Randomized Assignment of Jobs to Servers in Heterogeneous Clusters of Shared Servers for Low Delay

We consider the job assignment problem in a multi-server system consisting of $N$ parallel processor sharing servers, categorized into $M$ ($\ll N$) different types according to their processing capacity or speed. Jobs of random sizes arrive at the system according to a Poisson process with rate $N \lambda$. Upon each arrival, a small number of servers from each type is sampled uniformly at random. The job is then assigned to one of the sampled servers based on a selection rule. We propose two schemes, each corresponding to a specific selection rule that aims at reducing the mean sojourn time of jobs in the system. We first show that both methods achieve the maximal stability region. We then analyze the system operating under the proposed schemes as $N \to \infty$ which corresponds to the mean field. Our results show that asymptotic independence among servers holds even when $M$ is finite and exchangeability holds only within servers of the same type. We further establish the existence and uniqueness of stationary solution of the mean field and show that the tail distribution of server occupancy decays doubly exponentially for each server type. When the estimates of arrival rates are not available, the proposed schemes offer simpler alternatives to achieving lower mean sojourn time of jobs, as shown by our numerical studies

A study of Indian print exports to the United States

Offshoring is the reality of globalization. According to a survey conducted by trendstowatchgraphics.com, the percentage of American print providers who are worried about US print buyers offshoring their print requirements increased from 1% in 1995 to 7% in 2005. According to a survey conducted by the Graphics Arts Technical Foundation, 40% of the printers in the US think that their current customers are also seeking out offshore printers for their printing needs. According to UN Comtrade, exports of printed matter to the US from China in 2005 amounted to $722 million, and exports of printed matter to the US from India during the same period amounted to$52 million (The market for overseas print providers, 2006). China is currently the preferred destination for US print buyers for offshoring their print requirements. But India, having invested heavily in education, is likely to see phenomenal growth in the upcoming years. While China is well-known for manufacturing, India has grown in the IT sector. India is also developing its infrastructure to enhance growth in the manufacturing sector. According to Mr. Regis Delmontagne, former president of the Association for Suppliers of Printing, Publishing, and Converting Technologies (NPES): xii India, today, is not merely a target market for products from outside its borders . . . and not just a place foreign customers will turn for less expensive printing. It is also a source of new products, new technologies, and new ideas. . . . The United States remains India’s largest trading partner, providing both a market for Indian goods and services and a dependable flow of the latest technologies to help India continue its competitive emergence. (as cited in Association for Suppliers of Printing, Publishing, and Converting Technologies, 2006) This thesis reports on the results of two surveys: one sent to book publishers in the US and the other sent to Indian print service providers. The main results of the thesis are as follows: • Turnaround times and quality concerns are the biggest barriers prohibiting the growth of Indian printers in the US print buying market. • The US book publishers are not aware of the manufacturing capabilities of the Indian printers. They are more prone towards sending their pre-media requirements to India • Confidentiality, level of technology and infrastructure, and range of services offered by the Indian print service providers are the three main criteria by the US book publishers while selecting Indian print service providers. • There is definitely an opportunity for the Indian print service providers in the US print buying market if they can create brand name, pay more attention to quality, establish a common medium for communication, and plan and schedule accurately

Context-Aware Software Builds

Software libraries are of the static or dynamic variety. In a static library, code from the library is integrated into the executable at compile time. The resulting executable is relatively large but runs fast and in a stand-alone manner. In a dynamic library, code from the library is linked to the executable at run-time. The executable is smaller, and due to the sharing of dynamic libraries across processes, has less memory overhead. However, running the executable is contingent on the presence of the dynamic library in the machine that it runs on. Linking a library at run-time can also cause loss in speed. This disclosure presents machine-learning based techniques to optimally identify a build target as a shared or static library. A recommendation is made to the software developer regarding an optimal setting (dynamic or static) for compilation. The techniques enable a developer to make informed design decisions

Detection of Deadlocks and Race Conditions in Computing Systems

A race condition is a phenomenon wherein the output of an electronic device or computer process (thread) depends on the relative timing of events outside the control of the process or device. A deadlock is a state in which multiple computing processes that share a common resource are stalled due to the processes mutually locking each other out of access to the resource. Deadlocks and race conditions are difficult bugs to detect, or even reproduce for debugging. This disclosure presents techniques that detect deadlocks and race conditions using machine-learning models to analyze the control flow graph of a program. Predictions of potential race or deadlock conditions are accompanied by justifications, e.g., potential scenarios that cause a race conditions or deadlock to arise. The classifying and generalizing abilities of machinelearning models are applied such that these difficult to detect bugs are caught at design stage, most advantageously for large code bases

Automatic Denormalization of Databases

Normalization is a technique in databases to eliminate data redundancy or inconsistent dependency. Normalizing is achieved by dividing larger tables of a database into smaller ones and defining relationships between them. Normalization can yield performance gains, such as improved response times, but only to an extent. Highly normalized databases are not performance optimal. Optimal database performance is often obtained at a sweet spot between optimizing sizes of individual tables and the number of tables. A highly normalized database is therefore often denormalized to improve performance. Traditionally, denormalization is driven by user or developer intuition. This disclosure describes a machine-learning model to optimally denormalize a database based on, e.g., typical database queries, frequency of queries, response-times, projections of response time upon denormalization, etc. The techniques result in an optimal normal form of the database, in turn resulting in superior data integrity and performance

Distributed Placement of Machine-Learning Computing in an Edge Network

Smart devices continue to proliferate as the Internet-of-Things expands. Collectively, Internet-of-Things devices generate massive amounts of data for processing, analysis, and implementation. However, most individual smart devices lack sufficient hardware resources to process collected data in an efficient or timely manner. Thus, most devices send their data to a remote server or other cloud-based computing system for processing because of the increased computational capacities of such remote locations. Although these remote locations can process the data faster and more efficiently, the increase in the number of smart devices accessing the remote locations increases the transmission traffic, and associated bottlenecks, on networks and other data-transmission systems. Many smart devices reside on local networks that feature other, more-powerful, computing devices, such as desktops, laptops, home servers, and gaming systems. Some of these additional computing devices could be tasked with processing data and other information for Internet-of-Things devices that lack sufficient computational capacity to process the data themselves

Transfer Inference Learning

Techniques are described that combine machine learning with an edge network that includes IoT devices to yield an effective and efficient method of assessing a condition of an environment. An inference module that includes a machine-learning algorithm, installed and executing on the IoT devices, assesses a condition detected from multiple, different geographic locations. The IoT devices transfer sets of data and inferences as well as respective sets of confidence levels to converge on a verified set of inferences. The verified set of inferences is arrived at quickly and with a high confidence level

Choosing among heterogeneous server clouds

This paper considers a model of interest in cloud computing applications. We consider a multiserver system consisting of N heterogeneous servers. The servers are categorized into M( ≪N ) different types according to their service capabilities. Jobs having specific resource requirements arrive at the system according to a Poisson process with rate Nλ . Upon each arrival, a small number of servers are sampled uniformly at random from each server type. The job is then routed to the sampled server with maximum vacancy per server capacity. If a job cannot obtain the required amount of resources from the server to which it is assigned, then the job is discarded. We analyze the system in the limit as N→∞ . This gives rise to a mean field, which we show has a unique fixed point and is globally attractive. Furthermore, as N→∞ , the servers behave independently. The stationary tail probabilities of server occupancies are obtained from the stationary solution of the mean field. Numerical results suggest that the proposed scheme significantly reduces the average blocking probability compared to static schemes that probabilistically route jobs to servers in proportion to the number of servers of each type. Moreover, the reduction in blocking holds even for systems at high load. For the limiting system in statistical equilibrium, our simulation results indicate that the occupancy distribution is insensitive to the holding time distribution and only depends on its mean

Mean field and propagation of chaos in multi-class heterogeneous loss models

We consider a system consisting of parallel servers, where jobs with different resource requirements arrive and are assigned to the servers for processing. Each server has a finite resource capacity and therefore can serve only a finite number of jobs at a time. We assume that different servers have different resource capacities. A job is accepted for processing only if the resource requested by the job is available at the server to which it is assigned. Otherwise, the job is discarded or blocked. We consider randomized schemes to assign jobs to servers with the aim of reducing the average blocking probability of jobs in the system. In particular, we consider a scheme that assigns an incoming job to the server having maximum available vacancy or unused resource among randomly sampled servers. We consider the system in the limit where both the number of servers and the arrival rates of jobs are scaled by a large factor. This gives rise to a mean field analysis. We show that in the limiting system the servers behave independently—a property termed as propagation of chaos. Stationary tail probabilities of server occupancies are obtained from the stationary solution of the mean field which is shown to be unique and globally attractive. We further characterize the rate of decay of the stationary tail probabilities. Numerical results suggest that the proposed scheme significantly reduces the average blocking probability of jobs as compared to static schemes that probabilistically route jobs to servers independently of their states

Mean field and propagation of chaos in multi-class heterogeneous loss models

We consider a system consisting of parallel servers, where jobs with different resource requirements arrive and are assigned to the servers for processing. Each server has a finite resource capacity and therefore can serve only a finite number of jobs at a time. We assume that different servers have different resource capacities. A job is accepted for processing only if the resource requested by the job is available at the server to which it is assigned. Otherwise, the job is discarded or blocked. We consider randomized schemes to assign jobs to servers with the aim of reducing the average blocking probability of jobs in the system. In particular, we consider a scheme that assigns an incoming job to the server having maximum available vacancy or unused resource among randomly sampled servers. We consider the system in the limit where both the number of servers and the arrival rates of jobs are scaled by a large factor. This gives rise to a mean field analysis. We show that in the limiting system the servers behave independently—a property termed as propagation of chaos. Stationary tail probabilities of server occupancies are obtained from the stationary solution of the mean field which is shown to be unique and globally attractive. We further characterize the rate of decay of the stationary tail probabilities. Numerical results suggest that the proposed scheme significantly reduces the average blocking probability of jobs as compared to static schemes that probabilistically route jobs to servers independently of their states