Xiao Liu PhD Thesis.pdf - Faculty of Information and Communication ...

Genetic Algorithm
Input: Integrated Task-to-VM list L{ ( Ti , VM j ) | i = 1,2,.., n;
j = ; 1,2,.., m};
Tasks { T i , Cost i , Time i , DAG{
T i > T i+ ; 1}};
Virtual Machines { VM j,
Price j,
Speed.
j}.
Output: Optimised Scheduling Plan
//two dimensional encoding
1) Encoding(L);
// package based random generation of initial populaton
2) Create initial Population of fixed size;
// Optimising the overall makespan and cost
3) While (stopping condition is not met)
{
//selecting Parents from Population
4) Selection(Population);
5) Crossover( Parents)
→ Children;
//change resource peer of a random cell
6) Mutation(Children);
//validate with DAG, retain or discard
7) Validation(Children);
//replace children of the worst fitness with the best one
8) Replace( WorstChild,
BestChild);
//check with the QoS constraints, retain or discard
9) Check(Children);
}
// Selecting the BestSolution according to user preferences
10) BestSoluti on = Compare( SolutionSet,
UserPref );
// decoding and deploy
11) Decoding( BestSolution);
12) Deploy(L).
Figure 8.3 GA based Rescheduling Algorithm
8.3.3 Metaheuristic Algorithm 2: Ant Colony Optimisation
In recent years, ACO, a type of optimisation algorithm inspired by the foraging
behaviour of real ants in the wild, has been adopted to address large complex
scheduling problems and proved to be quite effective in many distributed and
dynamic resource environments, such as parallel processor systems and grid
workflow systems [12, 25].
The detailed introduction for each operation of ACO can be found in our work in
[94] and [62], and hence omitted here. As shown in Figure 8.4, the first searching
stage is to optimise the overall execution time and cost for the integrated Task-
Resource list through ACO (Line 1 to Line 10). The ACO algorithm starts from
initialisation of pheromone and all parameters (Line 1). In [26], two types of
pheromone, viz. dτ ij and cτ ij , are defined. Here, dτ ij denotes the desirability of
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