This paper describes the design criteria and implementation details of a dynamic storage allocator for real-time systems. The main requirements that have to be considered when designing a new allocator are concerned with temporal and spatial constraints. The proposed algorithm, called TLSF (two-level segregated fit), has an asymptotic constant cost, O(1), maintaining a fast response time (less than 200 processor instructions on a x86 processor) and a low level of memory usage (low fragmentation). TLSF uses two levels of segregated lists to arrange free memory blocks and an incomplete search policy. This policy is implemented with word-size bitmaps and logical processor instructions. Therefore, TLSF can be categorized as a good-fit allocator. The incomplete search policy is shown also to be a good policy in terms of fragmentation. The fragmentation caused by TLSF is slightly smaller (better) than that caused by best fit (which is one of the best allocators regarding memory fragmentation). In order to evaluate the proposed allocator, three analyses are presented in this paper. The first one is based on worst-case scenarios. The second one provides a detailed consideration of the execution cost of the internal operations of the allocator and its fragmentation. The third analysis is a comparison with other well-known allocators from the temporal (number of cycles and processor instructions) and spatial (fragmentation) points of view. In order to compare them, a task model has been presented. Copyright © 2007 John Wiley & Sons, Ltd.

/pdf/implementation-of-a-constant-time-dynamic-storage-allocator-1mbwo1hcax.pdf

Implementation of a constant-time dynamic storage allocator

Cloud computing augments applications with ease-of-access to the enormous resources on the Internet. Combined with mobile computing technologies, mobile applications can exploit the Cloud everywhere by statically distributing code segments or dynamically migrating running processes onto cloud services. Existing migration techniques are however too coarse-grained for mobile devices, so the overheads often offset the benefits of migration. To build a truly elastic mobile cloud computing infrastructure, we introduce eXCloud (eXtensible Cloud) — a middleware system with multi-level mobility support, ranging from as coarse as a VM instance to as fine as a runtime stack frame, and allows resources to be integrated and used dynamically. In eXCloud, a stack-on-demand (SOD) approach is used to support computation mobility throughout the mobile cloud environment. The approach is fully adaptive, goal-driven and transparent. By downward task migration, applications running on the cloud nodes can exploit or take control of special resources in mobile devices such as GPS and cameras. With a restorable MPI layer, task migrations of MPI parallel programs can happen between cloud nodes or be initiated from a mobile device. Our evaluation shows that SOD outperforms several existing migration mechanisms in terms of migration overhead and latency. All our techniques result in better resource utilization through task migrations among cloud nodes and mobile nodes.

/pdf/excloud-transparent-runtime-support-for-scaling-mobile-2v3oi2ajeb.pdf

eXCloud: Transparent runtime support for scaling mobile applications in cloud

Mobile cloud computing allows mobile applications to use the enormous resources in the clouds. In order to seamlessly utilize the resources, it is common to migrate computation among mobile nodes and cloud nodes. Therefore, a highly portable and transparent migration approach is needed. In terms of portability, application-level migration with code instrumentation is the most portable approach. However, in the existing literature, this approach imposes significant runtime overhead, even when no migration takes place. Most of these works are for mobile agents, and migrations are to be invoked by the programs. Migration points are also restricted to certain locations where migration status is being polled. In this paper, we propose a Java byte code transformation technique for realizing task migration without imposing significant overhead on normal execution. Asynchronous migration technique is used to allow migrations to take place virtually anywhere in the user codes, and the proposed Twin Method Hierarchy minimizes the overhead resulting from state-restoration codes in normal execution. We have implemented our approach in our middleware. The results show that our approach can allow lightweight computation migration at application level, achieve considerable speedups and utilize the cloud resources from mobile devices.

/pdf/lightweight-application-level-task-migration-for-mobile-3c1i5jpjn4.pdf

Lightweight Application-Level Task Migration for Mobile Cloud Computing

http://gac.des.udc.es/~juan/papers/comcom.pdf

Java Fast Sockets: Enabling high-speed Java communications on high performance clusters

Cloud computing is all the rage these days; its confluence with mobile computing would bring an even more pervasive influence. Clouds per se are elastic computing infrastructure where mobile applications can offload or draw tasks in an on-demand push-pull manner. Lightweight and portable task migration support enabling better resource utilization and data access locality is the key for success of mobile cloud computing. Existing task migration mechanisms are however too coarse-grained and costly, offsetting the benefits from migration and hampering flexible task partitioning among the mobile and cloud resources. We propose a new computation migration technique called stack-on-demand (SOD) that exports partial execution states of a stack machine to achieve agile mobility, easing into small-capacity devices and flexible distributed execution in a multi-domain workflow style. Our design also couples SOD with a novel object faulting technique for efficient access to remote objects. We implement the SOD concept into a middleware system for transparent execution migration of Java programs. It is shown that SOD migration cost is pretty low, comparing to several existing migration mechanisms. We also conduct experiments with an iPhone handset to demonstrate the elasticity of SOD by which server-side heavyweight processes can run adaptively on the cell phone.

/pdf/a-stack-on-demand-execution-model-for-elastic-computing-1i8tfaomfe.pdf

A Stack-on-Demand Execution Model for Elastic Computing

This paper presents JavaSplit, a portable runtime for distributed execution of multithreaded Java programs. Java-Split transparently distributes threads and objects of an application among the participating nodes. Thus, it gains augmented computational power and increased memory capacity without modifying the Java multithreaded programming conventions. Java-Split works by rewriting the bytecodes of a given parallel application, transforming it into a distributed application that incorporates all the runtime logic. Each runtime node carries out its part of the resulting distributed computation using nothing but its local standard (unmodified) Java virtual machine (JVM). This is unlike previous Java-based distributed runtime systems, which use a specialized JVM or utilize unconventional programming constructs. Since Java-Split is orthogonal to the implementation of a local JVM, it achieves portability across any existing platform and allows each node to locally optimize the performance of its JVM, e.g., via a just-in-time compiler (JIT).

JavaSplit: a runtime for execution of monolithic Java programs on heterogenous collections of commodity workstations

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JavaSplit: a runtime for execution of monolithic Java programs on heterogenous collections of commodity workstations