Carlos A. Varela

Programming dynamically reconfigurable open systems with SALSA

Applications running on the Internet, or on limited-resource devices, need to be able to adapt to changes in their execution environment at run-time. Current languages and systems fall short of enabling developers to migrate and reconfigure application sub-components at program-execution time.In this paper, we describe essential aspects of the design and implementation of SALSA, an actor-based language for mobile and Internet computing. SALSA simplifies programming dynamically reconfigurable, open applications by providing universal names, active objects, and migration. Moreover, SALSA introduces three language mechanisms to help programmers coordinate asynchronous, mobile computations: token-passing continuations, join continuations and first-class continuations.We provide some examples which illustrate how SALSA programs are not only dynamically reconfigurable and open, but also much more concise and easier to follow than comparable Java code. Furthermore, we provide empirical results which show SALSAs performance to be better than Java code using an actor library, and which illustrate the difference between local, local area, and wide area communication and migration. Finally, we discuss the implementation of our preprocessor which translates SALSA code into Java.

Transactors: a programming model for maintaining globally consistent distributed state in unreliable environments

We introduce transactors, a fault-tolerant programming model for composing loosely-coupled distributed components running in an unreliable environment such as the internet into systems that reliably maintain globally consistent distributed state. The transactor model incorporates certain elements of traditional transaction processing, but allows these elements to be composed in different ways without the need for central coordination, thus facilitating the study of distributed fault-tolerance from a semantic point of view. We formalize our approach via the τ-calculus, an extended lambda-calculus based on the actor model, and illustrate its usage through a number of examples. The τ-calculus incorporates constructs which distributed processes can use to create globally-consistent checkpoints. We provide an operational semantics for the τ-calculus, and formalize the following safety and liveness properties: first, we show that globally-consistent checkpoints have equivalent execution traces without any node failures or application-level failures, and second, we show that it is possible to reach globally-consistent checkpoints provided that there is some bounded failure-free interval during which checkpointing can occur.

The Internet Operating System: Middleware for Adaptive Distributed Computing

Large-scale, dynamic, and heterogeneous networks of computational resources (a.k.a. grids) promise to provide high performance and scalability to computationally intesive applications. To fulfill this promise, grid environments require complex resource management. We propose decetralized middleware-triggered dynamic reconfiguration straegies to enable application adaptation to the constantly changing resource availability of Internet-scale shared coputational grids. As a proof of concept, we present a sofware framework for dynamically reconfigurable distributed applications. The Internet Operating System (IOS) is a middleware infrastructure which aims at freeing appliction developers from dealing with non-functional concerns while seeking to optimize application performance and glbal resource utilization. IOS consists of distributed middlware agents that are capable of interconnecting themselves in various virtual peer-to-peer topologies. IOS middleware agents: 1) profile application communication patterns; 2) evaluate the dynamics of the underlying physical resources; and 3) reconfigure application components by changing their mappings to physical resources through migration and by changing their granularity through a split and merge mechanism. A key characteristic of IOS is its decentralized coordination, thereby avoiding the use of global knowledge and thus enabling scalable reconfiguration. The IOS middlware is programming model-independent: we have implmented an actor programming model interface for SALSA programs and also a process programming model interface for MPI programs. Experimental results show that adaptive middleware can be an effective approach to reconfiguring distributed applications with various ratios of communiction to computation in order to improve their performance, and more effectively utilize grid resources.

Dynamic Malleability in Iterative MPI Applications

Malleability enables a parallel applications execution system to split or merge processes modifying granularity. While process migration is widely used to adapt applications to dynamic execution environments, it is limited by the granularity of the applications processes. Malleability empowers process migration by allowing the applications processes to expand or shrink following the availability of resources. We have implemented malleability as an extension to the PCM (process checkpointing and migration) library, a user-level library for iterative MPI applications. PCM is integrated with the Internet operating system (IOS), a framework for middleware-driven dynamic application reconfiguration. Our approach requires minimal code modifications and enables transparent middleware- triggered reconfiguration. Experimental results using a two-dimensional data parallel program that has a regular communication structure demonstrate the usefulness of malleability.

An Architecture for Reconfigurable Iterative MPI Applications in Dynamic Environments

With the proliferation of large scale dynamic execution environments such as grids, the need for providing efficient and scalable application adaptation strategies for long running parallel and distributed applications has emerged. Message passing interfaces have been initially designed with a traditional machine model in mind which assumes homogeneous and static environments. It is inevitable that long running message passing applications will require support for dynamic reconfiguration to maintain high performance under varying load conditions. In this paper we describe a framework that provides iterative MPI applications with reconfiguration capabilities. Our approach is based on integrating MPI applications with a middleware that supports process migration and large scale distributed application reconfiguration. We present our architecture for reconfiguring MPI applications, and verify our design with a heat diffusion application in a dynamic setting.

Elastic Scalable Cloud Computing Using Application-Level Migration

We present the Cloud Operating System (COS), a middleware framework to support autonomous workload elasticity and scalability based on application-level migration as a reconfiguration strategy. While other scalable frameworks (e.g., MapReduce or Google App Engine) force application developers to write programs following specific APIs, COS provides scalability in a general-purpose programming framework based on an actor-oriented programming language. When all executing VMs are highly utilized, COS scales a workload up by migrating mobile actors over to newly dynamically created VMs. When VM utilization drops, COS scales the workload down by consolidating actors and terminating idle VMs. Application-level migration is advantageous compared to VM migration especially in hybrid clouds in which migration costs over the Internet are critical to scale out the workloads. We demonstrate the general purpose programming approach using a tightly-coupled computation. We compare the performance of autonomous (i.e., COS-driven) versus ideal reconfiguration, as well as the impact of granularity of reconfiguration, i.e., VM migration versus application-level migration. Our results show promise for future fully automated cloud computing resource management systems that efficiently enable truly elastic and scalable general-purpose workloads.

An analysis of massively distributed evolutionary algorithms

Computational science is placing new demands on optimization algorithms as the size of data sets and the computational complexity of scientific models continue to increase. As these complex models have many local minima, evolutionary algorithms (EAs) are very useful for quickly finding optimal solutions in these challenging search spaces. In addition to the complex search spaces involved, calculating the objective function can be extremely demanding computationally. Because of this, distributed computation is a necessity. In order to address these computational demands, top-end distributed computing systems are surpassing hundreds of thousands of computing hosts; and as in the case of Internet based volunteer computing systems, they can also be highly heterogeneous and faulty. This work examines asynchronous strategies for distributed EAs using simulated computing environments. Results show that asynchronous EAs can scale to hundreds of thousands of computing hosts while being highly resilient to heterogeneous and faulty computing environments, something not possible for traditional distributed EAs which require synchronization. While the simulation not only provides insight as to how asynchronous EAs perform on distributed computing environments with different latencies and heterogeneity, it also serves as a sanity check because live distributed systems require problems with high computation to communication ratios and traditional benchmark problems cannot be used for meaningful analysis due to their short computation times.

Maximum Likelihood Fitting of Tidal Streams With Application to the Sagittarius Dwarf Tidal Tails

We present a maximum likelihood method for determining the spatial properties of tidal debris and of the Galactic spheroid. With this method we characterize Sagittarius debris using stars with the colors of blue F turnoff stars in SDSS stripe 82. The debris is located at (α, δ, R) = (31.37 ◦ ± 0.26 ◦ ,0.0,29.22± 0.20 kpc), with a (spatial) direction given by the unit vector , in Galactocentric Cartesian coordinates, and with FWHM = 6.74± 0.06 kpc. This 2.5 ◦ -wide stripe contains 0.892% as many F turnoff stars as the current Sagittarius dwarf galaxy. Over small spatial extent, the debris is modeled as a cylinder with a density that falls off as a Gaussian with distance from the axis, while the smooth component of the spheroid is modeled with a Hernquist profile. We assume that the absolute magnitude of F turnoff stars is distributed as a Gaussian, which is an improvement over previous methods which fixed the absolute magnitude at ¯ Mg0 = 4.2. The effectiveness and correctness of the algorithm is demonstrated on a simulated set of F turnoff stars created to mimic SDSS stripe 82 data, which shows that we have a much greater accuracy than previous studies. Our algorithm can be applied to divide the stellar data into two catalogs: one which fits the stream density profile and one with the characteristics of the spheroid. This allows us to effectively separate tidal debris from the spheroid population, both facilitating the study of the tidal stream dynamics and providing a test of whether a smooth spheroidal population exists.

Malleable applications for scalable high performance computing

Abstract Iterative applications are known to run as slow as their slowest computational component. This paper introduces malleability, a new dynamic reconfiguration strategy to overcome this limitation. Malleability is the ability to dynamically change the data size and number of computational entities in an application. Malleability can be used by middleware to autonomously reconfigure an application in response to dynamic changes in resource availability in an architecture-aware manner, allowing applications to optimize the use of multiple processors and diverse memory hierarchies in heterogeneous environments. The modular Internet Operating System (IOS) was extended to reconfigure applications autonomously using malleability. Two different iterative applications were made malleable. The first is used in astronomical modeling, and representative of maximum-likelihood applications was made malleable in the SALSA programming language. The second models the diffusion of heat over a two dimensional object, and is representative of applications such as partial differential equations and some types of distributed simulations. Versions of the heat application were made malleable both in SALSA and MPI. Algorithms for concurrent data redistribution are given for each type of application. Results show that using malleability for reconfiguration is 10 to 100 times faster on the tested environments. The algorithms are also shown to be highly scalable with respect to the quantity of data involved. While previous work has shown the utility of dynamically reconfigurable applications using only computational component migration, malleability is shown to provide up to a 15% speedup over component migration alone on a dynamic cluster environment. This work is part of an ongoing research effort to enable applications to be highly reconfigurable and autonomously modifiable by middleware in order to efficiently utilize distributed environments. Grid computing environments are becoming increasingly heterogeneous and dynamic, placing new demands on applications’ adaptive behavior. This work shows that malleability is a key aspect in enabling effective dynamic reconfiguration of iterative applications in these environments.

A Hierarchical Model for Coordination of Concurrent Activities

We describe a hierarchical model for coordination of concurrent activities based on grouping actors into casts and coordinating casts by actors that are designated directors. The hierarchical model provides a simple, intuitive basis for actor communication and coordination. Casts serve as abstraction units for naming, migration, synchronization and load balancing. Messengers are actors used to send messages with special behaviour across casts. Moreover, an implementation of the hierarchical model does not require a reflective run-time architecture. We present the operational semantics for our model and illustrate the model by two sample applications: an atomic multicast protocol and a messenger carrying remote exception-handling code. These applications have been implemented in Java, leveraging the existence of cross-platform, safe virtual machine implementations.