  As computer clusters are found to be highly effective for handling massive
datasets, the design of efficient parallel algorithms for such a computing
model is of great interest. We consider ({\alpha}, k)-minimal algorithms for
such a purpose, where {\alpha} is the number of rounds in the algorithm, and k
is a bound on the deviation from perfect workload balance. We focus on new
({\alpha}, k)-minimal algorithms for sorting and skew equijoin operations for
computer clusters. To the best of our knowledge the proposed sorting and skew
join algorithms achieve the best workload balancing guarantee when compared to
previous works. Our empirical study shows that they are close to optimal in
workload balancing. In particular, our proposed sorting algorithm is around 25%
more efficient than the state-of-the-art Terasort algorithm and achieves
significantly more even workload distribution by over 50%.
