14,293 research outputs found
3E: Energy-Efficient Elastic Scheduling for Independent Tasks in Heterogeneous Computing Systems
Reducing energy consumption is a major design constraint for modern heterogeneous computing systems to minimize electricity cost, improve system reliability and protect environment. Conventional energy-efficient scheduling strategies developed on these systems do not sufficiently exploit the system elasticity and adaptability for maximum energy savings, and do not simultaneously take account of user expected finish time. In this paper, we develop a novel scheduling strategy named energy-efficient elastic (3E) scheduling for aperiodic, independent and non-real-time tasks with user expected finish times on DVFS-enabled heterogeneous computing systems. The 3E strategy adjusts processors’ supply voltages and frequencies according to the system workload, and makes trade-offs between energy consumption and user expected finish times. Compared with other energy-efficient strategies, 3E significantly improves the scheduling quality and effectively enhances the system elasticity
Task Scheduling on the Cloud with Hard Constraints
Scheduling Bag-of-Tasks (BoT) applications on the cloud can be more
challenging than grid and cluster environ- ments. This is because a user may
have a budgetary constraint or a deadline for executing the BoT application in
order to keep the overall execution costs low. The research in this paper is
motivated to investigate task scheduling on the cloud, given two hard
constraints based on a user-defined budget and a deadline. A heuristic
algorithm is proposed and implemented to satisfy the hard constraints for
executing the BoT application in a cost effective manner. The proposed
algorithm is evaluated using four scenarios that are based on the trade-off
between performance and the cost of using different cloud resource types. The
experimental evaluation confirms the feasibility of the algorithm in satisfying
the constraints. The key observation is that multiple resource types can be a
better alternative to using a single type of resource.Comment: Visionary Track of the IEEE 11th World Congress on Services (IEEE
SERVICES 2015
Experimental Analysis of Algorithms for Coflow Scheduling
Modern data centers face new scheduling challenges in optimizing job-level
performance objectives, where a significant challenge is the scheduling of
highly parallel data flows with a common performance goal (e.g., the shuffle
operations in MapReduce applications). Chowdhury and Stoica introduced the
coflow abstraction to capture these parallel communication patterns, and
Chowdhury et al. proposed effective heuristics to schedule coflows efficiently.
In our previous paper, we considered the strongly NP-hard problem of minimizing
the total weighted completion time of coflows with release dates, and developed
the first polynomial-time scheduling algorithms with O(1)-approximation ratios.
In this paper, we carry out a comprehensive experimental analysis on a
Facebook trace and extensive simulated instances to evaluate the practical
performance of several algorithms for coflow scheduling, including the
approximation algorithms developed in our previous paper. Our experiments
suggest that simple algorithms provide effective approximations of the optimal,
and that the performance of our approximation algorithms is relatively robust,
near optimal, and always among the best compared with the other algorithms, in
both the offline and online settings.Comment: 29 pages, 8 figures, 11 table
Porting Decision Tree Algorithms to Multicore using FastFlow
The whole computer hardware industry embraced multicores. For these machines,
the extreme optimisation of sequential algorithms is no longer sufficient to
squeeze the real machine power, which can be only exploited via thread-level
parallelism. Decision tree algorithms exhibit natural concurrency that makes
them suitable to be parallelised. This paper presents an approach for
easy-yet-efficient porting of an implementation of the C4.5 algorithm on
multicores. The parallel porting requires minimal changes to the original
sequential code, and it is able to exploit up to 7X speedup on an Intel
dual-quad core machine.Comment: 18 pages + cove
Power Strip Packing of Malleable Demands in Smart Grid
We consider a problem of supplying electricity to a set of
customers in a smart-grid framework. Each customer requires a certain amount of
electrical energy which has to be supplied during the time interval . We
assume that each demand has to be supplied without interruption, with possible
duration between and , which are given system parameters (). At each moment of time, the power of the grid is the sum of all the
consumption rates for the demands being supplied at that moment. Our goal is to
find an assignment that minimizes the {\it power peak} - maximal power over
- while satisfying all the demands. To do this first we find the lower
bound of optimal power peak. We show that the problem depends on whether or not
the pair belongs to a "good" region . If it does - then
an optimal assignment almost perfectly "fills" the rectangle with being the sum of all the energy demands - thus
achieving an optimal power peak . Conversely, if do not belong to
, we identify the lower bound on the optimal value of
power peak and introduce a simple linear time algorithm that almost perfectly
arranges all the demands in a rectangle
and show that it is asymptotically optimal
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