A Meta-Heuristic Load Balancer for Cloud Computing Systems

This paper introduces a strategy to allocate services on a cloud system without overloading the nodes and maintaining the system stability with minimum cost. We specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. A prototype meta-heuristic load balancer is demonstrated and experimental results are presented and discussed. We also propose a novel genetic algorithm, where population is seeded with the outputs of other meta-heuristic algorithms.

Interaction Rituals and Jumbled Emotions Among “Relative Strangers”: Simulated Patient Work on a Trainee Complementary Therapy Practitioner Program

Learning games such as role-play (which we refer to as “simulated interaction rituals”) are commonly used as social tools to develop trainee health practitioners. However, the effect of such rituals on individual and group participant emotions has not been carefully studied. Using a heuristic approach, we explore the experiences of complementary therapy practitioner trainees (and their trainers) participating in a personal development course. Ten trainees and two tutors were interviewed, observational notes taken, and a secondary qualitative analysis undertaken. Participants and tutors described a medley of disparate emotional and moral responses to group rituals, conceptualized in this article as “jumbled emotions.” Such emotions required disentangling, and both trainees and staff perceived participating in unfamiliar rituals “with relative strangers” as challenging. Front of stage effects are frequently processed “backstage,” as rituals threaten social embarrassment and confusion. Concerns around emotional triggers, authenticity, and outcomes of rituals arise at the time, yet trainees can find ways to work through these issues in time.

Preconditioning 2D Integer Data for Fast Convex Hull Computations

In order to accelerate computing the convex hull on a set of n points, a heuristic procedure is often applied to reduce the number of points to a set of s points, s ≤ n, which also contains the same hull. We present an algorithm to precondition 2D data with integer coordinates bounded by a box of size p × q before building a 2D convex hull, with three distinct advantages. First, we prove that under the condition min(p, q) ≤ n the algorithm executes in time within O(n); second, no explicit sorting of data is required; and third, the reduced set of s points forms a simple polygonal chain and thus can be directly pipelined into an O(n) time convex hull algorithm. This paper empirically evaluates and quantifies the speed up gained by preconditioning a set of points by a method based on the proposed algorithm before using common convex hull algorithms to build the final hull. A speedup factor of at least four is consistently found from experiments on various datasets when the condition min(p, q) ≤ n holds; the smaller the ratio min(p, q)/n is in the dataset, the greater the speedup factor achieved.