Abstract: Cloud Computing refers to applications delivered as
services over the internet, and the datacenters that provide those
services with hardware and systems software. These were earlier
referred to as Software as a Service (SaaS). Scheduling is justified by
job components (called tasks), lack of information. In fact, in a large
fraction of jobs from machine learning, bio-computing, and image
processing domains, it is possible to estimate the maximum time
required for a task in the job. This study focuses on Trust based
scheduling to improve cloud security by modifying Heterogeneous
Earliest Finish Time (HEFT) algorithm. It also proposes TR-HEFT
(Trust Reputation HEFT) which is then compared to Dynamic Load
Scheduling.
Abstract: Job Scheduling plays an important role for efficient
utilization of grid resources available across different domains and
geographical zones. Scheduling of jobs is challenging and NPcomplete.
Evolutionary / Swarm Intelligence algorithms have been
extensively used to address the NP problem in grid scheduling.
Artificial Bee Colony (ABC) has been proposed for optimization
problems based on foraging behaviour of bees. This work proposes a
modified ABC algorithm, Cluster Heterogeneous Earliest First Min-
Min Artificial Bee Colony (CHMM-ABC), to optimally schedule
jobs for the available resources. The proposed model utilizes a novel
Heterogeneous Earliest Finish Time (HEFT) Heuristic Algorithm
along with Min-Min algorithm to identify the initial food source.
Simulation results show the performance improvement of the
proposed algorithm over other swarm intelligence techniques.