong case for compiler optimizations that reduce the executable size of programs. Standard compilers (like LLVM) offer an out-of-the-box -Oz optimization option—just a series of compiler optimization passes—that is specifically targeted for the reduction of the generated executable size. In this paper, we aim to analyze the effects of optimizations of LLVM compiler on the reduction of executable size. Specifically, we take the size of the executable as a metric and attempt to divide the -Oz series into logical groups and study their individual effects; while also study the effect of their combinations. Our preliminary study over SPEC CPU 2017 benchmarks gives us an insight into the comparative effect of the groups of passes on the executable size. Our work has potential to enable the user to tailor a custom series of passes so as to obtain the desired executable size

Abstract

In a distributed network, we consider two special nodes called the sender S and the receiver R that are connected by n node-disjoint (except for S and R) bi-directional wires. Out of these n wires, the adversary can control at most t wires (of its choice) in Byzantine fashion. In this setting, our goal is to design a message transmission protocol Π that assures the following two conditions hold: (1) by the end of the protocol Π, R gets the correct message m transmitted by S without any error (perfect reliability), and (2) the adversary learns no information aboutm, whatsoever, in information theoretic sense (perfect secrecy). Protocols that satisfy these two conditions are known as the Perfectly Secure Message Transmission (PSMT) protocols.

Abstract

The combination of two overlapping technologies (bigdata and cloud computing) helps easy access to the evolving applications. In this context, there is a serious requirement of ensuring the transmission of data securely in order to improve the productivity over the public channel. Since the data collected by various sources are strictly private and confidential, there is also a great requirement to deal with the privacy preservation of the bigdata. To handle this issue, a new privacy-preserving bigdata collection technique in cloud computing environment, called 2PBDC, has been designed, which allows secure communication between the bigdata gateway nodes and the cloud servers. 2PBDC is shown to be secure against various known attacks against an active/passive adversary through the formal security verification as well as informal security analysis. A detailed comparative study among 2PBDC and other existing schemes has been conducted. This study shows that 2PBDC offers a better trade-off among the security and functionality features and communication and computation overheads while these are compared with other schemes.

Abstract

Optical sensing for chemical analysis is emerging as it provides advantages such as good sensitivity, selectivity, electromagnetic immunity, etc. This work presents a low-cost, robust and easy to use technique for measurement of bulk water property changes, specifically pH, total dissolved solids (TDS), and turbidity. The designed multi-wavelength sensing mechanism is capable of measuring the absorption of light emitted by three different LEDs after passing through water. The optical responses obtained using this mechanism are then related to parameter changes of water for quality measurement. The results show that measurements for pH, TDS, and turbidity have a linear regression coefficient of 0.9691, 0.9729 and 0.76 respectively. By utilizing narrowband light sources of characteristic wavelengths for the target parameters, a compact and portable device can be designed for rapid measurements. This can work …