Abstract Autoscaling enables container cluster orchestrators to automatically adjust computational resources, such as containers and Virtual Machines (VMs), to handle fluctuating workloads effectively. This adaptation can involve modifying the amount of resources (horizontal scaling) or adjusting their computational capacity (vertical scaling). The motivation for our work stems from the limitations of previous autoscaling approaches, which are either partial (scaling containers or VMs, but not both) or excessively complex to be used in real systems. This complexity arises from their use of models with a large number of variables and the addressing of two simultaneous challenges: achieving the optimal deployment for a single scheduling window and managing the transition between successive scheduling windows. We propose an Integer Linear Programming (ILP) model to address the challenge of autoscaling containers and VMs jointly, both horizontally and vertically, to minimize deployment costs. This model is designed to be used with predictive autoscalers and be solved in a reasonable time, even for large clusters. To this end, improvements and reasonable simplifications with respect to previous models have been carried out to drastically reduce the size of the resource allocation problem. Furthermore, the proposed model provides an enhanced representation of system performance in comparison to previous approaches. A tool called Conlloovia has been developed to implement this model. To evaluate its performance, we have conducted a comprehensive assessment, comparing it with two heuristic allocators with different problem sizes. Our findings indicate that Conlloovia consistently demonstrates lower deployment costs in a significant number of cases. Conlloovia has also been evaluated with a real application, using synthetic and real workload traces, as well as different scheduling windows, with deployment costs approximately 20% lower than heuristic allocators.

For a long time, the common billing time slot used by cloud providers was the hour, but recently they have changed it to one second with a minimum charge of one minute. In this paper, the impact of this change on virtual machine allocation strategies is analyzed. With the minimum one-minute charge, the state-of-the-art allocation strategies are no longer optimal. This paper proposes a new analytic model to obtain the optimal allocation that minimizes the total cost considering this new billing scheme. However, the optimization problem using this model is unmanageable due to its computational complexity. Therefore, other sub-optimal allocations strategies are proposed. The performance of these strategies (in terms of total allocation cost and computational effort) is analyzed in order to assess the influence of the time slot length. Allocation time slots of one hour, one minute and one second are compared. The experimental work is carried out using synthetic workloads based on two real public traces. The study concludes that the time slot of one minute offers the best trade-off between allocation cost and computational effort. The experimentation shows that the proposed strategy can save up to 14 percent over using the time slot of one hour.

Nowadays, enterprises providing services through Internet often require online services supplied by other enterprises. This entails the cooperation of enterprise servers using Web services technology. The service exchange between enterprises must be carried out with a determined level of quality, which is usually established in a service level agreement (SLA). However, the fulfilment of SLAs is not an easy task and requires equipping the servers with special control mechanisms which control the quality of the services supplied. The first contribution of this research work is the analysis and definition of the main requirements that these control mechanisms must fulfil. The second contribution is the design of a control mechanism which fulfils these requirements and overcomes numerous deficiencies posed by previous mechanisms. The designed mechanism provides differentiation between distinct categories of service consumers as well as protection against server overloads. Furthermore, it scales in a cluster and does not require any modification to the system software of the host server, or to its application logic.

This paper presents the study carried out on the data traffic collected on the network of a cable operator based on hybrid fiber-coax technology, and the subsequent simulation model developed to predict the bandwidth requirements of the network channels. The paper starts with the analysis of the traffic measurements, taken over two periods of time in one year, on all the network's channels. This analysis identifies the main characteristics of the traffic, as well as some relationships between network parameters and their persistence over time. The paper proceeds to present the development of a simulation model, which represents the cable network. This model is built from the results of the traffic analysis and network parameters. The major challenge of this model is to predict the traffic on each channel of the cable network, related to parameters of the network configuration, such as the number of assigned subscribers to the channel and the time of day. To reach this goal, the simulation model has been developed with a modular structure, which gives it flexibility to adapt to changes in the network. The process followed involves the establishment of a traffic model, a system model, and finally, the validation of the results.

Presents the utilization bound for earliest deadline first (EDF) scheduling on homogeneous multiprocessor systems with partitioning strategies. Assuming that tasks are pre-emptively scheduled on each processor according to the EDF algorithm, and allocated according to the first-fit (FF) heuristic, we prove that the worst-case achievable utilization is 0.5(n+1), where n is the number of processors. This bound is valid for arbitrary utilization factors. Moreover, if all the tasks have utilization factors under a value /spl alpha/, the previous bound is raised, and the new utilization bound considering /spl alpha/ is calculated. In addition, we prove that no uniprocessor scheduling algorithm/allocation algorithm pair can provide a higher worst-case achievable utilization than that of EDF-FF. Finally, simulation provides the average-case achievable utilization for EDF-FF.