Main contributions

7.1.1 Desirability-aware load-balancing algorithm

Chapter 4 presented a new desirability-aware load balancing algorithm for heterogeneous computational grids.

We gave two criteria for site desirability: processing power and transfer delay. For each site si in a grid, our algorithm uses the desirability of other sites to si to form k

number of partners and p number of neighbours for the site si. Partners are sites with

comparable or greater processing power, and neighbours are nearby sites with low transfer delays. We have designed an approach for constructing the partner sites for each site when a site joins the grid. We determined an approach to enable the set of partners for a site to be updated dynamically at runtime based on feedback information, and a relatively simple approach to form neighbouring sites for each grid site.

Rather than using the conventional periodic or polling approaches, state information exchange between a site and its partners or neighbours is performed via Mutual Information Feedback (MIF) to reduce communication overheads.

The algorithm comprises two specific policies for load distribution: Instantaneous Distribution Policy (IDP) and Load Adjustment Policy respectively (LAP). When a new job arrives at a site, it either remains at that site or is immediately allocated by IDP to it or to one of its partner sites. Due to the likely fluctuating behaviour of grid resources, continuous load adjustment is employed among neighbour sites under the guidance of LAPto better exploit the grid environment.

Extensive simulation studies were conducted to analyse the performance of our load-balancing algorithm. The algorithm was compared to the Nearest Neighbour load- balancing algorithm. The results show that our algorithm performs better than the Nearest Neighbor algorithm, and reduces the average job response time over a wide range of system parameters.

Our algorithm (which considers the heterogeneity of sites) makes more powerful sites carry higher loads, because jobs executed at fast sites are more likely to execute at high speed. From the system perspective, our load-balancing scheme – which takes into account the different network transfer delay between sites – enables quick responses to load imbalances. In other words, the desirability-aware approach is “greedy” in the sense that it tries, at each step, to make jobs assignments at lightly loaded sites.

7.1.2 Performance-driven desirability-aware load-balancing algorithm

Chapter 5 presented a novel performance-driven load-balancing algorithm for heterogeneous computational grids while considering the site desirability.

The accumulated job execution time is defined as load index of a site. We included the dynamic communication cost in the cost calculation for job execution at a remote site, and how to determine the performance benefit that a job can gain for execution at a remote site. The state information exchange is done via MIF.

The algorithm uses site desirability to guide load assignments (Chapter 4), and integrates three dynamic approaches to make load distribution and redistribution driven by the performance benefit that jobs can gain: IDP, LAP, and Augmented Load Adjustment Policy (ALAP).

• IDP. A new job arriving at a site is immediately allocated to that site or one of its partner sites, giving the job maximum performance benefit.

• LAP. The load adjustment policy aims to continuously reduce load difference among a site and its neighbours by transferring the job that benefits most in the global job queue of that site.

• ALAP. LAP causes a bottleneck in less powerful sites and their overlapping neighbourhoods. The load adjustment can be further accomplished by transferring to an augmented neighbour the job that benefits most from being in the global job queue. How to find the augmented neighbour and how to trigger the ALAP were described.

The performance-driven approach is “greedy” in the sense that it tries, at each step, to make job assignments at the site that can provide most performance benefit. We extensively evaluated the algorithm using simulations. Those results showed that our algorithm outperforms the Performance-driven Neighbours-based algorithm, while having a closer performance to the unrealistic algorithm, Central.

7.1.3 Performance-driven Region-based load-balancing algorithm

Chapter 6 presented a Performance-driven Region-based load-balancing algorithm for heterogeneous computational grids by applying clustering approach. The algorithm is partially based on research that was presented in Chapter 5.

The grid sites are clustered into regions around a set of known broker sites in terms of network transfer delay; the regional brokers are organised in a fully decentralised fashion. We developed a decentralised load-balancing mechanism for the intra-region and inter-region load balancing directly in the sites. For each regional grid, our algorithm integrates static IDP and dynamic LAP to make load distribution and redistribution driven by the performance benefit that jobs can gain. The LAP also considers load redistribution across regional grids. The intra-region communication is minimised by MIF. To control inter-region communication, the random polling of a remote regional broker site is performed by each regional broker site at a set time interval.

We used simulations to extensively evaluate the algorithm, and showed that it performs better than Minimum Completion Time algorithm (MCT).

7.1.4 Discussion

Optimising workload allocation for heterogeneous grid systems is not an easy task. The assignment of jobs to processing sites is done in such a way as to minimise the average response time of jobs while minimising the overhead from communication delay. Owing to the dynamic nature of the grid computing environment, designing an ideal load-balancing algorithm on it remains a challenge. We hope our algorithms can serve as examples for continuing work on research into decentralised load-balancing solutions.