Interference in Multiprocessor Systems with Localized Memory Access Probabilities

Past studies of memory interference in multiprocessor systems have generally assumed that the references of each processor are uniformly distributed among the memory modules. In this paper we develop a model with local referencing, which reflects more closely the behavior of real-life programs. This model is analyzed using Markov chain techniques and expressions are derived for the multiprocessor performance. New expressions are also obtained for the performance in the traditional uniform reference model and are compared with other expressions-available in the literature. Results of a simulation study are given to show the accuracy of the expressions for both models.

Variable Granularity Access Tracking Scheme for Improving the Performance of Software Transactional Memory

Software transactional memory (STM) has been proposed as a promising programming paradigm for shared memory multi-threaded programs as an alternative to conventional lock based synchronization primitives. Typical STM implementations employ a conflict detection scheme, which works with uniform access granularity, tracking shared data accesses either at word/cache line or at object level. It is well known that a single fixed access tracking granularity cannot meet the conflicting goals of reducing false conflicts without impacting concurrency adversely. A fine grained granularity while improving concurrency can have an adverse impact on performance due to lock aliasing, lock validation overheads, and additional cache pressure. On the other hand, a coarse grained granularity can impact performance due to reduced concurrency. Thus, in general, a fixed or uniform granularity access tracking (UGAT) scheme is application-unaware and rarely matches the access patterns of individual application or parts of an application, leading to sub-optimal performance for different parts of the application(s). In order to mitigate the disadvantages associated with UGAT scheme, we propose a Variable Granularity Access Tracking (VGAT) scheme in this paper. We propose a compiler based approach wherein the compiler uses inter-procedural whole program static analysis to select the access tracking granularity for different shared data structures of the application based on the application's data access pattern. We describe our prototype VGAT scheme, using TL2 as our STM implementation. Our experimental results reveal that VGAT-STM scheme can improve the application performance of STAMP benchmarks from 1.87% to up to 21.2%.

Federated Search Service for OAI-compliant, Open-Access Repositories in India

Many of the research institutions and universities across the world are facilitating open-access (OA) to their intellectual outputs through their respective OA institutional repositories (IRs) or through the centralized subject-based repositories. The registry of open access repositories (ROAR) lists more than 2850 such repositories across the world. The awareness about the benefits of OA to scholarly literature and OA publishing is picking up in India, too. As per the ROAR statistics, to date, there are more than 90 OA repositories in the country. India is doing particularly well in publishing open-access journals (OAJ). As per the directory of open-access journals (DOAJ), to date, India with 390 OAJs, is ranked 5th in the world in terms of numbers of OAJs being published. Much of the research done in India is reported in the journals published from India. These journals have limited readership and many of them are not being indexed by Web of Science, Scopus or other leading international abstracting and indexing databases. Consequently, research done in the country gets hidden not only from the fellow countrymen, but also from the international community. This situation can be easily overcome if all the researchers facilitate OA to their publications. One of the easiest ways to facilitate OA to scientific literature is through the institutional repositories. If every research institution and university in India set up an open-access IR and ensure that copies of the final accepted versions of all the research publications are uploaded in the IRs, then the research done in India will get far better visibility. The federation of metadata from all the distributed, interoperable OA repositories in the country will serve as a window to the research done across the country. Federation of metadata from the distributed OAI-compliant repositories can be easily achieved by setting up harvesting software like the PKP Harvester. In this paper, we share our experience in setting up a prototype metadata harvesting service using the PKP harvesting software for the OAI-compliant repositories in India.

Modern energy access to all in rural India: An integrated implementation strategy

Expanding energy access to the rural population of India presents a critical challenge for its government. The presence of 364 million people without access to electricity and 726 million who rely on biomass for cooking indicate both the failure of past policies and programs, and the need for a radical redesign of the current system. We propose an integrated implementation framework with recommendations for adopting business principles with innovative institutional, regulatory, financing and delivery mechanisms. The framework entails establishment of rural energy access authorities and energy access funds, both at the national and regional levels, to be empowered with enabling regulatory policies, capital resources and the support of multi-stakeholder partnership. These institutions are expected to design, lead, manage and monitor the rural energy interventions. At the other end, trained entrepreneurs would be expected to establish bioenergy-based micro-enterprises that will produce and distribute energy carriers to rural households at an affordable cost. The ESCOs will function as intermediaries between these enterprises and the international carbon market both in aggregating carbon credits and in trading them under CDM. If implemented, such a program could address the challenges of rural energy empowerment by creating access to modern energy carriers and climate change mitigation. (C) 2011 Elsevier Ltd. All rights reserved.

Network pricing for QoS: A 'Regulation' approach

V. S. Borkar’s work was supported in part by grant number III.5(157)/99-ET from the Department of Science and Technology, Government of India. D. Manjunath’s work was supported in part by grant number 1(1)/2004-E-Infra from the Ministry of Information Technology, Government of India.

Reinforcement learning applications in dynamic pricing of retail markets

In this paper, we investigate the use of reinforcement learning (RL) techniques to the problem of determining dynamic prices in an electronic retail market. As representative models, we consider a single seller market and a two seller market, and formulate the dynamic pricing problem in a setting that easily generalizes to markets with more than two sellers. We first formulate the single seller dynamic pricing problem in the RL framework and solve the problem using the Q-learning algorithm through simulation. Next we model the two seller dynamic pricing problem as a Markovian game and formulate the problem in the RL framework. We solve this problem using actor-critic algorithms through simulation. We believe our approach to solving these problems is a promising way of setting dynamic prices in multi-agent environments. We illustrate the methodology with two illustrative examples of typical retail markets.

Optimal multi-layered congestion based pricing schemes for enhanced QoS

Pricing is an effective tool to control congestion and achieve quality of service (QoS) provisioning for multiple differentiated levels of service. In this paper, we consider the problem of pricing for congestion control in the case of a network of nodes with multiple queues and multiple grades of service. We present a closed-loop multi-layered pricing scheme and propose an algorithm for finding the optimal state dependent price levels for individual queues, at each node. This is different from most adaptive pricing schemes in the literature that do not obtain a closed-loop state dependent pricing policy. The method that we propose finds optimal price levels that are functions of the queue lengths at individual queues. Further, we also propose a variant of the above scheme that assigns prices to incoming packets at each node according to a weighted average queue length at that node. This is done to reduce frequent price variations and is in the spirit of the random early detection (RED) mechanism used in TCP/IP networks. We observe in our numerical results a considerable improvement in performance using both of our schemes over that of a recently proposed related scheme in terms of both throughput and delay performance. In particular, our first scheme exhibits a throughput improvement in the range of 67-82% among all routes over the above scheme. (C) 2011 Elsevier B.V. All rights reserved.

Pricing Defaultable Bonds in a Markov Modulated Market

We address the problem of pricing defaultable bonds in a Markov modulated market. Using Merton's structural approach we show that various types of defaultable bonds are combination of European type contingent claims. Thus pricing a defaultable bond is tantamount to pricing a contingent claim in a Markov modulated market. Since the market is incomplete, we use the method of quadratic hedging and minimal martingale measure to derive locally risk minimizing derivative prices, hedging strategies and the corresponding residual risks. The price of defaultable bonds are obtained as solutions to a system of PDEs with weak coupling subject to appropriate terminal and boundary conditions. We solve the system of PDEs numerically and carry out a numerical investigation for the defaultable bond prices. We compare their credit spreads with some of the existing models. We observe higher spreads in the Markov modulated market. We show how business cycles can be easily incorporated in the proposed framework. We demonstrate the impact on spreads of the inclusion of rare states that attempt to capture a tight liquidity situation. These states are characterized by low risk-free interest rate, high payout rate and high volatility.

Pricing credit derivatives

The financial crisis set off by the default of Lehman Brothers in 2008 leading to disastrous consequences for the global economy has focused attention on regulation and pricing issues related to credit derivatives. Credit risk refers to the potential losses that can arise due to the changes in the credit quality of financial instruments. These changes could be due to changes in the ratings, market price (spread) or default on contractual obligations. Credit derivatives are financial instruments designed to mitigate the adverse impact that may arise due to credit risks. However, they also allow the investors to take up purely speculative positions. In this article we provide a succinct introduction to the notions of credit risk, the credit derivatives market and describe some of the important credit derivative products. There are two approaches to pricing credit derivatives, namely the structural and the reduced form or intensity-based models. A crucial aspect of the modelling that we touch upon briefly in this article is the problem of calibration of these models. We hope to convey through this article the challenges that are inherent in credit risk modelling, the elegant mathematics and concepts that underlie some of the models and the importance of understanding the limitations of the models.

Method of designing an access mechanism for social networks

The rapid development of communication and networking has lessened geographical boundaries among actors in social networks. In social networks, actors often want to access databases depending upon their access rights, privacy, context, privileges, etc. Managing and handling knowledge based access of actors is complex and hard for which broad range of technologies need to be called. Access based on dynamic access rights and circumstances of actors impose major tasks on access systems. In this paper, we present an Access Mechanism for Social Networks (AMSN) to render access to actors over databases taking privacy and status of actors into consideration. The designed AMSN model is tested over an Agriculture Social Network (ASN) which utilises distinct access rights and privileges of actors related to the agriculture occupation, and provides access to actors over databases.

Physical Layer Network Coding for the K-User Multiple Access Relay Channel

We propose a Physical layer Network Coding (PNC) scheme for the K-user wireless Multiple Access Relay Channel, in which K source nodes want to transmit messages to a destination node D with the help of a relay node R. The proposed scheme involves (i) Phase 1 during which the source nodes alone transmit and (ii) Phase 2 during which the source nodes and the relay node transmit. At the end of Phase 1, the relay node decodes the messages of the source nodes and during Phase 2 transmits a many-to-one function of the decoded messages. To counter the error propagation from the relay node, we propose a novel decoder which takes into account the possibility of error events at R. It is shown that if certain parameters are chosen properly and if the network coding map used at R forms a Latin Hypercube, the proposed decoder offers the maximum diversity order of two. Also, it is shown that for a proper choice of the parameters, the proposed decoder admits fast decoding, with the same decoding complexity order as that of the reference scheme based on Complex Field Network Coding (CFNC). Simulation results indicate that the proposed PNC scheme offers a large gain over the CFNC scheme.

Achieving universal rural energy access in india: A low carbon pathway

India's energy challenges are three pronged: presence of majority energy poor lacking access to modern energy; need for expanding energy system to bridge this access gap as well as to meet the requirements of fast-growing economy; and the desire to partner with global economies in mitigating the threat of climate change. The presence of 364 million people without access to electricity and 726 million relying on biomass for cooking out of a total rural population of 809 million indicate the seriousness of challenge. In this paper, we discuss an innovative approach to address this challenge, which intends to take advantage of recent global developments and untapped capabilities possessed by India. Intention is to use climate change mitigation imperative as a stimulus and adopt a public-private-partnership-driven ‘business model' with innovative institutional, regulatory, financing, and delivery mechanisms. Some of the innovations are: creation of rural energy access authorities within the government system as leadership institutions; establishment of energy access funds to enable transitions from the regime of "investment/fuel subsidies" to "incentive-linked" delivery of energy services; integration of business principles to facilitate affordable and equitable energy sales and carbon trade; and treatment of entrepreneurs as implementation targets. This proposal targets 100% access to modern energy carriers by 2030 through a judicious mix of conventional and biomass energy systems with an investment of US$35 billion over 20 years. The estimated annual cost of universal energy access is about US$9 billion for a GHG mitigation potential of 213Tg CO2e at an abatement cost of US$41/tCO2e. It is a win-win situation for all stakeholders. Households benefit from modern energy carriers at affordable cost; entrepreneurs run profitable energy enterprises; carbon markets have access to CERs; the government has the satisfaction of securing energy access to rural people; and globally, there is a benefit of climate change mitigation.

A simulation-based algorithm for optimal pricing policy under demand uncertainty

We propose a simulation-based algorithm for computing the optimal pricing policy for a product under uncertain demand dynamics. We consider a parameterized stochastic differential equation (SDE) model for the uncertain demand dynamics of the product over the planning horizon. In particular, we consider a dynamic model that is an extension of the Bass model. The performance of our algorithm is compared to that of a myopic pricing policy and is shown to give better results. Two significant advantages with our algorithm are as follows: (a) it does not require information on the system model parameters if the SDE system state is known via either a simulation device or real data, and (b) as it works efficiently even for high-dimensional parameters, it uses the efficient smoothed functional gradient estimator.