Mobile healthcare (m-Healthcare) systems are regarded as a solution to address skyrocketing healthcare costs without reducing the quality of patient care. It is our aim to build an m-Healthcare platform based on the service-oriented computing paradigm. We are developing a Service-oriented Architecture (SOA) for m-Healthcare services platform, called SOAMOH that shall also support interfacing with the HL7 standard and facilitate the provisioning of healthcare to people anywhere, anytime using mobile devices that are connected through wireless communication technologies.

In this paper, we evaluate and compare the performance of several spanning tree routing strategies for divisible load scheduling on arbitrary graphs and derive recommendations as to which routing strategy provides a better trade-off between complexity and time performance. We consider a network comprising heterogeneous processors interconnected by heterogeneous links in an arbitrary manner. We evaluate the performance over a wide range of arbitrary dense graphs with varying connectivity and processor densities and study the effect of network scalability. In addition, we introduce a novel spanning tree routing strategy, which is referred to as minimum equivalent network spanning tree (EST), and analyze its performance. We apply the resource-aware optimal load distribution with optimal sequencing (RAOLD-OS) scheduling algorithm presented in the literature for obtaining an optimal solution. This study attempts to pool all known and applicable divisible load scheduling algorithms for arbitrary networks and presents a collective and comparative view of their performance.

In this paper, we present two heuristic energy-aware scheduling algorithms (EGMS and EGMSIV) for scheduling task precedence graphs in an embedded multiprocessor system having processing elements with dynamic voltage scaling capabilities. Unlike most energy-aware scheduling algorithms that consider task ordering and voltage scaling separately from task mapping, our algorithms consider them in an integrated way. EGMS uses the concept of energy gradient to select tasks to be mapped onto new processors and voltage levels. EGM-SIV extends EGMS by introducing intra-task voltage scaling using a Linear Programming (LP) formulation to further reduce the energy consumption. Through rigorous simulations, we compare the performance of our proposed algorithms with a few approaches presented in the literature. The results demonstrate that our algorithms are capable of obtaining energy-efficient schedules using less optimization time. On the average, our algorithms produce schedules which consume 10% less energy with more than 47% reduction in optimization time when compared to a few approaches presented in the literature. In particular, our algorithms perform better in generating energy-efficient schedules for larger task graphs. Our results show a reduction of up to 57% in energy consumption for larger task graphs compared to other approaches.

In this paper, we propose novel low-energy static and dynamic scheduling algorithms with low computational complexities, for heterogeneous multiprocessor real-time embedded systems. We consider task graphs with deadlines and precedence relationships to satisfy. We propose a novel scheme, referred to as “critical-path information track-and update”, based on critical-path analysis to distribute the slack-time over tasks such that energy consumption is minimized, while guaranteeing the precedence and timing constraints. Our dynamic scheduling algorithm applies the static scheduling algorithm during runtime based on the updated average-case execution demands of tasks. Our simulation results show that the proposed static scheduling algorithm consumes only 2% of the computational time with no degradation in energy savings, whereas the dynamic scheduling algorithm delivers up to 25% more energy savings while reducing the computational time overhead by more than 90%, when compared with recent heterogeneous multiprocessor scheduling algorithms.

In this paper, we propose novel low-energy scheduling algorithms with low computational complexities for the heterogeneous body area network (BAN) systems, considering task graphs with deadlines (timing constraints) and precedence relationships to satisfy. Our proposed novel scheme, referred to as "critical-path information track-and-update", analyses the critical-paths, identifies the slack and distributes it over tasks such that the overall energy consumption is minimised. Our dynamic scheduling algorithm utilises the results from the static scheduling algorithm and attempts to aggressively reduce the energy consumption. Simulations for the task graph for a typical BAN application show that our static and dynamic scheduling algorithms deliver 25% and 15% more energy savings respectively compared to typical slack reclamation based scheduling algorithms.

In this paper, we propose distributed algorithms referred to as resource-aware dynamic incremental scheduling (RADIS) strategies. Our strategies are specifically designed to handle large volumes of computationally intensive arbitrarily divisible loads submitted for processing at cluster/grid systems involving multiple sources and sinks (processing nodes). We consider a real-life scenario, wherein the buffer space (memory) available at the sinks (required for holding and processing the loads) varies over time, and the loads have deadlines and propose efficient "pull-based" scheduling strategies with an admission control policy that ensures that the admitted loads are processed, satisfying their deadline requirements. The design of our proposed strategies adopts the divisible load paradigm, referred to as the divisible load theory (DLT), which is shown to be efficient in handling large volume loads. We demonstrate detailed workings of the proposed algorithms via a simulation study by using real-life parameters obtained from a major physics experiment.