New Results on Online Resource Minimization

We consider the online resource minimization problem in which jobs with hard deadlines arrive online over time at their release dates. The task is to determine a feasible schedule on a minimum number of machines. We rigorously study this problem and derive various algorithms with small constant competitive ratios for interesting restricted problem variants. As the most important special case, we consider scheduling jobs with agreeable deadlines. We provide the first constant ratio competitive algorithm for the non-preemptive setting, which is of particular interest with regard to the known strong lower bound of n for the general problem. For the preemptive setting, we show that the natural algorithm LLF achieves a constant ratio for agreeable jobs, while for general jobs it has a lower bound of Omega(n^(1/3)). We also give an O(log n)-competitive algorithm for the general preemptive problem, which improves upon the known O(p_max/p_min)-competitive algorithm. Our algorithm maintains a dynamic partition of the job set into loose and tight jobs and schedules each (temporal) subset individually on separate sets of machines. The key is a characterization of how the decrease in the relative laxity of jobs influences the optimum number of machines. To achieve this we derive a compact expression of the optimum value, which might be of independent interest. We complement the general algorithmic result by showing lower bounds that rule out that other known algorithms may yield a similar performance guarantee

Scheduling independent stochastic tasks under deadline and budget constraints

International audienceThis paper discusses scheduling strategies for the problem of maximizing the expected number of tasks that can be executed on a cloud platform within a given budget and under a deadline constraint. The execution times of tasks follow IID probability laws. The main questions are how many processors to enroll and whether and when to interrupt tasks that have been executing for some time. We provide complexity results and an asymptotically optimal strategy for the problem instance with discrete probability distributions and without deadline. We extend the latter strategy for the general case with continuous distributions and a deadline and we design an efficient heuristic which is shown to outperform standard approaches when running simulations for a variety of useful distribution laws

Preemption of State Spam Laws by the Federal Can-Spam Act

Unsolicited bulk commercial email is an increasing problem, and though many states have passed laws aimed at curbing its use and abuse, for several years the federal government took no action. In 2003 that changed when Congress passed the CAN-SPAM Act. Though the law contains many different restrictions on spam messages, including some restriction of nearly every type that states had adopted, the Act was widely criticized as weak. Many of the CAN-SPAM Act\u27s provisions are weaker than corresponding provisions of state law, and the Act preempts most state spam laws that would go farther, including two state laws that would have banned all spam. Despite these weaknesses, this Comment argues that when properly interpreted the CAN-SPAM Act leaves key state law provisions in force, and accordingly is stronger than many spam opponents first thought. First, the law explicitly preserves state laws to the extent that they prohibit falsity or deception in any portion of a commercial electronic mail message or information attached thereto. Though Congress was primarily concerned with saving state consumer protection laws, this language can be applied much more broadly. Second, the law is silent on the question of state law enforcement methods. State enforcement can be, and frequently is, substantially stronger than federal enforcement, which is largely limited to actions by the federal government, internet service providers, and state agencies. The Comment concludes by arguing that this narrow interpretation of its preemption clause is most consistent with the CAN-SPAM Act\u27s twin policy goals. By limiting the substantive provisions states may adopt, the Act prevents states from enacting inconsistent laws and enforces a uniform national spam policy. At the same time, narrowly interpreting the preemption clause permits states to experiment within the limits of that policy, in hopes of finding the most effective set of spam regulations